From 00466512ea6532bee5357b0b46e0bb831bc9f63c Mon Sep 17 00:00:00 2001 From: "michele.debonis" Date: Tue, 22 Nov 2022 17:48:34 +0100 Subject: [PATCH] implementation of the new software configuration --- .../java/eu/dnetlib/pace/DedupLocalTest.java | 18 +- .../eu/dnetlib/pace/config/sw.tree.conf.json | 150 ++ .../pace/examples/software.dump.2000.json | 2000 +++++++++++++++++ .../pace/examples/software.to.fix.json | 3 + .../pace/examples/softwares.dump.2000.json | 2000 ----------------- .../clustering/ClusteringFunctionTest.java | 4 + 6 files changed, 2164 insertions(+), 2011 deletions(-) create mode 100644 dnet-dedup-test/src/test/resources/eu/dnetlib/pace/config/sw.tree.conf.json create mode 100644 dnet-dedup-test/src/test/resources/eu/dnetlib/pace/examples/software.dump.2000.json create mode 100644 dnet-dedup-test/src/test/resources/eu/dnetlib/pace/examples/software.to.fix.json delete mode 100644 dnet-dedup-test/src/test/resources/eu/dnetlib/pace/examples/softwares.dump.2000.json diff --git a/dnet-dedup-test/src/test/java/eu/dnetlib/pace/DedupLocalTest.java b/dnet-dedup-test/src/test/java/eu/dnetlib/pace/DedupLocalTest.java index a380b7f..92c1070 100644 --- a/dnet-dedup-test/src/test/java/eu/dnetlib/pace/DedupLocalTest.java +++ b/dnet-dedup-test/src/test/java/eu/dnetlib/pace/DedupLocalTest.java @@ -1,7 +1,6 @@ package eu.dnetlib.pace; import eu.dnetlib.Deduper; -import eu.dnetlib.graph.JavaGraphProcessor; import eu.dnetlib.jobs.SparkCreateDedupEntity; import eu.dnetlib.jobs.SparkCreateMergeRels; import eu.dnetlib.jobs.SparkCreateSimRels; @@ -12,7 +11,6 @@ import eu.dnetlib.pace.util.MapDocumentUtil; import eu.dnetlib.pace.utils.Utility; import eu.dnetlib.support.ArgumentApplicationParser; import eu.dnetlib.support.Block; -import eu.dnetlib.support.ConnectedComponent; import eu.dnetlib.support.Relation; import org.apache.commons.io.FileUtils; import org.apache.hadoop.conf.Configuration; @@ -23,8 +21,6 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.ForeachFunction; import org.apache.spark.api.java.function.PairFunction; -import org.apache.spark.graphx.Edge; -import org.apache.spark.rdd.RDD; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Encoders; import org.apache.spark.sql.SparkSession; @@ -183,10 +179,10 @@ public class DedupLocalTest extends DedupTestUtils { //custom parameters for this test DedupConfig dedupConfig = DedupConfig.load(readFileFromHDFS( - Paths.get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/config/ds.tree.conf.json").toURI()).toFile().getAbsolutePath() + Paths.get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/config/sw.tree.conf.json").toURI()).toFile().getAbsolutePath() )); - String inputPath = Paths.get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/examples/publications.to.fix.json").toURI()).toFile().getAbsolutePath(); + String inputPath = Paths.get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/examples/software.dump.2000.json").toURI()).toFile().getAbsolutePath(); String simRelsPath = workingPath + "/simrels"; String mergeRelsPath = workingPath + "/mergerels"; @@ -242,11 +238,11 @@ public class DedupLocalTest extends DedupTestUtils { @Test //test the match between two JSON @Disabled public void matchTest() throws Exception { - String json1 = "{\"context\": [], \"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"sysimport:crosswalk:repository\", \"classname\": \"sysimport:crosswalk:repository\", \"schemename\": \"dnet:provenanceActions\", \"schemeid\": \"dnet:provenanceActions\"}, \"inferred\": true, \"inferenceprovenance\": \"dedup-similarity-result-levenstein\", \"invisible\": false, \"trust\": \"0.9\"}, \"resourcetype\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"pid\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"pmc\", \"classname\": \"pmc\", \"schemename\": \"dnet:pid_types\", \"schemeid\": \"dnet:pid_types\"}, \"value\": \"PMC1688333\"}], \"contributor\": [], \"resulttype\": {\"classid\": \"publication\", \"classname\": \"publication\", \"schemename\": \"dnet:result_typologies\", \"schemeid\": \"dnet:result_typologies\"}, \"relevantdate\": [], \"collectedfrom\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"PubMed Central\", \"key\": \"10|opendoar____::eda80a3d5b344bc40f3bc04f65b7a357\"}], \"id\": \"50|od_______267::12220bb41c4f62ee8ae2ec051b22444d\", \"subject\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"keyword\", \"classname\": \"keyword\", \"schemename\": \"dnet:subject_classification_typologies\", \"schemeid\": \"dnet:subject_classification_typologies\"}, \"value\": \"Correspondence\"}], \"embargoenddate\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"lastupdatetimestamp\": 0, \"author\": [{\"surname\": \"Smith\", \"name\": \"S. L. H.\", \"pid\": [], \"rank\": 1, \"affiliation\": [], \"fullname\": \"Smith, S L H\"}], \"instance\": [{\"refereed\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"hostedby\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"PubMed Central\", \"key\": \"10|opendoar____::eda80a3d5b344bc40f3bc04f65b7a357\"}, \"processingchargeamount\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"license\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"url\": [\"https://europepmc.org/articles/PMC1688333/\"], \"distributionlocation\": \"\", \"processingchargecurrency\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"1976-09-18\"}, \"collectedfrom\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"PubMed Central\", \"key\": \"10|opendoar____::eda80a3d5b344bc40f3bc04f65b7a357\"}, \"accessright\": {\"classid\": \"OPEN\", \"classname\": \"Open Access\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"instancetype\": {\"classid\": \"0038\", \"classname\": \"Other literature type\", \"schemename\": \"dnet:publication_resource\", \"schemeid\": \"dnet:publication_resource\"}}], \"dateofcollection\": \"2019-07-01T18:28:03Z\", \"fulltext\": [], \"dateoftransformation\": \"\", \"description\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}], \"format\": [], \"journal\": {\"issnPrinted\": \"\", \"conferencedate\": \"\", \"conferenceplace\": \"\", \"name\": \"\", \"edition\": \"\", \"vol\": \"\", \"sp\": \"\", \"iss\": \"\", \"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"issnOnline\": \"\", \"ep\": \"\", \"issnLinking\": \"\"}, \"coverage\": [], \"publisher\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"language\": {\"classid\": \"eng\", \"classname\": \"English\", \"schemename\": \"dnet:languages\", \"schemeid\": \"dnet:languages\"}, \"bestaccessright\": {\"classid\": \"OPEN\", \"classname\": \"Open Access\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"country\": [], \"extraInfo\": [], \"originalId\": [\"oai:pubmedcentral.nih.gov:1688333\"], \"source\": [], \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"1976-09-18\"}, \"title\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"main title\", \"classname\": \"main title\", \"schemename\": \"dnet:dataCite_title\", \"schemeid\": \"dnet:dataCite_title\"}, \"value\": \"Admission of old people to hospital\"}]}"; - String json2 = "{\"context\": [], \"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"sysimport:actionset\", \"classname\": \"sysimport:actionset\", \"schemename\": \"dnet:provenanceActions\", \"schemeid\": \"dnet:provenanceActions\"}, \"inferred\": true, \"inferenceprovenance\": \"dedup-similarity-result-levenstein\", \"invisible\": false, \"trust\": \"0.9\"}, \"resourcetype\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"pid\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"doi\", \"classname\": \"doi\", \"schemename\": \"dnet:pid_types\", \"schemeid\": \"dnet:pid_types\"}, \"value\": \"10.1136/bmj.2.6039.812-d\"}], \"contributor\": [], \"resulttype\": {\"classid\": \"publication\", \"classname\": \"publication\", \"schemename\": \"dnet:result_typologies\", \"schemeid\": \"dnet:result_typologies\"}, \"relevantdate\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"published-print\", \"classname\": \"published-print\", \"schemename\": \"dnet:dataCite_date\", \"schemeid\": \"dnet:dataCite_date\"}, \"value\": \"1976-10-2\"}], \"collectedfrom\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Crossref\", \"key\": \"10|openaire____::081b82f96300b6a6e3d282bad31cb6e2\"}, {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Microsoft Academic Graph\", \"key\": \"10|openaire____::5f532a3fc4f1ea403f37070f59a7a53a\"}, {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"UnpayWall\", \"key\": \"10|openaire____::8ac8380272269217cb09a928c8caa993\"}], \"id\": \"50|doiboost____::faf91584ab611ecac7be042c95e82939\", \"subject\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"keyword\", \"classname\": \"keyword\", \"schemename\": \"dnet:subject\", \"schemeid\": \"dnet:subject\"}, \"value\": \"General Medicine\"}], \"embargoenddate\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"lastupdatetimestamp\": 0, \"author\": [{\"surname\": \"Campbell\", \"name\": \"A G M\", \"pid\": [{\"qualifier\": {\"classid\": \"MAG Identifier\", \"classname\": \"MAG Identifier\"}, \"value\": \"2424823041\"}], \"rank\": 1, \"affiliation\": [], \"fullname\": \"A G M Campbell\"}], \"instance\": [{\"refereed\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"hostedby\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Unknown Repository\", \"key\": \"10|openaire____::55045bd2a65019fd8e6741a755395c8c\"}, \"processingchargeamount\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"license\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"url\": [\"http://europepmc.org/articles/pmc1688323?pdf=render\"], \"distributionlocation\": \"\", \"processingchargecurrency\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"collectedfrom\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"UnpayWall\", \"key\": \"10|openaire____::8ac8380272269217cb09a928c8caa993\"}, \"accessright\": {\"classid\": \"OPEN\", \"classname\": \"Open Access\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"instancetype\": {\"classid\": \"0001\", \"classname\": \"Article\", \"schemename\": \"dnet:publication_resource\", \"schemeid\": \"dnet:publication_resource\"}}, {\"refereed\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"hostedby\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Unknown Repository\", \"key\": \"10|openaire____::55045bd2a65019fd8e6741a755395c8c\"}, \"processingchargeamount\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"license\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"url\": [\"https://syndication.highwire.org/content/doi/10.1136/bmj.2.6039.812-d\"], \"distributionlocation\": \"\", \"processingchargecurrency\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"collectedfrom\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Crossref\", \"key\": \"10|openaire____::081b82f96300b6a6e3d282bad31cb6e2\"}, \"accessright\": {\"classid\": \"UNKNOWN\", \"classname\": \"not available\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"instancetype\": {\"classid\": \"0001\", \"classname\": \"Article\", \"schemename\": \"dnet:publication_resource\", \"schemeid\": \"dnet:publication_resource\"}}, {\"refereed\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"hostedby\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Unknown Repository\", \"key\": \"10|openaire____::55045bd2a65019fd8e6741a755395c8c\"}, \"processingchargeamount\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"license\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"url\": [\"https://academic.microsoft.com/#/detail/2054538157\"], \"distributionlocation\": \"\", \"processingchargecurrency\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"collectedfrom\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Microsoft Academic Graph\", \"key\": \"10|openaire____::5f532a3fc4f1ea403f37070f59a7a53a\"}, \"accessright\": {\"classid\": \"UNKNOWN\", \"classname\": \"not available\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"instancetype\": {\"classid\": \"0001\", \"classname\": \"Article\", \"schemename\": \"dnet:publication_resource\", \"schemeid\": \"dnet:publication_resource\"}}, {\"refereed\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"hostedby\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Unknown Repository\", \"key\": \"10|openaire____::55045bd2a65019fd8e6741a755395c8c\"}, \"processingchargeamount\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"license\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"url\": [\"http://dx.doi.org/10.1136/bmj.2.6039.812-d\"], \"distributionlocation\": \"\", \"processingchargecurrency\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"collectedfrom\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"Crossref\", \"key\": \"10|openaire____::081b82f96300b6a6e3d282bad31cb6e2\"}, \"accessright\": {\"classid\": \"RESTRICTED\", \"classname\": \"Restricted\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"instancetype\": {\"classid\": \"0001\", \"classname\": \"Article\", \"schemename\": \"dnet:publication_resource\", \"schemeid\": \"dnet:publication_resource\"}}], \"dateofcollection\": \"2019-02-15\", \"fulltext\": [], \"dateoftransformation\": \"\", \"description\": [], \"format\": [], \"journal\": {\"issnPrinted\": \"0959-8138\", \"conferencedate\": \"\", \"conferenceplace\": \"\", \"name\": \"BMJ\", \"edition\": \"\", \"vol\": \"\", \"sp\": \"\", \"iss\": \"\", \"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"issnOnline\": \"1468-5833\", \"ep\": \"\", \"issnLinking\": \"\"}, \"coverage\": [], \"publisher\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"\"}, \"language\": {\"classid\": \"und\", \"classname\": \"Undetermined\", \"schemename\": \"dnet:languages\", \"schemeid\": \"dnet:languages\"}, \"bestaccessright\": {\"classid\": \"OPEN\", \"classname\": \"Open Access\", \"schemename\": \"dnet:access_modes\", \"schemeid\": \"dnet:access_modes\"}, \"country\": [], \"extraInfo\": [], \"originalId\": [\"10.1136/bmj.2.6039.812-d\"], \"source\": [], \"dateofacceptance\": {\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"value\": \"1976-10-2\"}, \"title\": [{\"dataInfo\": {\"deletedbyinference\": false, \"provenanceaction\": {\"classid\": \"\", \"classname\": \"\", \"schemename\": \"\", \"schemeid\": \"\"}, \"inferred\": false, \"inferenceprovenance\": \"\", \"invisible\": false, \"trust\": \"\"}, \"qualifier\": {\"classid\": \"main title\", \"classname\": \"main title\", \"schemename\": \"dnet:dataCite_title\", \"schemeid\": \"dnet:dataCite_title\"}, \"value\": \"Admission of old people to hospital\"}]}"; + String json1 = "{\"author\":[{\"affiliation\":[],\"fullname\":\"Hanayik, Taylor\",\"name\":\"Taylor\",\"pid\":[],\"rank\":1,\"surname\":\"Hanayik\"},{\"affiliation\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:actionset\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"University of South Carolina\"}],\"fullname\":\"Drake, Christopher\",\"name\":\"Christopher\",\"pid\":[],\"rank\":2,\"surname\":\"Drake\"},{\"affiliation\":[],\"fullname\":\"Rorden, Chris\",\"name\":\"Chris\",\"pid\":[],\"rank\":3,\"surname\":\"Rorden\"},{\"affiliation\":[],\"fullname\":\"Hardcastle, Nell\",\"name\":\"Nell\",\"pid\":[],\"rank\":4,\"surname\":\"Hardcastle\"},{\"affiliation\":[],\"fullname\":\"Androulakis, Anthony\",\"name\":\"Anthony\",\"pid\":[],\"rank\":5,\"surname\":\"Androulakis\"}],\"bestaccessright\":{\"classid\":\"OPEN\",\"classname\":\"Open Access\",\"schemeid\":\"dnet:access_modes\",\"schemename\":\"dnet:access_modes\"},\"collectedfrom\":[{\"key\":\"10|openaire____::9e3be59865b2c1c335d32dae2fe7b254\",\"value\":\"Datacite\"}],\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:actionset\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"dateofacceptance\":{\"value\":\"2022-03-02\"},\"dateofcollection\":\"2022-03-02T11:25:20+0000\",\"dateoftransformation\":\"2022-03-02T11:25:20+0000\",\"description\":[{\"value\":\"a WebGL2 based NIFTI volume viewer\"}],\"id\":\"50|doi_________::b596bfb411bbc62b902aedb11d0088d8\",\"instance\":[{\"accessright\":{\"classid\":\"OPEN\",\"classname\":\"Open Access\",\"schemeid\":\"dnet:access_modes\",\"schemename\":\"dnet:access_modes\"},\"collectedfrom\":{\"key\":\"10|openaire____::9e3be59865b2c1c335d32dae2fe7b254\",\"value\":\"Datacite\"},\"dateofacceptance\":{\"value\":\"2022-03-02\"},\"hostedby\":{\"key\":\"10|opendoar____::358aee4cc897452c00244351e4d91f69\",\"value\":\"ZENODO\"},\"instancetype\":{\"classid\":\"0029\",\"classname\":\"Software\",\"schemeid\":\"dnet:publication_resource\",\"schemename\":\"dnet:publication_resource\"},\"pid\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:actionset\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"qualifier\":{\"classid\":\"doi\",\"classname\":\"Digital Object Identifier\",\"schemeid\":\"dnet:pid_types\",\"schemename\":\"dnet:pid_types\"},\"value\":\"10.5281/zenodo.5786269\"}],\"refereed\":{\"classid\":\"0000\",\"classname\":\"Unknown\",\"schemeid\":\"dnet:review_levels\",\"schemename\":\"dnet:review_levels\"},\"url\":[\"https://dx.doi.org/10.5281/zenodo.5786269\"]}],\"language\":{\"classid\":\"und\",\"classname\":\"Undetermined\",\"schemeid\":\"dnet:languages\",\"schemename\":\"dnet:languages\"},\"originalId\":[\"10.5281/zenodo.5786269\"],\"pid\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:actionset\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"qualifier\":{\"classid\":\"doi\",\"classname\":\"Digital Object Identifier\",\"schemeid\":\"dnet:pid_types\",\"schemename\":\"dnet:pid_types\"},\"value\":\"10.5281/zenodo.5786269\"}],\"publisher\":{\"value\":\"Zenodo\"},\"relevantdate\":[{\"qualifier\":{\"classid\":\"issued\",\"classname\":\"issued\",\"schemeid\":\"dnet:dataCite_date\",\"schemename\":\"dnet:dataCite_date\"},\"value\":\"2022-03-02\"}],\"resourcetype\":{\"classid\":\"UNKNOWN\",\"classname\":\"Unknown\",\"schemeid\":\"dnet:dataCite_resource\",\"schemename\":\"dnet:dataCite_resource\"},\"resulttype\":{\"classid\":\"software\",\"classname\":\"software\",\"schemeid\":\"dnet:result_typologies\",\"schemename\":\"dnet:result_typologies\"},\"subject\":[],\"title\":[{\"qualifier\":{\"classid\":\"main title\",\"classname\":\"main title\",\"schemeid\":\"dnet:dataCite_title\",\"schemename\":\"dnet:dataCite_title\"},\"value\":\"niivue/niivue: 0.21.1\"}]}"; + String json2 = "{\"author\":[{\"affiliation\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"University of South Carolina\"}],\"fullname\":\"Chris Rorden\",\"name\":\"\",\"pid\":[],\"rank\":1,\"surname\":\"\"},{\"affiliation\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"University of Oxford\"}],\"fullname\":\"Taylor Hanayik\",\"name\":\"\",\"pid\":[],\"rank\":2,\"surname\":\"\"},{\"affiliation\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"University of South Carolina\"}],\"fullname\":\"Christopher Drake\",\"name\":\"\",\"pid\":[],\"rank\":3,\"surname\":\"\"},{\"affiliation\":[],\"fullname\":\"Nell Hardcastle\",\"name\":\"\",\"pid\":[],\"rank\":4,\"surname\":\"\"},{\"affiliation\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"University of South Carolina\"}],\"fullname\":\"Roger Newman-Norlund\",\"name\":\"\",\"pid\":[],\"rank\":5,\"surname\":\"\"}],\"bestaccessright\":{\"classid\":\"OPEN\",\"classname\":\"Open Access\",\"schemeid\":\"dnet:access_modes\",\"schemename\":\"dnet:access_modes\"},\"collectedfrom\":[{\"key\":\"10|opendoar____::358aee4cc897452c00244351e4d91f69\",\"value\":\"ZENODO\"}],\"context\":[],\"contributor\":[],\"country\":[],\"coverage\":[],\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"dateofacceptance\":{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"2021-12-16\"},\"dateofcollection\":\"2022-11-11T00:19:05+0000\",\"dateoftransformation\":\"2022-11-11T07:32:42.689Z\",\"description\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"

a WebGL2 based NIFTI volume viewer

\"}],\"documentationUrl\":[],\"eoscifguidelines\":[],\"externalReference\":[],\"extraInfo\":[],\"format\":[],\"fulltext\":[],\"id\":\"50|doi_________::59a7f397515febc2c7017fd2f866a777\",\"instance\":[{\"accessright\":{\"classid\":\"OPEN\",\"classname\":\"Open Access\",\"schemeid\":\"dnet:access_modes\",\"schemename\":\"dnet:access_modes\"},\"alternateIdentifier\":[],\"collectedfrom\":{\"key\":\"10|opendoar____::358aee4cc897452c00244351e4d91f69\",\"value\":\"ZENODO\"},\"dateofacceptance\":{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"2021-12-16\"},\"hostedby\":{\"key\":\"10|opendoar____::358aee4cc897452c00244351e4d91f69\",\"value\":\"ZENODO\"},\"instancetype\":{\"classid\":\"0029\",\"classname\":\"Software\",\"schemeid\":\"dnet:publication_resource\",\"schemename\":\"dnet:publication_resource\"},\"pid\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"qualifier\":{\"classid\":\"doi\",\"classname\":\"Digital Object Identifier\",\"schemeid\":\"dnet:pid_types\",\"schemename\":\"dnet:pid_types\"},\"value\":\"10.5281/zenodo.5786270\"}],\"refereed\":{\"classid\":\"0000\",\"classname\":\"UNKNOWN\",\"schemeid\":\"dnet:review_levels\",\"schemename\":\"dnet:review_levels\"},\"url\":[\"http://dx.doi.org/10.5281/zenodo.5786270\",\"https://doi.org/10.5281/zenodo.5786270\"]}],\"language\":{\"classid\":\"UNKNOWN\",\"classname\":\"UNKNOWN\",\"schemeid\":\"dnet:languages\",\"schemename\":\"dnet:languages\"},\"lastupdatetimestamp\":1668556279928,\"license\":[],\"originalId\":[\"oai:zenodo.org:5786270\",\"50|od______2659::c8a3ff31e2ff537ebac22cdc4d7c64b0\"],\"pid\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"qualifier\":{\"classid\":\"doi\",\"classname\":\"Digital Object Identifier\",\"schemeid\":\"dnet:pid_types\",\"schemename\":\"dnet:pid_types\"},\"value\":\"10.5281/zenodo.5786270\"}],\"programmingLanguage\":{\"classid\":\"\",\"classname\":\"\",\"schemeid\":\"\",\"schemename\":\"\"},\"publisher\":{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"value\":\"Zenodo\"},\"relevantdate\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"qualifier\":{\"classid\":\"issued\",\"classname\":\"issued\",\"schemeid\":\"dnet:dataCite_date\",\"schemename\":\"dnet:dataCite_date\"},\"value\":\"2021-12-16\"}],\"resourcetype\":{\"classid\":\"UNKNOWN\",\"classname\":\"UNKNOWN\",\"schemeid\":\"dnet:dataCite_resource\",\"schemename\":\"dnet:dataCite_resource\"},\"resulttype\":{\"classid\":\"software\",\"classname\":\"software\",\"schemeid\":\"dnet:result_typologies\",\"schemename\":\"dnet:result_typologies\"},\"source\":[],\"subject\":[],\"title\":[{\"dataInfo\":{\"deletedbyinference\":false,\"inferred\":false,\"invisible\":false,\"provenanceaction\":{\"classid\":\"sysimport:crosswalk:repository\",\"classname\":\"Harvested\",\"schemeid\":\"dnet:provenanceActions\",\"schemename\":\"dnet:provenanceActions\"},\"trust\":\"0.9\"},\"qualifier\":{\"classid\":\"main title\",\"classname\":\"main title\",\"schemeid\":\"dnet:dataCite_title\",\"schemename\":\"dnet:dataCite_title\"},\"value\":\"niivue/niivue: 0.13.0\"}]}"; DedupConfig config = DedupConfig.load(readFileFromHDFS(Paths - .get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/config/pub.new.tree.conf.json").toURI()) + .get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/config/sw.tree.conf.json").toURI()) .toFile() .getAbsolutePath())); @@ -263,12 +259,12 @@ public class DedupLocalTest extends DedupTestUtils { @Disabled public void dedupTest() throws Exception { final String entitiesPath = Paths - .get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/examples/openorgs.to.fix.json").toURI()) + .get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/examples/software.to.fix.json").toURI()) .toFile() .getAbsolutePath(); DedupConfig dedupConf = DedupConfig.load(readFileFromHDFS(Paths - .get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/config/organization.current.conf.json").toURI()) + .get(DedupLocalTest.class.getResource("/eu/dnetlib/pace/config/sw.tree.conf.json").toURI()) .toFile() .getAbsolutePath())); diff --git a/dnet-dedup-test/src/test/resources/eu/dnetlib/pace/config/sw.tree.conf.json b/dnet-dedup-test/src/test/resources/eu/dnetlib/pace/config/sw.tree.conf.json new file mode 100644 index 0000000..56bf537 --- /dev/null +++ b/dnet-dedup-test/src/test/resources/eu/dnetlib/pace/config/sw.tree.conf.json @@ -0,0 +1,150 @@ +{ + "wf" : { + "threshold" : "0.99", + "dedupRun" : "001", + "entityType" : "result", + "subEntityType" : "resulttype", + "subEntityValue" : "software", + "orderField" : "title", + "queueMaxSize" : "200", + "groupMaxSize" : "100", + "maxChildren" : "100", + "slidingWindowSize" : "50", + "rootBuilder" : [ "result", "resultProject_outcome_isProducedBy", "resultResult_publicationDataset_isRelatedTo", "resultResult_similarity_isAmongTopNSimilarDocuments", "resultResult_similarity_hasAmongTopNSimilarDocuments", "resultOrganization_affiliation_hasAuthorInstitution", "resultResult_part_hasPart", "resultResult_part_isPartOf", "resultResult_supplement_isSupplementTo", "resultResult_supplement_isSupplementedBy", "resultResult_version_isVersionOf" ], + "includeChildren" : "true" + }, + "pace" : { + "clustering" : [ + { "name" : "wordsStatsSuffixPrefixChain", "fields" : [ "title" ], "params" : { "mod" : "10" } }, + { "name" : "lowercase", "fields" : [ "doi", "altdoi" ], "params" : { "collapseOn:pid":"0"} }, + { "name" : "ngrams", "fields" : [ "title" ], "params" : {"ngramLen": 3, "max": 4, "maxPerToken":1, "minNgramLen":3}}, + { "name" : "urlclustering", "fields": [ "url" ], "params" : {}} + ], + "decisionTree": { + "start": { + "fields": [ + { + "field": "doi", + "comparator": "exactMatch", + "weight": 1, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 1, + "aggregation": "MAX", + "positive": "MATCH", + "negative": "titleCheck", + "undefined": "titleCheck", + "ignoreUndefined": "false" + }, + "titleCheck": { + "fields": [ + { + "field": "title", + "comparator": "levensteinTitleIgnoreVersion", + "weight": 1, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 0.95, + "aggregation": "AVG", + "positive": "pidCheck", + "negative": "NO_MATCH", + "undefined": "NO_MATCH", + "ignoreUndefined": "false" + }, + "pidCheck": { + "fields": [ + { + "field": "altdoi", + "comparator": "exactMatch", + "weight": 1, + "countIfUndefined": "false", + "params": {} + }, + { + "field": "doi", + "comparator": "exactMatch", + "weight": 1, + "countIfUndefined": "false", + "params": {"crossCompare": "altdoi"} + }, + { + "field": "url", + "comparator": "exactMatch", + "weight": 1, + "countIfUndefined": "false", + "params": {} + } + ], + "threshold": 1, + "aggregation": "OR", + "positive": "MATCH", + "negative": "authorsCheck", + "undefined": "authorsCheck", + "ignoreUndefined": "false" + }, + "authorsCheck": { + "fields": [ + { + "field": "authors", + "comparator": "authorsMatch", + "weight": 1.0, + "countIfUndefined": "false", + "params": { + "surname_th": 0.70, + "fullname_th": 0.70, + "size_th": 20, + "mode": "surname" + } + } + ], + "threshold": 1, + "aggregation": "AVG", + "positive": "MATCH", + "negative": "NO_MATCH", + "undefined": "MATCH", + "ignoreUndefined": "false" + } + }, + "model" : [ + { + "name" : "doi", + "type" : "String", + "path" : "$.instance.pid[?(@.qualifier.classid == 'doi')].value" + }, + { + "name" : "altdoi", + "type" : "String", + "path" : "$.instance.alternateIdentifier[?(@.qualifier.classid == 'doi')].value" + }, + { + "name" : "title", + "type" : "String", + "path" : "$.title[?(@.qualifier.classid == 'main title')].value", + "length" : 250, + "size" : 5 + }, + { + "name" : "url", + "type" : "String", + "path" : "$.instance.url" + }, + { + "name" : "resulttype", + "type" : "String", + "path" : "$.resulttype.classid" + }, + { + "name": "authors", + "type": "List", + "path": "$.author[*].fullname", + "size": 200 + } + ], + "blacklists" : {}, + "synonyms": {} + } +} \ No newline at end of file diff --git a/dnet-dedup-test/src/test/resources/eu/dnetlib/pace/examples/software.dump.2000.json b/dnet-dedup-test/src/test/resources/eu/dnetlib/pace/examples/software.dump.2000.json new file mode 100644 index 0000000..e658799 --- /dev/null +++ b/dnet-dedup-test/src/test/resources/eu/dnetlib/pace/examples/software.dump.2000.json @@ -0,0 +1,2000 @@ +{"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "sysimport:crosswalk:datasetarchive", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": "0.9"}, "resourcetype": {"classid": "Command-line tool", "classname": "Command-line tool", "schemename": "dnet:dataCite_resource", "schemeid": "dnet:dataCite_resource"}, "programmingLanguage": {"classid": "Python", "classname": "Python", "schemename": "", "schemeid": ""}, "pid": [], "contributor": [], "bestaccessright": {"classid": "UNKNOWN", "classname": "not available", "schemename": "dnet:access_modes", "schemeid": "dnet:access_modes"}, "relevantdate": [], "collectedfrom": [{"value": "bio.tools", "key": "10|rest________::b8e502674c3c3499d5374e9b2ea6d8d5"}], "id": "50|__bioTools__::5f22656cf5c9ee4d69657c3913dce8eb", "subject": [{"dataInfo": {"deletedbyinference": false, "provenanceaction": {"classid": "sysimport:crosswalk:datasetarchive", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "inferenceprovenance": "", "invisible": false, "trust": "0.9"}, "qualifier": {"classid": "keyword", "classname": "keyword", "schemename": "dnet:subject_classification_typologies", "schemeid": "dnet:subject_classification_typologies"}, "value": "Sequencing"}, 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Full Changelog: https://github.com/nlesc-nano/data-CAT/compare/0.7.1...0.7.2

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High-throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field-based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat lines planted in full plots and grid-spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unmanned aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from -0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from -0.40 to 0.06. A genome wide association analysis (GWAS) identified the same marker trait associations in both full plots and single plots, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early-generation germplasm. Efficient use of early-generation, affordable HTP data could improve overall genetic gain in plant breeding.

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This tag is released for HKO-7 dataset.

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The simulation setups are separated into polydisperse bubbly flows utilising the HZDR Baseline model set according to Liao et al. (Chem Eng Sci, 2019, Vol. 202, 55-69) and setups for a hybrid modelling approach (disperse and resolved interfaces) according to Meller et al. (Int J Numer Meth Fluids, 2021, Vol. 93, 748\u2013773). Cases using the HZDR Baseline model set baseline/1998_Liu Reference for experiment: Liu, 3rd Int Conf Multiph Flow (ICMF), Vol. 98, 8-12 Reference for case setup: Rzehak et al., Nucl Eng Des (accepted) Kriebitzsch and Rzehak, Fluids, 2016, Vol. 1, 29 baseline/2005_Lucas_et_al Reference for experiment: Lucas et al., Int J Multiph Flow, 2005, Vol. 31, 1304-1328 Reference for case setup: Lehnigk et al., AlChE J (submitted) baseline/2008_Shawkat Reference for experiment: Shawkat et al., Int J Multiph Flow, 2008, Vol. 34, 767-785 Reference for case setup: Kriebitzsch and Rzehak, Fluids, 2016, Vol. 1, 29 baseline/2009_Hosokawa Reference for experiment: Hosokawa and Tomiyama, Chem Eng Science, 2009, Vol. 64, 5308-5318 Reference for case setup: Rzehak et al., Nucl Eng Des (accepted) baseline/2013_Hosokawa_and_Tomiyama Reference for experiment: Hosokawa and Tomiyama, Int J Heat Fluid Flow, 2013, Vol. 40, 97-105 Reference for case setup: Kriebitzsch and Rzehak, Fluids, 2016, Vol. 1, 29 Liao et al., Comp Fluids, 2020, Vol. 202, 104496 baseline/2016_Kim_et_al Reference for experiment: Kim et al., Exp Fluids, 2016, Vol. 57, 1432-1114 Reference for case setup: Liao et al., Comp Fluids, 2020, Vol. 202, 104496 Cases using the hybrid modelling approach hybrid/wenka/2D-MP3-23 Reference for experiment: St\u00e4bler, Ph.D. thesis, 2007 Reference for case setup: Tekav\u010di\u010d et al., Nucl Eng Des (accepted) hybrid/risingBubbleHysingEtAl2009 References for case setup: Hysing et al., Int J Numer Meth Fluids, 2009, Vol. 60, 1259-1288 Meller et al., Int J Numer Meth Fluids, 2021, Vol. 93, 748\u2013773 Meller et al., Flow Turbul Combust (submitted) hybrid/risingBubbleBalcazarEtAl2015 Reference for experiment: Bhaga and Weber, J Fluid Mech, 1981, Vol. 105, 61-85 Reference for direct numerical simulation: Balc\u00e1zar et al., Int J Heat Fluid Flow, 2015, Vol. 56, 91-107 References for case setup: Meller et al., Int J Numer Meth Fluids, 2021, Vol. 93, 748\u2013773 hybrid/risingBubbleMellerEtAl2021 Reference for case setup: Meller et al., Flow Turbul Combust (submitted)"}, {"value": "This work was supported by the Helmholtz European Partnering Program in the project \"Crossing borders and scales (Crossing)\"."}, {"value": "{\"references\": [\"Meller, R., Schlegel, F., & Lucas, D. 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MATLAB implementation of RRT, RRT* and RRT*FN algorithms.

What is RRT, RRT* and RRT*FN

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Still somewhat raw code and all appropriate data for the re-analysis of data and figure generation for that reported in Bitter et al. (2020) Am Nat.

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This archive contains the Snakemake workflow used to produce the results presented in the Bioconda publication, along with all required input data. See https://github.com/bioconda/bioconda-paper for usage instructions.

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Code accompanying manuscript submission to The Cryosphere journal.

surfacespeed_ratios22.m computes surface speeds predicted by internal deformation and Weertman sliding mechanisms, comparing predictions from the two mechanisms to observed speeds.

surfacespeed_ratios23.m repeats the computation from surfacespeed_ratios22.m, but with more smoothing on several variables input to the internal deformation and Weertman sliding models. This code requires that the user already has a workspace open with the latitude and longitude data for a flowline.

surfacespeed_ratios24.m computes the misfit between predictions of each mechanism and observed data. This code requires that the user load the results from surfacespeed_ratios22.m or surfacepseed_ratios23.m into the workspace, choosing a particular flowline and a region within it where the user has already concluded (qualitatively) that one mechanism provides closer agreement with observed data than the other. This code is to verify qualitative conclusions made on the figure comparisons produced by surfacespeed_ratios23.m and surfacespeed_ratios22.m. 

The ERA-Interim reanalysis data used to estimate the ice temperature at depth are publicly available, and were downloaded from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim. Ice thickness, ice elevation, bedrock topography data and relevant Matlab functions were sourced from the Bedmap2 data set, available from Matlab File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/42353-bedmap2-toolbox-for-matlab. Ice velocity and grounding line data were sourced from MEaSUREs data set, available from Matlab File Exchange: https://www.mathworks.com/matlabcentral/fileexchange/47329-measures?s_tid=ta_fx_results.

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In our solver, we first compute the initial lower bound and then apply kernelization strategy using several reduction rules. Then we employ branch-and-reduce on the (hopefully small) kernel. Since the problem is closely related to the VC problem,
we introduce some modifications of well-known VC reduction rules and utilize them for our DFVS solver.

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Wright"}, {"surname": "", "name": "", "pid": [], "rank": 7, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "Ecole Polytechnique"}], "fullname": "Michel Deudon"}, {"surname": "", "name": "", "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "orcid_pending", "classname": "Open Researcher and Contributor ID", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "0000-0002-1471-646x"}], "rank": 8, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "Element AI"}], "fullname": "Alfredo Kalaitzis"}, {"surname": "", "name": "", "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "orcid_pending", "classname": "Open Researcher and Contributor ID", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "0000-0001-9854-8100"}], "rank": 9, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "University of Oxford"}], "fullname": "At\u0131l\u0131m G\u00fcne\u015f Baydin"}, {"surname": "", "name": "", "pid": [], "rank": 10, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "University of Oxford"}], "fullname": "Yarin Gal"}], "documentationUrl": [], "instance": [{"refereed": {"classid": "0000", "classname": "UNKNOWN", "schemename": "dnet:review_levels", "schemeid": "dnet:review_levels"}, "hostedby": {"value": "ZENODO", "key": "10|opendoar____::358aee4cc897452c00244351e4d91f69"}, "url": ["http://dx.doi.org/10.5281/zenodo.3750373", "https://doi.org/10.5281/zenodo.3750373"], "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "doi", "classname": "Digital Object Identifier", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "10.5281/zenodo.3750373"}], "alternateIdentifier": [], "dateofacceptance": {"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "2020-04-13"}, "collectedfrom": {"value": "ZENODO", "key": "10|opendoar____::358aee4cc897452c00244351e4d91f69"}, "accessright": {"classid": "OPEN", "classname": "Open Access", "schemename": "dnet:access_modes", "schemeid": "dnet:access_modes"}, "instancetype": {"classid": "0029", "classname": "Software", "schemename": "dnet:publication_resource", "schemeid": "dnet:publication_resource"}}], "dateofcollection": "2022-11-11T00:19:05+0000", "fulltext": [], "dateoftransformation": "2022-11-11T07:31:20.666Z", "description": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "

Converter software to calibrate and super-resolve solar magnetograms

Currently only working on magnetograms taken by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SoHO) or the Global Oscillation Network Group (GONG). Output magnetograms have the resolution and systematics of magnetograms taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO).

 

New Fits Keywords

The converter magnetogram contains the following added keywords:

'instrume': Old value is retained, but with the appended '-2HMI_HR' suffix.

'date-conv': UTC date of conversion.

'nn-model': Name of neural network architecture.

'loss': Loss parameters.

'converter_doi': DOI pointing to this converter's repository

 

Usage Arguments

Required

'--instrument': Input instrument. Currently only 'gong' and 'mdi' are valid choices.

'--data_path': Folder containing the files to be converted.

'--destination': Destination of the super-resolved magnetograms.

Optional

'--add_noise': Variance of the gaussian noise (in gauss) to be added to the output magnetogram. By default no noise is added. For noise similar to HMI use 4.7.

'--plot': Make a plot of the output magnetogram.

'--overwrite': Flag to overwrite files in output folder. Files are not overwritten by default.

'--use_patches': Run inference on magnetogram patches instead of the default full disk inference.

'--zero_outside': Padd outside the solar disk using zeros. Default np.nan.

'--no_rescale': Don't rescale the magnetogram before running inference. By default the magnetogram is rotated and scaled to a standard plate scale that is a multiple of HMI's mean plate scale of 0.504273/pixel.

 

Example:

python convert2HMI.py --instrument mdi --data_path /tmp/gong/input --destination /tmp/gong/output --use_patches --overwrite --plot

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Studies of self-organising groups like schools of fish or flocks of birds have sought to uncover the behavioural rules individuals use (local-level interactions) to coordinate their motion (global-level patterns). However, empirical studies tend to focus on short-term or one-off observations where coordination has already been established, or describe transitions between different coordinated states. As a result, we have a poor understanding of how behavioural rules develop and are maintained in groups. Here, we study the emergence and repeatability of coordinated motion in shoals of stickleback fish (Gasterosteus aculeatus). Shoals were introduced to a simple environment, where their spatio-temporal position was deduced via video analysis. Using directional correlation between fish velocities and wavelet analysis of fish positions, we demonstrate how shoals that are initially uncoordinated in their motion quickly transition to a coordinated state with defined individual leader-follower roles. The identities of leaders and followers were repeatable across two trials, and coordination was reached more quickly during the second trial, and by groups of fish with higher mean levels of activity. The rapid emergence of coordinated motion and repeatability of social roles in stickleback fish shoals may act to reduce uncertainty of social interactions in the wild, where individuals live in a system with high fission-fusion dynamics and non-random patterns of association.

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The gCube System - data-miner-manager-cl
--------------------------------------------------

DataMiner Manager Client Library


This software is part of the gCube Framework (https://www.gcube-system.org/): an
open-source software toolkit used for building and operating Hybrid Data
Infrastructures enabling the dynamic deployment of Virtual Research Environments
by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of
European Union programmes including:
* the Sixth Framework Programme for Research and Technological Development -
DILIGENT (grant no. 004260);
* the Seventh Framework Programme for research, technological development and
demonstration - D4Science (grant no. 212488), D4Science-II (grant no.
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine
(grant no. 283644);
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680),
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant
no. 654119), SoBigData (grant no. 654024);


Version
--------------------------------------------------

1.3.0-4.7.0-148453 (2017-10-12)

Please see the file named \"changelog.xml\" in this directory for the release notes.


Authors
--------------------------------------------------

* Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it),
Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" CNR, Pisa IT


Maintainers
-----------

* Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it),
Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" CNR, Pisa IT


Download information
--------------------------------------------------

Source code is available from SVN:
https://svn.research-infrastructures.eu/public/d4science/gcube/trunk/data-analysis/data-miner-manager-cl

Binaries can be downloaded from the gCube website:
https://www.gcube-system.org/


Installation
--------------------------------------------------

Installation documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/DataMinerManagerCL


Documentation
--------------------------------------------------

Documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/DataMinerManagerCL


Support
--------------------------------------------------

Bugs and support requests can be reported in the gCube issue tracking tool:
https://support.d4science.org/projects/gcube/


Licensing
--------------------------------------------------

This software is licensed under the terms you may find in the file named \"LICENSE\" in this directory.

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Using this program written in GAP 4.11.1, we show that for \\(n \\geqslant 5\\), any homomorphism from the special automorphism group of the free group, \\(\\operatorname{SAut}(F_n)\\) to the symmetric group \\(S_{19}\\) is trivial.

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The gCube System - gCube VRE Deploy Wizard Portlet
--------------------------------------------------

gCube VRE Deploy Wizard Portlet.


This software is part of the gCube Framework (https://www.gcube-system.org/): an
open-source software toolkit used for building and operating Hybrid Data
Infrastructures enabling the dynamic deployment of Virtual Research Environments
by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of
European Union programmes including:
* the Sixth Framework Programme for Research and Technological Development -
DILIGENT (grant no. 004260);
* the Seventh Framework Programme for research, technological development and
demonstration - D4Science (grant no. 212488), D4Science-II (grant no.
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine
(grant no. 283644);
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680),
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant
no. 654119), SoBigData (grant no. 654024);


Version
--------------------------------------------------

4.0.1-4.1.0-132544 (2016-11-29)

Please see the file named \"changelog.xml\" in this directory for the release notes.


Authors
--------------------------------------------------

* Massimiliano Assante (massimiliano.assante-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" - CNR, Pisa (Italy).



Maintainers
-----------

* Massimiliano Assante (massimiliano.assante-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" - CNR, Pisa (Italy).


Download information
--------------------------------------------------

Source code is available from SVN:
http://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/admin/vre-deploy

Binaries can be downloaded from the gCube website:
https://www.gcube-system.org/


Installation
--------------------------------------------------

Installation documentation is available on-line in the gCube Wiki:
https://wiki.gcube-system.org/gcube/index.php/


Documentation
--------------------------------------------------

Documentation is available on-line in the gCube Wiki:
https://wiki.gcube-system.org/gcube/index.php/
https://wiki.gcube-system.org/gcube/index.php/

Support
--------------------------------------------------

Bugs and support requests can be reported in the gCube issue tracking tool:
https://support.d4science.org/projects/gcube/


Licensing
--------------------------------------------------

This software is licensed under the terms you may find in the file named \"LICENSE\" in this directory.

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PyMICE is a Python\u00ae library for mice behavioural data analysis.

The library can be used for loading and analysing of data obtained\u00a0from IntelliCage\u2122 system in an intuitive way in Python programming language.

The library provides user with an object oriented application programming\u00a0interface (API) and a data abstraction layer. It also comes with auxiliary\u00a0tools supporting development of analysis workflows, like data validators and\u00a0a tool for workflow configuration.

We ask that PyMICE\u00a0resource identifier\u00a0(RRID:nlx_158570) is provided in any published research making use of PyMICE.

For more details please see the project website:\u00a0https://neuroinflab.wordpress.com/research/pymice/\u00a0.


Auhors of the library


Author of the tutorial data

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This is a minor bug-fix release in the 0.25.x series and includes some regression fixes and bug fixes. We recommend that all users upgrade to this version.

See the full whatsnew for a list of all the changes.

The release can be installed with conda from the defaults and conda-forge channels:

conda install pandas

Or via PyPI:

python3 -m pip install --upgrade pandas

Please report any issues with the release on the pandas issue tracker.

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1. When searching for food, great tits (Parus major) can use herbivore-induced plant volatiles (HIPVs) as an indicator of arthropod presence. Their ability to detect HIPVs was shown to be learned, and not innate, yet the flexibility and generalization of learning remains unclear. 2. We studied if, and if so how, na\u00efve and trained great tits (Parus major) discriminate between herbivore-induced and non-induced saplings of Scotch elm (Ulmus glabra) and cattley guava (Psidium cattleyanum). We chemically analysed the used plants and showed that their HIPVs differed significantly and overlapped only in a few compounds. 3. Birds trained to discriminate between herbivore-induced and non-induced saplings preferred the herbivore-induced saplings of the plant species they were trained to. Na\u00efve birds did not show any preferences. Our results indicate that the attraction of great tits to herbivore-induced plants is not innate, rather it is a skill that can be acquired through learning, one tree species at a time. 4. We demonstrate that the ability to learn to associate HIPVs with food reward is flexible, expressed to both tested plant species, even if the plant species has not coevolved with the bird species (i.e. guava). Our results imply that the birds are not capable of generalising HIPVs among tree species but suggest that they either learn to detect individual compounds or associate whole bouquets with food rewards.

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This package contains the code and data to reproduce the figures and tables in Dasaratha, Golub, and Hak (forthcoming), "Learning from Neighbors about a Changing State," Review of Economic Studies and to calculate equilibria in the model.

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MELODIST (MEteoroLOgical observation time series DISaggregation Tool) is an open-source software package written in Python for temporally downscaling (disaggregating) daily meteorological time series to hourly data. MELODIST is capable of disaggregating the most commonly used meteorological variables for geoscientific modelling including temperature, precipitation, humidity, wind speed, and shortwave radiation.

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Analysis of flow cytometry data relating to sporulation with phage sigma factors.

The raw output from flow cytometry (fcs files) is in the FCM.tar.gz folder. To reproduce the analysis the FCM folder should be uncompressed and saved in the repository data folder.

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The choice between two mutually exclusive measures (such as different levels of insulation) is made on the basis of their \"net present value\" (NPV), the difference of life-time savings and installation cost, rather than their SIR. This has been shown to be the more correct criterion on which to base the selection between two measures, both of which cannot be installed. The audit computes and reports to the user the energy savings, discounted dollar savings, installation cost, and SIR for each measure considered cost-effective. For those with SIR greater than the user-designated cutoff, a materials list gives the material name, type, and quantity required for installation of the measure. Weatherization Assistant permits entry of pre-retrofit billing data for gas or electrically heated homes or homes with electric air-conditioning. 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The script allows performing Spectral indices and Spectral decomposition analysis on Google Earth Engine satellite image collections. The script was developed using Google Colaboratory and does not need any desktop installation.

This Python script code was developed by dr. F. Brandolini and G. Domingo-Ribas to accompany the paper: "Brandolini F., Domingo-Ribas G., Zerboni A. & Turner S. "A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features",  Open Research Europe 2021, 1:22 (https://doi.org/10.12688/openreseurope.13135.1)

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current approaches take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the “natural” and “cultural” landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. Satellite remote sensing technologies have enabled significant improvements in landscape research. The advent of the cloud-based platform of Google Earth Engine has allowed the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. In this paper, the use of Sentinel-2 satellite data in the identification of palaeo-riverscape features has been assessed in the Po Plain, selected because it is characterized by human exploitation since the Mid-Holocene. A multi-temporal approach has been adopted to investigate the potential of satellite imagery to detect buried hydrological features along with Spectral Index and Spectral Decomposition analysis. This research represents one of the first applications of the GEE Python API in landscape studies. The complete FOSS-cloud protocol proposed here consists of a Python code script developed in Google Colab which could be simply adapted and replicated in different areas of the world.

The Sentinel 2 (S2) satellite data were accessed through the Python [1] module geemap [2] in Colab [3], a serverless Jupyter notebook computational environment for interactive development [4]. The native GEE Python API has relatively limited functionality for visualizing results but the geemap Python module was created specifically to fill this gap. Finally, the python code developed enables the analysis of the S2 filtered image collection through Spectral Index (SI) and Spectral Decomposition (SD) techniques. Each image was exported in Geo.TIFF format in QGIS [5] where the Min/Max values were adjusted with the Cumulative Count Cut tool. Finally, the figures presented in this paper were generated in the QGIS Layout Editor. The Python module rasterio [6] was used to create individual plots for each band of the raster. Additionally, the Python packages rioxarray [7] and matplotlib [8] were employed, respectively, to access each raster band and create customisable histograms of their values.

The code is avaialble in 3 formats: .py / .ipynb / .txt 

The location of each archaeological site are indicated in the "ROIs.txt" file.

The authors thank Prof. Qiusheng Wu (The University of Tennessee, Knoxville - USA) for his suggestions during the development of the script code. Part of the research has been defined with the support of the Dipartimento di Scienze della Terra “Ardito Desio” (Università degli Studi di Milano, Italy) in the framework of the project ‘Dipartimenti di Eccellenza 2018–2022’ (WP4—Risorse del Patrimonio Culturale) - Italian Ministry of Education, University, and Research (MIUR). The authors are grateful to the reviewers for
their constructive criticism and comments.

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Aging is associated with diverse molecular processes such as oxidative damage, a decrease in immunocompetence, or an increase in epigenetic abnormalities, mutations, and inflammations. Many of these processes are linked to nutrient-sensing signaling pathways, suggesting that diet plays a critical role in the aging process. In fact, the protein content in the diet can affect both longevity and fecundity, but often in opposite directions. Indeed, in many solitary organisms, a protein-rich diet dramatically shortens lifespan but increases egg production. We used the ant Temnothorax rugatulus to investigate the effect of the protein-to-carbohydrate ratio in the diet on the survival and fecundity of fertile workers. We fed colonies either a moderately high-protein or high-carbohydrate diet (2:1 and 1:2 respectively) and monitored worker survival and egg production over nine weeks. The protein-rich diet did not alter the ability of workers to lay eggs, but reduced worker survival, suggesting that consuming too much protein may shorten the lifespan of fertile ant workers without promoting reproduction. Our study shows for the first time that a protein-rich diet reduces the overall fitness of fertile workers.

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Original release of the scripts for Vulis et al. Climate signatures on lake and wetland size distributions in arctic deltas.

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This repository contains data and analysis scripts for the manuscript:

Coral reef resilience to thermal stress in the Eastern Tropical Pacific

Authors: Mauricio Romero-Torres, Alberto Acosta, Ana M. Palacio-Castro, Eric A. Treml, Fernando A. Zapata, David A. Paz-García, and James W. Porter

Journal: Global Change Biology doi:10.1111/gcb.15126

 

This work uses 44-year trend of live ETP coral cover (1970–2014), and SST data obtained from NOAA High-resolution Blended Analysis (OISST):

Contents:

Scripts:

Data:

 

DHW: Directory containing the data files and scripts used to extract OISST data and calculate DHW.

You do not have to run the code in the DHW folder to produce the data analysis in the paper (this can be achieved by running "Coral_cover_analysis.Rmd"). However, you could use these scripts to retrieve the SST data (.nc files from NOAA) or to verify the calculation of DHW based on the SST data. The outputs of these scripts are already included in the "Data/DHW.csv" and "Data/RoC_DWH.csv" files. By running the scripts inside this folder you will create those files again but they will be placed in the "DHW/Output/" folder

This requires ~ 18G of free space

Scripts

Data

Output:

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This project contains the files used to serve two distinct applications via node.js.

TicTrainer™

TicTrainer™ is a web tool meant to help people build their ability to fight their tics. It was built to implement the RE-ERP strategy developed by Kevin Black (see description here). A working server is available for routine public use at TicTrainer.com.

TicTimer Web

Also included now is TicTimer Web, which is accessed at root/nt/. TicTimer is a program designed to automate record keeping and reward delivery during tic suppression research using Woods and Himle's Tic Suppression Paradigm. This web-based version of TicTimer allows for sessions to be performed remotely and without the bulky token dispenser hardware that was used with the previous version of TicTimer. Additional details are provided in the TicTimer Web software article listed in "Related identifiers" below.

Version 4.0.2

Incorporated TicTimer Web into the existing server, so that research sessions can be run remotely.
v4.0.2 fixes a vulnerability that was found in v4.0.1. 

v4.0 incorporated TicTimer Web into the existing server, so research TicTimer sessions could be run remotely, and included a number of other tweaks and improvements including:

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python -m pip install taupy/
This will install the following dependencies, if not already present: 
'dd>=0.5.6', 'iteration_utilities>=0.11.0' 'more_itertools>=8.8.0', 'numpy>=1.19.4', 'pandas>=1.2.2', 'python_igraph>=0.9.6', 'scikit_learn>=0.24.2', 'sympy>=1.6.2', 'z3-solver>=4.8.14.0',
If you are on a Linux operating system, dd will automatically install the dd.cudd module which contains a compiled version of CUDD. On Windows and Mac OS, dd will not automatically install CUDD, and taupy will rely on dd's pure Python implementation of binary decision diagrams instead. This does not have implications for functionality, but may have for computation time. Depending on their system configuration, Windows users who use Python 3.10 may see a request from pip to install Microsoft's Visual C++ Build Tools. A guide for how to do this is available on Stackoverflow. This software can be uninstalled when pip has completed installation. This requirement is temporary at time of uploading this repository, and it does not apply to Windows users on Python 3.9. The notebooks are in the notebooks folder and they contain the information that one needs to pass to taupy in order to obtain the results presented in the paper. The notebooks contain additional documentation of this code. 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"instancetype": {"classid": "0029", "classname": "Software", "schemename": "dnet:publication_resource", "schemeid": "dnet:publication_resource"}}], "dateofcollection": "2022-11-11T00:19:05+0000", "fulltext": [], "dateoftransformation": "2022-11-11T07:30:45.963Z", "description": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "SpecCal v.1.2

SpecCal is a stand-alone IDL application for the in-field spectral calibration of high-resolution spectrometers, based on the spectral matching algorithm proposed by Meroni et al. (2010). This repository contains a copy of the software originally submitted alongside the paper by Busetto et al. \"SpecCal: a novel software for in-field spectral characterization of high-resolution spectrometers\" (See full details below).

NOTE If you encounter any problems, you can report it in the \"issues\" page of this repository, However, since the software is not currently actively manteined, we can't guarantee we'll be able to help.

Authors:

Lorenzo Busetto, PhD Email: lbusett@gmail.com

Michele Meroni, PhD email: michele.meroni@jrc.ec.europa.eu

License:

GPL-3.0

Citation:

Please cite as:

L. Busetto, M. Meroni, G.F. Crosta, L. Guanter, R. Colombo, SpecCal: Novel software for in-field spectral characterization of high-resolution spectrometers, Computers & Geosciences, Volume 37, Issue 10, October 2011, Pages 1685-1691, ISSN 0098-3004, http://doi.org/10.1016/j.cageo.2010.12.005.

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Yn newydd yn y cyhoeddiad yma o'r gwaith yw: sgriptiau cychwynnol i rhag-hyfforddi modelau gyda rhagor o sain leferydd Cymraeg ac yna i'w fireinio ar gyfer wireddu adnabod lleferydd Cymraeg gwell o ganlyniad, model arbrofol newydd ('wav2vec2-base-cy') sydd wedi ei rhag-hyfforddi gyda dros 180 awr o leferydd Cymraeg o amrywiaeth o fideos YouTube. modd i hyfforddi gydag is-setiau ein hunain a fwy defnyddiol o fersiwn 11 o Common Voice Cymraeg a Saesneg (gweler https://github.com/techiaith/docker-commonvoice-custom-splits-builder) a chyhoeddwyd ym mis Medi 2022. o ganlyniad, model acwstig adnabod lleferydd dwyieithog Cymraeg a Saesneg newydd ('wav2vec2-xlsr-ft-en-cy') gyda WER o 17.07% a model adnabod lleferydd Cymraeg ('wav2vec2-xlsr-ft-cy') lawer mwy effeithiol a chywir gyda gostyngiad o 67% yn y WER o 12.38% i 4.05% ar gyfer adnabod lleferydd Cymraeg yn unig. seilwaith gweinydd API trawsgrifio newydd gyda'r modd i gysylltu ag API sy'n atgyweirio atalnodi a chyfalafu mewn testunau Cymraeg (gweler https://github.com/techiaith/docker-atalnodi-server) Ceir ffeiliau modelau ar wefan HuggingFace: https://huggingface.co/techiaith/wav2vec2-xlsr-ft-cy/tree/22.10 https://huggingface.co/techiaith/wav2vec2-xlsr-ft-en-cy/tree/22.10 https://huggingface.co/techiaith/wav2vec2-base-cy/tree/22.10 <hr/> in English These are our models and scripts in October 2022 (22.10) for effective Welsh speech recognition based on wav2vec2. 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https://github.com/techiaith/docker-atalnodi-server) Model files can be found on the HuggingFace website: https://huggingface.co/techiaith/wav2vec2-xlsr-ft-cy/tree/22.10 https://huggingface.co/techiaith/wav2vec2-xlsr-ft-en-cy/tree/22.10 https://huggingface.co/techiaith/wav2vec2-base-cy/tree/22.10"}], "language": {"classid": "und", "classname": "Undetermined", "schemename": "dnet:languages", "schemeid": "dnet:languages"}, "author": [{"surname": "Jones", "name": "Dewi Bryn", "pid": [], "rank": 1, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "@techiaith"}], "fullname": "Jones, Dewi Bryn"}], "resourcetype": {"classid": "UNKNOWN", "classname": "Unknown", "schemename": "dnet:dataCite_resource", "schemeid": "dnet:dataCite_resource"}, "pid": [{"dataInfo": 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Release before splitting the framework to submodules and plugins. This version still has navigation code, arduino code, networking code and all the other modules. The new version will be stripped to the core logger, UI, player and experiment functionality necessary in almost every experiment.

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Geneva, Switzerland, WMO, 466pp. (WMO-No. 306: I.1.A). https://library.wmo.int/index.php?lvl=notice_display&id=13617\", \"Veledin, V. and Gorodetskaya, I. (2020). Original telegrams of surface weather, ocean and ice state observations in the Southern Ocean on board the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017. (Version 1.0) [Data set]. 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"10|openaire____::9e3be59865b2c1c335d32dae2fe7b254"}], "dateoftransformation": "2020-08-01T17:39:12+0000", "dateofcollection": "2020-08-01T17:39:12+0000", "id": "50|doi_________::0ad7a4b86c1ba1987b85024e4c24c75d", "bestaccessright": {"classid": "OPEN", "classname": "Open Access", "schemename": "dnet:access_modes", "schemeid": "dnet:access_modes"}} +{"publisher": {"value": "Zenodo"}, "dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "description": [{"value": "This record collates DOIs for the software components used in 'An augmented Lagrangian preconditioner for the 3D stationary incompressible Navier--Stokes equations at high Reynolds number'. The Firedrake components and dependencies used were: COFFEE (A Compiler for Fast Expression Evaluation): 10.5281/zenodo.1064647 FInAT (a smarter library of finite elements): 10.5281/zenodo.1287050 fiat (The Finite Element Automated Tabulator): 10.5281/zenodo.1436621 firedrake (an automated finite element system): 10.5281/zenodo.1306051 petsc (Portable, Extensible Toolkit for Scientific Computation): 10.5281/zenodo.1436626 petsc4py (The Python interface to PETSc): 10.5281/zenodo.1287054 tsfc (The Two Stage Form Compiler): 10.5281/zenodo.1287051 ufl (The Unified Form Language): 10.5281/zenodo.1436625 PyOP2 (Framework for performance-portable parallel computations on unstructured meshes): 10.5281/zenodo.1287049 SSC (Subspace corrections preconditioner): 10.5281/zenodo.1438768 You can install Firedrake using exactly this set of component versions using: 
export PETSC_CONFIGURE_OPTIONS=\"--download-superlu --download-superlu_dist\" firedrake-install --doi 10.5281/zenodo.1436628
See firedrakeproject.org/download.html for more information."}], "language": {"classid": "und", "classname": "Undetermined", "schemename": "dnet:languages", "schemeid": "dnet:languages"}, "author": [{"affiliation": [], "fullname": "Firedrake-Zenodo", "pid": [], "rank": 1}], "resourcetype": {"classid": "UNKNOWN", "classname": "Unknown", "schemename": "dnet:dataCite_resource", "schemeid": "dnet:dataCite_resource"}, "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "doi", "classname": "Digital Object Identifier", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "10.5281/zenodo.1436627"}], "title": [{"qualifier": {"classid": "main title", "classname": "main title", "schemename": "dnet:dataCite_title", "schemeid": "dnet:dataCite_title"}, "value": "Software Used In 'An Augmented Lagrangian Preconditioner For The 3D Stationary Incompressible Navier--Stokes Equations At High Reynolds Number'"}], "originalId": ["10.5281/zenodo.1436627"], "subject": [], "instance": [{"refereed": {"classid": "0000", "classname": "Unknown", "schemename": "dnet:review_levels", "schemeid": "dnet:review_levels"}, "hostedby": {"value": "ZENODO", "key": "10|opendoar____::358aee4cc897452c00244351e4d91f69"}, "license": {"value": "https://creativecommons.org/licenses/by/4.0"}, "url": ["https://dx.doi.org/10.5281/zenodo.1436627"], "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "doi", "classname": "Digital Object Identifier", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "10.5281/zenodo.1436627"}], "dateofacceptance": {"value": "2018-09-27"}, "collectedfrom": {"value": "Datacite", "key": "10|openaire____::9e3be59865b2c1c335d32dae2fe7b254"}, "accessright": {"classid": "OPEN", "classname": "Open Access", "schemename": "dnet:access_modes", "schemeid": "dnet:access_modes"}, "instancetype": {"classid": "0029", "classname": "Software", "schemename": "dnet:publication_resource", "schemeid": "dnet:publication_resource"}}], "relevantdate": [{"qualifier": {"classid": "issued", "classname": "issued", "schemename": "dnet:dataCite_date", "schemeid": "dnet:dataCite_date"}, "value": "2018-09-27"}], "resulttype": {"classid": "software", "classname": "software", "schemename": "dnet:result_typologies", "schemeid": "dnet:result_typologies"}, "dateofacceptance": {"value": "2018-09-27"}, "collectedfrom": [{"value": "Datacite", "key": "10|openaire____::9e3be59865b2c1c335d32dae2fe7b254"}], "dateoftransformation": "2020-09-20T07:42:52+0000", "dateofcollection": "2020-09-20T07:42:52+0000", "id": "50|doi_________::0b355245fb1a237f1500b777e00c2ac0", "bestaccessright": {"classid": "OPEN", "classname": "Open Access", "schemename": "dnet:access_modes", "schemeid": "dnet:access_modes"}} +{"publisher": {"value": "Zenodo"}, "dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "description": [{"value": "IFITM proteins play a role in cancer cell progression through undefined mechanisms. Here, we propose an emergent role of IFITM1/3 regulating protein synthesis. SBP tagged IFITM1 protein was used to identify an association of IFITM1 protein with the cytosolic isoform of SRSF1 that transports mRNA to the ribosome. This cytosolic association was confirmed in situ using proximity ligation assays for SRSF1 and IFITM1/3, suggesting a role associated with translation. Accordingly, IFITM1/3 were shown to interact with HLA-B mRNA in response to IFN\u03b3 stimulation using RNA-protein proximity ligation assays. In addition, shotgun RNA sequencing in IFITM1/IFITM3 null cells and wt-SiHa cells indicated that reduced HLA-B gene expression does not account for lowered HLA-B protein synthesis in response to IFN\u03b3. Furthermore, ribosome profiling using sucrose gradient sedimentation identified a reduction in 80S ribosomal fraction an IFITM1/IFITM3 null cells compared to their wild-type counterpart, partially reverted by IFITM1/3 complementation. 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Cheng Huang, Radek Chrapkiewicz, Junjie Luo, Jessica A. Cardin, Mark J. Schnitzer, Vincent A. Pieribone

 

Genetically encoded fluorescent voltage indicators are ideally suited to reveal the millisecond-scale interactions among and between distinct, targeted cell populations. However, current indicator families lack the requisite sensitivity for in vivo multipopulation imaging. We describe high-performance green and red sensors, Ace-mNeon2 and VARNAM2, and their reverse response-polarity variants, pAce and pAceR. Our indicators enable 0.4-1 kHz voltage recordings from >50 neurons per field-of-view in awake mice and ~30-min continuous imaging in flies. Using dual-polarity multiplexed imaging, we uncovered behavioral state-dependent interactions between distinct neocortical subclasses, as well as contributions to hippocampal field potentials from non-overlapping projection neuronal ensembles. By combining three mutually compatible indicators, we demonstrate concurrent triple-population voltage imaging. Our approach will empower investigations of the dynamic interplay between neuronal subclasses at single-spike resolution.

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This code will accompany the subsequent publication.

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This repository houses the code and data for conducting the minijack rate analyses found in the manuscript titled: Lack of an effect of sire age on precocious maturation in hatchery spring Chinook Salmon as age-2 minijacks by P.F. Galbreath, C.A. Stockton, C.M. Knudsen, L.R. Medeiros, I.J. Koch, B.A. Staton, W.J. Bosch, H. Nuetzel, and A.L. Pierce.

The repository is considered \"preliminary\" since the manuscript has not yet completed the peer review process.

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A tool to process csv files obtained from monotonic and cyclic loading tests including high-cycle fatigue

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Release of version 1.018.2019

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IMPORTANT: Update Caper >= 0.8.

``` $ pip install caper --upgrade ```

IMPORTANT: Conda users must update pipeline's Conda env.

``` $ bash scripts/update_conda_env.sh ```

Bug fixes

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This page contains the software and test data used in "Identify, quantify and characterize cellular communication from single cell RNA sequencing data with scSeqComm" [Giacomo Baruzzo, Giulia Cesaro, Barbara Di Camillo; Bioinformatics, 2022]

 

Software

Folder Software contains the code of R package scSeqComm (file scseqcomm.zip). The R package contains also 21 ligand-receptor pairs databases (including the ones used in the manuscripts) and 9 transcriptional regulatory network databases (including the ones used in the manuscript). scSeqComm R package is freely available at https://gitlab.com/sysbiobig/scseqcomm and https://sysbiobig.dei.unipd.it/software/#scSeqComm.

 

Test data

Folder Test data contains the datasets used in the manuscript.

 

Additional references:

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Important Backwards incompatible changes Highlights: Acknowledgements:

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This upload includes 1) ImageJ macros / scripts for minor automated cell counting for brain slides with immunostaining and 2) MATLAB functions / scripts for minor automated  spine location cluster analysis (nearest neighbor distance / index).

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We released a trio of 'Cookbooks' on how to build a student-led CubeSat plus a guide for student High Altitude Balloon projects for payload testing. We released our 4 hardware designs (Power bus, CPU board, Sensors Board, Solar Panels) and our flight software as open source to the community. We also provide the CAD files for the 3U+ tabbed structure for other teams needing fabrication.

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Package for calculation of Q factors of strained beams with non-uniform width and thickness together with supplementary theory note. Possible boundary conditions are doubly clamped or Floquet-periodic

Used for calculations published in "Generalized dissipation dilution in strained mechanical resonators", "Elastic strain engineering for ultralow mechanical dissipation" and "Clamp-tapering increases the quality factor of stressed nanobeams". 

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Structure Embedding Version 2 Release 2 (StrEmbed-2 Release A)

University of Leeds The Open University

Embedding design structures in engineering information An EPSRC Design the Future project (EP/N005694/1)

StrEmbed-2 \u2014 Embedding project second prototype StrEmbed (Structure Embedding) Version 2 Release A

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The gCube System - OpensSearchDataSourceCommons
--------------------------------------------------

OpenSearch DataSource Commons


This software is part of the gCube Framework (https://www.gcube-system.org/): an
open-source software toolkit used for building and operating Hybrid Data
Infrastructures enabling the dynamic deployment of Virtual Research Environments
by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of
European Union programmes including:
* the Sixth Framework Programme for Research and Technological Development -
DILIGENT (grant no. 004260);
* the Seventh Framework Programme for research, technological development and
demonstration - D4Science (grant no. 212488), D4Science-II (grant no.
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine
(grant no. 283644);
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680),
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant
no. 654119), SoBigData (grant no. 654024);


Version
--------------------------------------------------

1.0.0-4.2.1-126502 (2017-02-12)

Please see the file named \"changelog.xml\" in this directory for the release notes.


Authors
--------------------------------------------------
* Alex Antoniadis (p.liakos@di.uoa.gr), University of Athens,
Department of Informatics and Telecommunications.

Maintainers
--------------------------------------------------

* Nikolaos Laskaris (laskarisn@di.uoa.gr), National and Kapodistrian University of Athens


Download information
--------------------------------------------------

Source code is available from SVN: http://svn.research-infrastructures.eu/public/d4science/gcube/branches/search/opensearchdatasource-commons/1.0/opensearchdatasource-commons

Binaries can be downloaded from the gCube website: https://www.gcube-system.org/


Installation
--------------------------------------------------

Installation documentation is available on-line in the gCube Wiki:
https://wiki.gcube-system.org/gcube/index.php

Documentation
--------------------------------------------------

Documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/index.php/Index_Management_Framework#Full_Text_Index
Support
--------------------------------------------------

Bugs and support requests can be reported in the gCube issue tracking tool:
https://support.d4science.org/projects/gcube/


Licensing
--------------------------------------------------

This software is licensed under the terms you may find in the file named \"LICENSE\" in this directory.


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================================[Special attention]Required for operation
sudo pip install tensorboard-chainer================================


*Lotter, W., Kreiman, G., & Cox, D., Deep predictive coding networks for video prediction and unsupervised learning, arXiv 1605.08104.
This program is new version used inWatanabe E, Kitaoka A, Sakamoto K, Yasugi M and Tanaka K, Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction. Frontiers in Psychology 9:345. doi: 10.3389/fpsyg.2018.00345 (2018)"}], "language": {"classid": "und", "classname": "Undetermined", "schemename": "dnet:languages", "schemeid": "dnet:languages"}, "author": [{"surname": "Watanabe", "name": "Eiji", "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "orcid_pending", "classname": "Open Researcher and Contributor ID", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "0000-0001-9089-8122"}], "rank": 1, "affiliation": [], "fullname": "Watanabe, Eiji"}], "resourcetype": {"classid": "UNKNOWN", "classname": "Unknown", "schemename": "dnet:dataCite_resource", "schemeid": "dnet:dataCite_resource"}, "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": 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The following ISINs changed their status:

<table> <thead> <tr> <th>ISIN</th> <th>Description</th> <th>Old Status</th> <th>New Status</th> </tr> </thead> <tbody> <tr> <td><code>INE013U14214</code></td> <td> GLOBE FINCAP LIMITED 365D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE016A14AU9</code></td> <td> DABUR INDIA LIMITED 90D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE020B08AK2</code></td> <td> RURAL ELECTRIFICATION CORPORATION LIMITED SR-150 7.03 BD 07SP22 FVRS10LAC </td> <td>Suspended</td> <td>Blocked Due To Aca</td> </tr> <tr> <td><code>INE059K07089</code></td> <td> CASPIAN IMPACT INVESTMENTS PRIVATE LIMITED SR 8 11.50 NCD 08SP22 FVRS1LAC </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE089C14CA0</code></td> <td> STERLITE TECHNOLOGIES LIMITED 85D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE092T16SD3</code></td> <td> IDFC FIRST BANK LIMITED CD 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE093JA7OG1</code></td> <td> ANAND RATHI GLOBAL FINANCE LIMITED SR IV BR NCD 02SP22 FVRS1LAC </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE0A2414031</code></td> <td> ORIGO COMMODITIES INDIA PRIVATE LIMITED 92D CP 06SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE0B4P14267</code></td> <td> LOANTAP CREDIT PRODUCTS PRIVATE LIMITED 275D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE128M14118</code></td> <td> L&T METRO RAIL (HYDERABAD) LIMITED 71D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE169A14GQ1</code></td> <td> COROMANDEL INTERNATIONAL LIMITED 90D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE233A14VG5</code></td> <td> GODREJ INDUSTRIES LIMITED 81D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE296A14TE1</code></td> <td> BAJAJ FINANCE LIMITED 91D CP 26AUG22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE296A14TF8</code></td> <td> BAJAJ FINANCE LIMITED 91D CP 30AUG22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE338I14DJ7</code></td> <td> MOTILAL OSWAL FINANCIAL SERVICES LIMITED 107D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE367Y14125</code></td> <td> WHEELSEMI PRIVATE LIMITED 149D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE391V07059</code></td> <td> SHREM INFRA STRUCTURE PRIVATE LIMITED SR C 1-150 8.39 LOA 31AG22 FVRS10LAC </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE423A14PE9</code></td> <td> ADANI ENTERPRISES LIMITED 182D CP 08SEP22 </td> <td>Active</td> <td>Blocked Due To Aca</td> </tr> <tr> <td><code>INE629D01012</code></td> <td> BANSWARA SYNTEX LIMITED EQ </td> <td>Active</td> <td>Blocked Due To Aca</td> </tr> <tr> <td><code>INE685A07090</code></td> <td> TORRENT PHARMACEUTICALS LIMITED RR LOA 31AG22 FVRS10LAC </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE695F09425</code></td> <td> ANDHRA PRADESH STATE FINANCIAL CORPORATION 9.48 LOA 28MR22 FVRS10LAC </td> <td>Active</td> <td>Blocked Due To Aca</td> </tr> <tr> <td><code>INE700G14CG0</code></td> <td> HDFC SECURITIES LIMITED 91D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE723X14BV8</code></td> <td> TRUST INVESTMENT ADVISORS PRIVATE LIMITED 105D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE723X14BX4</code></td> <td> TRUST INVESTMENT ADVISORS PRIVATE LIMITED 91D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE742F14QM2</code></td> <td> ADANI PORTS AND SPECIAL ECONOMIC ZONE LIMITED 60D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE742F14QN0</code></td> <td> ADANI PORTS AND SPECIAL ECONOMIC ZONE LIMITED 59D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE804IA7089</code></td> <td> ECL FINANCE LIMITED SR III 10.2 NCD 23AG22 FVRS1000 </td> <td>Active</td> <td>Blocked Due To Aca</td> </tr> <tr> <td><code>INE804IA7097</code></td> <td> ECL FINANCE LIMITED SR IV NCD 23AG22 FVRS1000 </td> <td>Active</td> <td>Blocked Due To Aca</td> </tr> <tr> <td><code>INE824H14HX0</code></td> <td> JULIUS BAER CAPITAL (INDIA) PRIVATE LIMITED 91D CP 08SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE831R07185</code></td> <td> ADITYA BIRLA HOUSING FINANCE LIMITED SR-F1 7.6 NCD 08SP22 FVRS10LAC </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE831R14CE5</code></td> <td> ADITYA BIRLA HOUSING FINANCE LIMITED 91D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE850D14ME4</code></td> <td> GODREJ AGROVET LIMITED 88D CP 09SEP22 </td> <td>Active</td> <td>Deleted</td> </tr> <tr> <td><code>INE860H07BS1</code></td> <td> ADITYA BIRLA FINANCE LIMITED SR-L1 8.85 NCD 09SP22 FVRS10LAC </td> <td>Active</td> <td>Deleted</td> </tr> </tbody> </table>

The following ISINs changed other fields:

diff

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TACAS'20 Artifact for CPU Energy Meter
======================================

This artifact for our TACAS'20 paper
"CPU Energy Meter: A Tool for Energy-Aware Algorithms Engineering"
consists of the tool CPU Energy Meter (https://github.com/sosy-lab/cpu-energy-meter)
which is available under the BSD 2-clause license.
Furthermore, the tool BenchExec (https://github.com/sosy-lab/benchexec),
which is available under the Apache 2.0 license,
is included to demonstrate the integration of CPU Energy Meter into BenchExec.

The tools CPU Energy Meter and BenchExec are also archived by themselves
(https://doi.org/10.5281/zenodo.1300309, https://doi.org/10.5281/zenodo.1163552).

Paper Abstract
--------------

Verification algorithms are among the most resource-intensive computation tasks.
Saving energy is important for our living environment and
to save cost in data centers.
Yet, researchers compare the efficiency of algorithms
still in terms of consumption of CPU time (or even wall time).
Perhaps one reason for this is that measuring energy consumption of computational
processes is not as convenient as measuring the consumed time
and there is no sufficient tool support.
To close this gap, we contribute CPU Energy Meter,
a small tool that takes care of reading the energy values
that Intel CPUs track inside the chip.
In order to make energy measurements as easy as possible,
we integrated CPU Energy Meter into BenchExec,
a benchmarking tool that is already used by many researchers
and competitions in the domain of formal methods.
As evidence for usefulness, we explored the energy consumption of some state-of-the-art verifiers
and report some interesting insights, for example, that
energy consumption is not necessarily correlated with CPU time.

Requirements
------------

CPU Energy Meter requires an Intel CPU with the RAPL feature,
which is present in x86 CPUs since the Sandy Bridge generation
(e.g., Core i3/5/7 2000 and above or Xeon E3/E5/E7).
Furthermore, the tool works only on a real machine (*no VM* and no container)
because it needs direct access to the so-called model-specific registers (MSRs)
of the CPU.
To access these registers, it requires a Linux kernel with the module "msr"
(this module should be available in standard distribution kernels).
The tool was tested on 64-bit Linux systems.
Furthermore, for installation, root access is required
(for example to activate the mentioned kernel module).

Installation
------------

For a Debian-based distribution (e.g., Ubuntu),
it is recommended to install the Debian packages (cf. TACAS20-cpu-energy-meter/deb/INSTALL.md).
Otherwise, please follow the instructions for installing from source
in TACAS20-cpu-energy-meter/source/INSTALL.md.

Note that in both cases, CPU Energy Meter can be installed either alone
(for experimenting with this tool alone)
or in addition to BenchExec.

Quick Test
----------

If CPU Energy Meter is correctly installed, the tool can be executed with "cpu-energy-meter".
It will produce output (showing the consumed energy while it was running)
only after Ctrl+C is pressed to terminate it.
A mode with debug output is available via "cpu-energy-meter -d".

The installation attempts to configure the host system such that CPU Energy Meter
is usable for all users.
If the tool does not work, please try executing it with root privileges: "sudo cpu-energy-meter".
Please also check that the kernel module "msr" is loaded with "lsmod | grep msr"
and that the appropriate device modules are present: "ls /dev/cpu/*/msr".

Use in Benchmarking
-------------------

CPU Energy Meter is intended to be used primarily by benchmarking frameworks
instead of end users.
If BenchExec is installed, it is possible to benchmark a single tool execution
with "runexec", e.g., "runexec --read-only-dir / -- /bin/sh -c 'echo "scale=2000; a(1)*4" | bc -l'"
(this example calculates 2000 digits of pi,
of course different commands can be used).
The command "sudo sysctl -w kernel.unprivileged_userns_clone=1" might be necessary
for BenchExec on non-Ubuntu systems to configure the kernel appropriately.
If CPU Energy Meter is installed, BenchExec will automatically use it to measure the energy consumption
and will report values such as

    cpuenergy=16.387085J
    cpuenergy-pkg0=16.387085J
    cpuenergy-pkg0-core=14.647461J
    cpuenergy-pkg0-dram=1.449158J
    cpuenergy-pkg0-psys=28.922119J
    cpuenergy-pkg0-uncore=0.013672J

The value "cpuenergy" is the sum of the "cpuenergy-pkg[0-9]+" values for all CPUs that were used.
The other values report the energy consumption of the various measurement domains
that the CPU(s) support (it depends on the CPU which values are available).

BenchExec also supports executing and measuring large sets of tool executions automatically.
This is independent of CPU Energy Meter and just requires the same steps
as benchmarking with BenchExec alone would.

As an example for those who are not familiar with BenchExec
we provide an example benchmark definition that calculates pi with 1000 to 10000 digits.
To execute this benchmark set, run "benchexec benchmark-example-calculatepi.xml".
BenchExec needs a proper setup of cgroups and uses some kernel features that might not be available on all systems.
If you have problems executing benchexec,
run "sudo sysctl -w kernel.unprivileged_userns_clone=1"
and "benchexec benchmark-example-calculatepi.xml --read-only-dir / -T -1 -M -1".
This will run BenchExec in a restricted mode with less measurements,
but measuring CPU energy will still work if cpu-energy-meter is properly installed.
In any case, to generate HTML and CSV tables with the results (including the energy measurements),
run "table-generator results/benchmark-example-calculatepi.*.results.xml.bz2 --all-columns --show" afterwards.

In order to use benchexec with other tools (e.g., verifiers),
a tool-specific module needs to be added to BenchExec
and an appropriate benchmark definition needs to be written.
This had been done for all participants of SV-COMP,
so it is possible to benchmark any participating verifier
using the verifier archives from https://gitlab.com/sosy-lab/sv-comp/archives-2019
and the benchmark definitions similar to those at https://github.com/sosy-lab/sv-comp/tree/svcomp19.

Reproducing Fig. 1
------------------

Fig. 1 was produced by measuring the energy consumption of an Intel Core i5-6440HQ CPU
in a Lenovo ThinkPad T460p for 10s.
This experiment can be replicated with the command
cpu-energy-meter & sleep 10s ; kill -INT %1
(reported domains and results will vary depending on the system).

Reproducing Table 1 and Figs. 2-4
---------------------------------

The results for these figures where taken from the literature
(Dirk Beyer, "Automatic Verification of C and Java Programs: SV-COMP 2019", Proc. TACAS'19,
 https://doi.org/10.1007/978-3-030-17502-3_9).
The raw data are available at
https://sv-comp.sosy-lab.org/2019/results/results-verified/All-Raw.zip.
More information on replicating these results is in the above publication
and on the SV-COMP website (https://sv-comp.sosy-lab.org/2019/systems.php).
The SV-COMP benchmark set (https://github.com/sosy-lab/sv-benchmarks/tree/svcomp19),
all participating tools (https://gitlab.com/sosy-lab/sv-comp/archives-2019),
and the benchmark definitions (https://github.com/sosy-lab/sv-comp/tree/svcomp19)
are available online.

Baseline Measurements
---------------------

We provide baseline measurements (of an idle state)
of the same system on which SV-COMP'19 was executed
as a comparison for the data in Table 1 and Figs. 2-4:

Domain     Avg. Idle Power (W)
------------------------------
Package         1.1
PP0 (Core)      0.34
DRAM            1.5

For the package domain, this is one order of magnitude less
than the average power when benchmarking CBMC or CPA-Seq.
Note that in Figs. 2 and 3 (which show the package domain)
the gray line towards the bottom of the plot
represents an average power of 1 W
and is thus roughly showing the idle energy consumption.
If we were to subtract the idle energy consumption from the energy measurements
in Table 1 and Figs. 2-4, the overall picture would not change
and the conclusions would remain the same.

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{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "2021-02-10"}, "collectedfrom": {"value": "ZENODO", "key": "10|opendoar____::358aee4cc897452c00244351e4d91f69"}, "accessright": {"classid": "OPEN", "classname": "Open Access", "schemename": "dnet:access_modes", "schemeid": "dnet:access_modes"}, "instancetype": {"classid": "0029", "classname": "Software", "schemename": "dnet:publication_resource", "schemeid": "dnet:publication_resource"}}], "dateofcollection": "2022-11-11T00:19:05+0000", "fulltext": [], "dateoftransformation": "2022-11-11T07:32:22.113Z", "description": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:crosswalk:repository", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "

The adaptive domain misorientation approach can resolve sub-grains and dislocation cell using conventional EBSD. The measurement principle utilises measurement domains that are grown radially until a specified misorientation value has been reached. This enables stochastic analysis of local misorientation to be carried out within individual sub-grains and dislocation cells.

The sub-structural boundaries are classified according to the total misorientation across the boundary region, the thickness of which can vary from approximately one hundred nanometres to several hundred nanometres. Sub-grain boundaries with a total misorientation larger than 2° are resolved effectively for as-measured Hough-based EBSD data. De-noising of the EBSD data allows small dislocation cells to be resolved, typically having a misorientation of 0.4° – 1.0°. The developed approach is found suitable to various deformed metals, showing a significant increase in the level of detail resolved compared to the conventional kernel misorientation approach.

Published under GNU GPL v3 License.

For further information, refer to the journal article and Wiki page:

Revision history:

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The gCube System - OperatorLibary
--------------------------------------------------

OperatorLibary


This software is part of the gCube Framework (https://www.gcube-system.org/): an
open-source software toolkit used for building and operating Hybrid Data
Infrastructures enabling the dynamic deployment of Virtual Research Environments
by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of
European Union programmes including:
* the Sixth Framework Programme for Research and Technological Development -
DILIGENT (grant no. 004260);
* the Seventh Framework Programme for research, technological development and
demonstration - D4Science (grant no. 212488), D4Science-II (grant no.
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine
(grant no. 283644);
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680),
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant
no. 654119), SoBigData (grant no. 654024);


Version
--------------------------------------------------

1.3.1-4.2.1-126502 (2017-02-13)

Please see the file named \"changelog.xml\" in this directory for the release notes.


Authors
--------------------------------------------------
Gerasimos Farantatos (g.farantatos-AT-di.uoa.gr), NKUA Athens,
National Kapodistrian University of Athens.


Maintainers
--------------------------------------------------

* Nikolaos Laskaris (laskarisn@di.uoa.gr), National and Kapodistrian University of Athens


Download information
--------------------------------------------------

Source code is available from SVN: http://svn.research-infrastructures.eu/public/d4science/gcube/branches/search/OperatorLibrary/1.3/OperatorLibrary

Binaries can be downloaded from the gCube website: https://www.gcube-system.org/


Installation
--------------------------------------------------

Installation documentation is available on-line in the gCube Wiki:

https://wiki.gcube-system.org/gcube/index.php

Documentation
--------------------------------------------------

Documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/index.php/Search_Operators

Support
--------------------------------------------------

Bugs and support requests can be reported in the gCube issue tracking tool:
https://support.d4science.org/projects/gcube/


Licensing
--------------------------------------------------

This software is licensed under the terms you may find in the file named \"LICENSE\" in this directory.


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Identifies named entities, environmental terms, linguistic features and sentiment in social media texts."}], "format": [], "measures": [], "coverage": [], "externalReference": [], "publisher": {"value": "The University of Sheffield"}, "eoscifguidelines": [], "language": {"classid": "und", "classname": "Undetermined", "schemename": "dnet:languages", "schemeid": "dnet:languages"}, "resulttype": {"classid": "software", "classname": "software", "schemename": "dnet:result_typologies", "schemeid": "dnet:result_typologies"}, "country": [], "extraInfo": [], "originalId": ["10.57771/j5ya-7h72"], "source": [], "context": [], "title": [{"qualifier": {"classid": "AlternativeTitle", "classname": "AlternativeTitle", "schemename": "dnet:dataCite_title", "schemeid": "dnet:dataCite_title"}, "value": "environmental-annotator"}, {"qualifier": {"classid": "main title", "classname": "main title", "schemename": "dnet:dataCite_title", "schemeid": "dnet:dataCite_title"}, "value": "DecarboNet Environmental Annotator"}]} +{"publisher": {"value": "Zenodo"}, "dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "description": [{"value": "Questions: To gain insights into the role of species-by-species replacements in cloud forest community structuring, we asked: (1) What are the effects of the spatial distribution of standing individuals on the seed rain, soil seed bank, and sapling density and survival in this cloud forest? and (2) What is the prevalence of conspecific vs. heterospecific replacements in the regeneration of this forest? Location: Santo Tom\u00e1s Teipan, Oaxaca State, southern Mexico. Methods: In a 1-ha cloud forest plot we assessed seed rain, seed bank, and sapling density and survival of four canopy tree species (Chiranthodendron pentadactylon, Cornus disciflora, Quercus laurina, Oreopanax xalapensis). All standing individuals of these and other tree species (dbh \u2265 2.5 cm) were mapped. We used neighbourhood models to examine the spatial patterns of the three life cycle stages relative to the spatial distribution of adults. The neighbourhood effect was assessed through the Neighbourhood Index, which integrates information on size (dbh) and distance to adults. Data analysis was based on maximum likelihood and model selection procedures. Results: We found large between-species differences regarding the spatial patterns of seeds and saplings. Three species showed evidence for the Janzen-Connell effect operating at the seed (C. pentadactylon and Q. laurina) or sapling (O. xalapensis) stage. We also found support for a critical role of specific microsite factors (i.e., niche differentiation) in the regeneration of two species (C. pentadactylon and C. disciflora). Conclusions: Seed and sapling distribution patterns suggest the prevalence of heterospecific replacements, and that both Janzen-Connell and niche differentiation effects contribute to this pattern. Our results largely support the notion that the prevalence of heterospecific replacements among canopy species promotes species coexistence in cloud forest."}, {"value": "Mej\u00eda-Dom\u00ednguez et al., 2021 Data. The data come from the 2007 census of 1-ha cloud forest plot in Teipan, Oaxaca, southern Mexico (16\u00b015' N; 95\u00b058' W; 2200-2500 m asl). The format is an Excel Book. Page 'data_dbh>2.5' contains coordinates in the 1-ha (100 m \u00d7 100 m) plot, diameter at breast height (dbh, cm) and species identity (see species codes below). Page 'Seed_rain' contains coordinates of subplot in the 1-ha plot and density for four species, for the seed rain stage. Page 'Seed_bank' contains coordinates of subplot in the 1-ha plot and density for four species, for the seed bank stage. Page 'Saplings_2007_2008' contains coordinates of subplot in the 1-ha plot and density for two species at the seedling stage. Species codes in all pages of the Excel Book: CHPE, Chiranthodendron pentadactylon; CODI, Cornus disciflora; ORXA, Orepanax xalapensis; QULA, Quercus laurina; SRS set of remaining tree species. Mej\u00eda-Dom\u00ednguez _et _al_2021_DataBase.xlsx Mej\u00eda-Dom\u00ednguez et al., 2021 Script. R script for calculating neighbourhood index (NI) and estimating neighbourhood models (see Data analysis in article for details). Mej\u00eda-Dom\u00ednguez_et_al_2021_code.R Funding provided by: Consejo Nacional de Ciencia y Tecnolog\u00eda
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100003141
Award Number: Funding provided by: Universidad Nacional Aut\u00f3noma de M\u00e9xico
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100005739
Award Number:"}, {"value": "Please see article in the Journal of Vegetation Science for a detailed description of field and analytical methods."}], "language": {"classid": "und", "classname": "Undetermined", "schemename": "dnet:languages", "schemeid": "dnet:languages"}, "author": [{"surname": "Mej\u00eda-Dom\u00ednguez", "name": "Nancy R.", "pid": [], "rank": 1, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "National Autonomous University of Mexico"}], "fullname": "Mej\u00eda-Dom\u00ednguez, Nancy R."}, {"surname": "Meave", "name": "Jorge A.", "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": 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This includes two components: (i) stochastic parameterizations, to simulate uncertainties in the model (using the algorithm described in Brankart et al., 2015), and (ii) ensemble simulations, with several members running simultaneously in parallel within a single executable (and interacting if needed), as introduced in Bessi\u00e8res et al. (2017). The implementation is kept generic, but the stochastic parameterizations are here focused on uncertainties in the ecosystem component, consistently with the objectives of the SEAMLESS project. The code is based on a previous version by Bessi\u00e8res et al. (2017) based on NEMO3.5 (see zenodo link in the reference). As compared to this previous version, the new features here are: (i) the rewriting of the ensemble simulation code (to adjust to the new NEMO parallelization, which is very different in NEMO4.0 as compared to NEMO3.5), (ii) the inclusion and upgrade of the biogeochemical stochastic parameterizations proposed in Garnier al. (2016), and (iii) the stochastic parameterization of location uncertainties, as developed by Leroux et al. (2022) in NEMO3.6, in the context of the IMMERSE project. See the README file included in the archive for more details. This development has been produced in the framework of the SEAMLESS project (https://www.seamlessproject.org/), to provide a code for stochastic parameterizations to generate ensembles in 1D or 3D MFC configurations (deliverable 3.3). Cite as: Popov M., Brankart J.-M., Molines J.-M., Leroux S., Penduff T., Brasseur P. (2022). Development of a probabilistic ocean modelling system based on NEMO4.0: a contribution to the SEAMLESS project (deliverable 3.3), doi:10.5281/zenodo.6303007"}, {"value": "-Disclaimer- The authors warn that this sources delivery does not imply warranties and support, they assumes no responsability for problems, errors, or incorrect usage of NEMO. Additional information can be found on nemo-ocean.eu website."}, {"value": "{\"references\": [\"Bessi\\u00e8res L., Leroux S., Brankart J.-M., Molines J.-M., Moine M.-P., Bouttier P.-A., Penduff T., Terray L., Barnier B., and S\\u00e9razin G. (2017). Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091-1106, doi:10.5194/gmd-10-1091-2017, Zenodo with the code: https://doi.org/10.5281/zenodo.61611\", \"Brankart J.-M., Candille G., Garnier F., Calone C., Melet A., Bouttier P.-A., Brasseur P. and Verron, J. (2015). A generic approach to explicit simulation of uncertainty in the NEMO ocean model. Geoscientific Model Development, 8, 1285-1297, doi:10.5194/gmd-8-1285-2015\", \"Garnier F., Brankart J.-M., Brasseur P. and Cosme E. (2016). Stochastic parameterizations of biogeochemical uncertainties in a 1/4\\u00b0 NEMO/PISCES model for probabilistic comparisons with ocean color data. Journal of Marine Systems, 155, 59-72, doi:10.1016/j.jmarsys.2015.10.012\", \"Leroux S., Brankart J.-M., Albert A., Brodeau L., Molines J.-M., Jamet Q., Le Sommer J., Penduff T., and Brasseur P. (2022). Ensemble quantification of short-term predictability of the oceandynamics at kilometric-scale resolution: A Western Mediterraneantest-case. Ocean Science. To be submitted.\"]}"}], "language": {"classid": "eng", "classname": "English", "schemename": "dnet:languages", "schemeid": "dnet:languages"}, "author": [{"surname": "Popov", "name": "Mikhail", "pid": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "qualifier": {"classid": "orcid_pending", "classname": "Open Researcher and Contributor ID", "schemename": "dnet:pid_types", "schemeid": "dnet:pid_types"}, "value": "0000-0002-7389-1081"}], "rank": 1, "affiliation": [{"dataInfo": {"invisible": false, "trust": "0.9", "provenanceaction": {"classid": "sysimport:actionset", "classname": "Harvested", "schemename": "dnet:provenanceActions", "schemeid": "dnet:provenanceActions"}, "inferred": false, "deletedbyinference": false}, "value": "IGE/UGA, Grenoble, France"}], "fullname": "Popov, 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Full Changelog: https://github.com/chaoss/augur/compare/v0.28.0...v0.28.1

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3.2.1 -> 3.3.0 feature release

What's Changed

New feature: polling

The user may now create polls.

$poll command creates a simple reaction poll with thumbs up, thumbs down, or unsure. Documentation: https://github.com/CSC510-Group-25/ClassMateBot/blob/group25-documentation/docs/Polling/poll.md

$quizpoll command creates a multi-reaction poll with options A-F. Documentation: https://github.com/CSC510-Group-25/ClassMateBot/blob/group25-documentation/docs/Polling/quizpoll.md

New Contributors

Full Changelog: https://github.com/CSC510-Group-25/ClassMateBot/compare/v3.2.1...3.3.0

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Snakemake workflow to align SARS-CoV-2 paired-end sequencing reads to NCBI reference sequence NC_045512.2.

The workflow preprocesses and maps reads to NC_045512.2 Wuhan seafood market pneumonia virus isolate Wuhan-Hu-1 using BBmap, calls variations with Lofreq and generates consensus fasta sequences with sites with zero coverage masked.

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A package for transforming a NetCDF dataset into a defined format suitable for publication according to a defined publication standard.

Updates to this version

Added new autoparse time methods
Added more functions to compute more variables
Changed cmip6 variable names to reflect the standard names
Only create input files for variables that have definitions
Bug fix for race condition when creating new directories

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This is a ProcessMCRaT release for Zenodo that is identical to v1.0.0 which is the initial released version of ProcessMCRaT.

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First working release meant for researchers to try out Sparkall. For production deployment, contact the owner.

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This release prevents a race condition inside \"recursive()\" strategies. The race condition occurs when the same \"recursive()\" strategy is shared among tests that are running in multiple threads (issue #2717).

The canonical version of these notes (with links) is on readthedocs.

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Fix typos in transcriptions and correct medatdata.

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Diff since v0.2.13

-Custom Downloader/CURL opts to handle slow response and erroneous headers from the HITRAN server

Closed issues:

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This is the artifact for the "Morpheus: Domain Specific Run Time Optimization for Software Data Planes" paper published at ASPLOS'22. 

Morpheus is a system working alongside static compilers that continuously optimizes the targeted networking code. It introduces a number of new techniques, from static code analysis to adaptive code instrumentation, together with a toolbox of domain specific optimizations used to manipulate the code on-the-fly depending on runtime traffic patterns and control plane configurations.

The Morpheus core exploits the LLVM compiler toolchain (v10.0.1) for code manipulation and run-time code generation. It works at the intermediate representation (IR) level as it allows to reason about the running code using a relatively high-level language framework without compromising on code generation time.

This artifact contains the source code, the experimental workflow, and additional information to 1) compile and build Morpheus, 2) install the software dependencies and setup the testbed to run all the experiments, 3) the scripts that can be used to perform some of the experiments presented in the paper, and 4) the scripts to generate the plots based on the obtained results.

For more information, please refer to https://github.com/Morpheus-compiler/Morpheus

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The gCube System - tabular-data-open-widget
--------------------------------------------------

tabular-data-open-widget allows the selection and the opening of tabular resource


This software is part of the gCube Framework (https://www.gcube-system.org/): an
open-source software toolkit used for building and operating Hybrid Data
Infrastructures enabling the dynamic deployment of Virtual Research Environments
by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of
European Union programmes including:
* the Sixth Framework Programme for Research and Technological Development -
DILIGENT (grant no. 004260);
* the Seventh Framework Programme for research, technological development and
demonstration - D4Science (grant no. 212488), D4Science-II (grant no.
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine
(grant no. 283644);
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680),
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant
no. 654119), SoBigData (grant no. 654024);


Version
--------------------------------------------------

2.9.0-4.2.1-125846 (2017-02-12)

Please see the file named \"changelog.xml\" in this directory for the release notes.


Authors
--------------------------------------------------

* Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it),
Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" CNR, Pisa IT


Maintainers
-----------

* Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it),
Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" CNR, Pisa IT


Download information
--------------------------------------------------

Source code is available from SVN:
https://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/user/tabular-data-open-widget

Binaries can be downloaded from the gCube website:
https://www.gcube-system.org/


Installation
--------------------------------------------------

Installation documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager


Documentation
--------------------------------------------------

Documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager


Support
--------------------------------------------------

Bugs and support requests can be reported in the gCube issue tracking tool:
https://support.d4science.org/projects/gcube/


Licensing
--------------------------------------------------

This software is licensed under the terms you may find in the file named \"LICENSE\" in this directory.

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NWelch uses Welch's method to estimate power spectra and magnitude-squared coherence for time series with nonuniform observing cadence. It is designed for Doppler planet searches, but can be applied to any type of univariate or bivariate time series. NWelch is written for <tt>python3</tt> and has been tested mostly on a Mac.

Thanks to @lootie for her contributions to this release.

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Major change is addition of jug pack subcommand.

Full Changelog: https://github.com/luispedro/jug/compare/v2.1.1...v2.2.0

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Full description of the FETCH3 can be found in the paper \"Tree Hydrodynamic Modelling of Soil Plant Atmosphere Continuum using FETCH3\" in the journal Geoscientific Model Development. This version includes the code for the NHL transpiration submodule as an example of a formulation that resolve the interactions between radiation and atmospheric conditions within the canopy and different storage and stomata restrictions at different vertical levels. Since this was tested in the previous FETCH publications it was not the goal to test it this study, but it is part of the model. The modular version was developed by Justine E. Missik, co-author of the publication. 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Test of integration and manual release tagging.

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--------------------------------------------------

tabular-data-sdmx-import-widget allows sdmx import of codelists and dataset


This software is part of the gCube Framework (https://www.gcube-system.org/): an
open-source software toolkit used for building and operating Hybrid Data
Infrastructures enabling the dynamic deployment of Virtual Research Environments
by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of
European Union programmes including:
* the Sixth Framework Programme for Research and Technological Development -
DILIGENT (grant no. 004260);
* the Seventh Framework Programme for research, technological development and
demonstration - D4Science (grant no. 212488), D4Science-II (grant no.
239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine
(grant no. 283644);
* the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680),
EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant
no. 654119), SoBigData (grant no. 654024);


Version
--------------------------------------------------

2.8.0-4.7.1-142639 (2017-10-19)

Please see the file named \"changelog.xml\" in this directory for the release notes.


Authors
--------------------------------------------------

* Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it),
Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" CNR, Pisa IT


Maintainers
-----------

* Giancarlo Panichi (giancarlo.panichi-AT-isti.cnr.it),
Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\" CNR, Pisa IT


Download information
--------------------------------------------------

Source code is available from SVN:
https://svn.research-infrastructures.eu/public/d4science/gcube/trunk/portlets/user/tabular-data-sdmx-import-widget

Binaries can be downloaded from the gCube website:
https://www.gcube-system.org/


Installation
--------------------------------------------------

Installation documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager


Documentation
--------------------------------------------------

Documentation is available on-line in the gCube Wiki:
https://gcube.wiki.gcube-system.org/gcube/index.php/Tabular_Data_Manager


Support
--------------------------------------------------

Bugs and support requests can be reported in the gCube issue tracking tool:
https://support.d4science.org/projects/gcube/


Licensing
--------------------------------------------------

This software is licensed under the terms you may find in the file named \"LICENSE\" in this directory.
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This release represents over 15 months of hard work on PySAL. It brings major transitions in the project together with many new enhancements, improved documentation, along with many bug fixes and optimizations.

<!-- markdown-toc start - Don't edit this section. Run M-x markdown-toc-generate-toc again --> **Table of Contents** - [Release Notes for PySAL 2.0](#release-notes-for-pysal-20) - [Major Changes](#major-changes) - [Entirely New Packages](#entirely-new-packages) - [Significant Enhancements to Existing Packages](#significant-enhancements-to-existing-packages) - [Changes by Package](#changes-by-package) - [libpysal](#libpysal) - [esda](#esda) - [giddy](#giddy) - [inequality](#inequality) - [pointpats](#pointpats) - [spaghetti](#spaghetti) - [mapclassify](#mapclassify) - [splot](#splot) - [spreg](#spreg) - [spglm](#spglm) - [spint](#spint) - [mgwr](#mgwr) - [spvcm](#spvcm) - [Summary Statistics](#summary-statistics) - [Package Activity](#package-activity) - [Contributor Activity](#contributor-activity) - [Contributor by Package Activity](#contributor-by-package-activity) <!-- markdown-toc end -->

<a name=\"major-changes\"></a>

Major Changes

This release, we've had a ton of activity in PySAL, but it has all been conducted in our subpackages, the independently-released components of our library. Because it is too onerous to list all the changes to the API here, please consult the Migrating to PySAL 2.0 page for a detailed discussion of all reorganization-related changes. This document will focus only on improvements to functionality, enhancements, and additional modules added to 2.0, over and above the last stable release of the 1.0 series, PySAL 1.14.4.

<a name=\"entirely-new-packages\"></a>

Entirely New Packages

For starters, we've added some entirely-new subpackages to this release candidate:

<a name=\"significant-enhancements-to-existing-packages\"></a>

Significant Enhancements to Existing Packages

We've also had a ton of activity adding new features in our submodules:

<a name=\"changes-by-package\"></a>

Changes by Package

Overall, there were 1636 commits that closed 368 issues, together with 236 pull requests since our last release on 2017-11-03.

<a name=\"libpysal\"></a>

libpysal

<a name=\"esda\"></a>

esda

<a name=\"giddy\"></a>

giddy