From a21fe310e57c5463eafb50205e1b35ab29759cf5 Mon Sep 17 00:00:00 2001 From: "miriam.baglioni" Date: Mon, 2 May 2022 17:43:20 +0200 Subject: [PATCH] [EOSCTag] last test and change in the implementation to search in title and descriptio --- .../doiboost/crossref/Crossref2Oaf.scala | 6 +- .../crossref/CrossrefMappingTest.scala | 4 +- .../eu/dnetlib/dhp/bulktag/SparkEoscTag.java | 355 ++++++------ .../dnetlib/dhp/bulktag/EOSCTagJobTest.java | 530 ++++++++++++++---- .../eosctag/twitter/dataset/dataset_10.json | 4 +- .../otherresearchproduct_10.json | 10 +- .../eosctag/twitter/software/software_10.json | 2 +- 7 files changed, 620 insertions(+), 291 deletions(-) diff --git a/dhp-workflows/dhp-doiboost/src/main/scala/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala b/dhp-workflows/dhp-doiboost/src/main/scala/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala index 0ba77ca84..f2d1dde3d 100644 --- a/dhp-workflows/dhp-doiboost/src/main/scala/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala +++ b/dhp-workflows/dhp-doiboost/src/main/scala/eu/dnetlib/doiboost/crossref/Crossref2Oaf.scala @@ -584,12 +584,10 @@ case object Crossref2Oaf { if (dp.length == 10) { return GraphCleaningFunctions.cleanDate(dp) } - } - else if (res.size ==2) { + } else if (res.size == 2) { val dp = f"${res.head}-${res(1)}%02d-01" return GraphCleaningFunctions.cleanDate(dp) - } - else if (res.size ==1) { + } else if (res.size == 1) { return GraphCleaningFunctions.cleanDate(s"${res.head}-01-01") } } diff --git a/dhp-workflows/dhp-doiboost/src/test/scala/eu/dnetlib/dhp/doiboost/crossref/CrossrefMappingTest.scala b/dhp-workflows/dhp-doiboost/src/test/scala/eu/dnetlib/dhp/doiboost/crossref/CrossrefMappingTest.scala index 9025cd6d2..78ed375c6 100644 --- a/dhp-workflows/dhp-doiboost/src/test/scala/eu/dnetlib/dhp/doiboost/crossref/CrossrefMappingTest.scala +++ b/dhp-workflows/dhp-doiboost/src/test/scala/eu/dnetlib/dhp/doiboost/crossref/CrossrefMappingTest.scala @@ -73,10 +73,10 @@ class CrossrefMappingTest { } - @Test def crossrefIssueDateTest(): Unit = { - val json = Source.fromInputStream(getClass.getResourceAsStream("/eu/dnetlib/doiboost/crossref/issue_date.json")).mkString + val json = + Source.fromInputStream(getClass.getResourceAsStream("/eu/dnetlib/doiboost/crossref/issue_date.json")).mkString assertNotNull(json) assertFalse(json.isEmpty) val resultList: List[Oaf] = Crossref2Oaf.convert(json) diff --git a/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java b/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java index 16baf70a6..1d0aaa631 100644 --- a/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java +++ b/dhp-workflows/dhp-enrichment/src/main/java/eu/dnetlib/dhp/bulktag/SparkEoscTag.java @@ -1,10 +1,14 @@ + package eu.dnetlib.dhp.bulktag; -import com.fasterxml.jackson.databind.ObjectMapper; -import eu.dnetlib.dhp.application.ArgumentApplicationParser; -import eu.dnetlib.dhp.schema.common.ModelConstants; -import eu.dnetlib.dhp.schema.oaf.*; -import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils; +import static eu.dnetlib.dhp.PropagationConstant.readPath; +import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession; + +import java.util.*; +import java.util.function.Function; +import java.util.stream.Collectors; +import java.util.stream.Stream; + import org.apache.commons.io.IOUtils; import org.apache.spark.SparkConf; import org.apache.spark.api.java.function.MapFunction; @@ -14,185 +18,214 @@ import org.apache.spark.sql.SparkSession; import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; -import java.util.Optional; +import com.fasterxml.jackson.databind.ObjectMapper; -import static eu.dnetlib.dhp.PropagationConstant.readPath; -import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession; +import eu.dnetlib.dhp.application.ArgumentApplicationParser; +import eu.dnetlib.dhp.schema.common.ModelConstants; +import eu.dnetlib.dhp.schema.oaf.*; +import eu.dnetlib.dhp.schema.oaf.utils.OafMapperUtils; public class SparkEoscTag { - private static final Logger log = LoggerFactory.getLogger(SparkEoscTag.class); - public static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); - public static final Qualifier EOSC_QUALIFIER = OafMapperUtils.qualifier("eosc", - "European Open Science Cloud", - ModelConstants.DNET_SUBJECT_TYPOLOGIES,ModelConstants.DNET_SUBJECT_TYPOLOGIES); - public static final DataInfo EOSC_DATAINFO = OafMapperUtils.dataInfo(false, "propagation", true, false, - OafMapperUtils.qualifier("propagation:subject","Inferred by OpenAIRE", - ModelConstants.DNET_PROVENANCE_ACTIONS,ModelConstants.DNET_PROVENANCE_ACTIONS), "0.9"); - public final static StructuredProperty EOSC_NOTEBOOK = OafMapperUtils.structuredProperty( - "EOSC::Jupyter Notebook", EOSC_QUALIFIER,EOSC_DATAINFO); - public final static StructuredProperty EOSC_GALAXY = OafMapperUtils.structuredProperty( - "EOSC::Galaxy Workflow", EOSC_QUALIFIER, EOSC_DATAINFO); - public final static StructuredProperty EOSC_TWITTER = OafMapperUtils.structuredProperty( - "EOSC::Twitter Data", EOSC_QUALIFIER,EOSC_DATAINFO); + private static final Logger log = LoggerFactory.getLogger(SparkEoscTag.class); + public static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); + public static final Qualifier EOSC_QUALIFIER = OafMapperUtils + .qualifier( + "eosc", + "European Open Science Cloud", + ModelConstants.DNET_SUBJECT_TYPOLOGIES, ModelConstants.DNET_SUBJECT_TYPOLOGIES); + public static final DataInfo EOSC_DATAINFO = OafMapperUtils + .dataInfo( + false, "propagation", true, false, + OafMapperUtils + .qualifier( + "propagation:subject", "Inferred by OpenAIRE", + ModelConstants.DNET_PROVENANCE_ACTIONS, ModelConstants.DNET_PROVENANCE_ACTIONS), + "0.9"); + public final static StructuredProperty EOSC_NOTEBOOK = OafMapperUtils + .structuredProperty( + "EOSC::Jupyter Notebook", EOSC_QUALIFIER, EOSC_DATAINFO); + public final static StructuredProperty EOSC_GALAXY = OafMapperUtils + .structuredProperty( + "EOSC::Galaxy Workflow", EOSC_QUALIFIER, EOSC_DATAINFO); + public final static StructuredProperty EOSC_TWITTER = OafMapperUtils + .structuredProperty( + "EOSC::Twitter Data", EOSC_QUALIFIER, EOSC_DATAINFO); - public static void main(String[] args) throws Exception { - String jsonConfiguration = IOUtils - .toString( - SparkEoscTag.class - .getResourceAsStream( - "/eu/dnetlib/dhp/bulktag/input_eoscTag_parameters.json")); + public static void main(String[] args) throws Exception { + String jsonConfiguration = IOUtils + .toString( + SparkEoscTag.class + .getResourceAsStream( + "/eu/dnetlib/dhp/bulktag/input_eoscTag_parameters.json")); - final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration); - parser.parseArgument(args); + final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration); + parser.parseArgument(args); - Boolean isSparkSessionManaged = Optional - .ofNullable(parser.get("isSparkSessionManaged")) - .map(Boolean::valueOf) - .orElse(Boolean.TRUE); - log.info("isSparkSessionManaged: {}", isSparkSessionManaged); + Boolean isSparkSessionManaged = Optional + .ofNullable(parser.get("isSparkSessionManaged")) + .map(Boolean::valueOf) + .orElse(Boolean.TRUE); + log.info("isSparkSessionManaged: {}", isSparkSessionManaged); - final String inputPath = parser.get("sourcePath"); - log.info("inputPath: {}", inputPath); + final String inputPath = parser.get("sourcePath"); + log.info("inputPath: {}", inputPath); - final String workingPath = parser.get("workingPath"); - log.info("workingPath: {}", workingPath); + final String workingPath = parser.get("workingPath"); + log.info("workingPath: {}", workingPath); - SparkConf conf = new SparkConf(); + SparkConf conf = new SparkConf(); - runWithSparkSession( - conf, - isSparkSessionManaged, - spark -> { - execEoscTag(spark, inputPath, workingPath); + runWithSparkSession( + conf, + isSparkSessionManaged, + spark -> { + execEoscTag(spark, inputPath, workingPath); - }); - } + }); + } - private static void execEoscTag(SparkSession spark, String inputPath, String workingPath) { + private static void execEoscTag(SparkSession spark, String inputPath, String workingPath) { - readPath(spark, inputPath + "/software", Software.class) - .map((MapFunction) s -> { - List sbject; - if (!Optional.ofNullable(s.getSubject()).isPresent()) - s.setSubject(new ArrayList<>()); - sbject = s.getSubject(); + readPath(spark, inputPath + "/software", Software.class) + .map((MapFunction) s -> { + List sbject; + if (!Optional.ofNullable(s.getSubject()).isPresent()) + s.setSubject(new ArrayList<>()); + sbject = s.getSubject(); - if(containsCriteriaNotebook(s)){ - sbject.add(EOSC_NOTEBOOK); + if (containsCriteriaNotebook(s)) { + sbject.add(EOSC_NOTEBOOK); - } - if(containsCriteriaGalaxy(s)){ - sbject.add(EOSC_GALAXY); - } - return s; - }, Encoders.bean(Software.class) ) - .write() - .mode(SaveMode.Overwrite) - .option("compression","gzip") - .json(workingPath + "/software"); + } + if (containsCriteriaGalaxy(s)) { + sbject.add(EOSC_GALAXY); + } + return s; + }, Encoders.bean(Software.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingPath + "/software"); - readPath(spark, workingPath + "/software" , Software.class) - .write() - .mode(SaveMode.Overwrite) - .option("compression","gzip") - .json(inputPath + "/software"); + readPath(spark, workingPath + "/software", Software.class) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(inputPath + "/software"); - readPath(spark, inputPath + "/otherresearchproduct", OtherResearchProduct.class) - .map((MapFunction) orp -> - { - List sbject; - if (!Optional.ofNullable(orp.getSubject()).isPresent()) - orp.setSubject(new ArrayList<>()); - sbject = orp.getSubject(); - if(containsCriteriaGalaxy(orp)){ - sbject.add(EOSC_GALAXY); - } - if(containscriteriaTwitter(orp)){ - sbject.add(EOSC_TWITTER); - } - return orp; - }, Encoders.bean(OtherResearchProduct.class)) - .write() - .mode(SaveMode.Overwrite) - .option("compression","gzip") - .json(workingPath + "/otherresearchproduct"); + readPath(spark, inputPath + "/otherresearchproduct", OtherResearchProduct.class) + .map((MapFunction) orp -> { + List sbject; + if (!Optional.ofNullable(orp.getSubject()).isPresent()) + orp.setSubject(new ArrayList<>()); + sbject = orp.getSubject(); + if (containsCriteriaGalaxy(orp)) { + sbject.add(EOSC_GALAXY); + } + if (containscriteriaTwitter(orp)) { + sbject.add(EOSC_TWITTER); + } + return orp; + }, Encoders.bean(OtherResearchProduct.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingPath + "/otherresearchproduct"); - readPath(spark, workingPath + "/otherresearchproduct", OtherResearchProduct.class) - .write() - .mode(SaveMode.Overwrite) - .option("compression","gzip") - .json(inputPath + "/otherresearchproduct"); + readPath(spark, workingPath + "/otherresearchproduct", OtherResearchProduct.class) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(inputPath + "/otherresearchproduct"); - readPath(spark, inputPath + "/dataset", Dataset.class) - .map((MapFunction) d -> { - List sbject; - if (!Optional.ofNullable(d.getSubject()).isPresent()) - d.setSubject(new ArrayList<>()); - sbject = d.getSubject(); - if(containscriteriaTwitter(d)){ - sbject.add(EOSC_TWITTER); - } - return d; - } , Encoders.bean(Dataset.class) ) - .write() - .mode(SaveMode.Overwrite) - .option("compression","gzip") - .json(workingPath + "/dataset"); + readPath(spark, inputPath + "/dataset", Dataset.class) + .map((MapFunction) d -> { + List sbject; + if (!Optional.ofNullable(d.getSubject()).isPresent()) + d.setSubject(new ArrayList<>()); + sbject = d.getSubject(); + if (containscriteriaTwitter(d)) { + sbject.add(EOSC_TWITTER); + } + return d; + }, Encoders.bean(Dataset.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingPath + "/dataset"); - readPath(spark, workingPath + "/dataset" , Dataset.class) - .write() - .mode(SaveMode.Overwrite) - .option("compression","gzip") - .json(inputPath + "/dataset"); - } + readPath(spark, workingPath + "/dataset", Dataset.class) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(inputPath + "/dataset"); + } - private static boolean containscriteriaTwitter(Result r) { - if (r.getTitle().stream().anyMatch(t -> t.getValue().toLowerCase().contains("twitter") && - t.getValue().toLowerCase().contains("data"))) - return true; - if(r.getDescription().stream().anyMatch(d -> d.getValue().toLowerCase().contains("twitter") && - d.getValue().toLowerCase().contains("data") )) - return true; - if(r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("twitter")) && - r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("data"))) - return true; - return false; - } + private static boolean containscriteriaTwitter(Result r) { + Set words = getWordsSP(r.getTitle()); + words.addAll(getWordsF(r.getDescription())); - private static boolean containsCriteriaGalaxy(Result r) { - if (r.getTitle().stream().anyMatch(t -> t.getValue().toLowerCase().contains("galaxy") && - (t.getValue().toLowerCase().contains("workflow") || t.getValue().toLowerCase().contains("software")))) - return true; - if(r.getDescription().stream().anyMatch(d -> d.getValue().toLowerCase().contains("galaxy") && - (d.getValue().toLowerCase().contains("workflow") || d.getValue().toLowerCase().contains("software")))) - return true; - if(r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("galaxy")) && - (r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("workflow"))) || - r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("software"))) - return true; - return false; - } + if (words.contains("twitter") && + (words.contains("data") || words.contains("dataset"))) + return true; - private static boolean containsCriteriaNotebook(Software s) { - if(s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("jupyter"))) - return true; - if(s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("python") && - sbj.getValue().toLowerCase().contains("notebook"))) - return true; - if(s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("python")) && - s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("notebook"))) - return true; - return false; - } + if (r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("twitter")) && + r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("data"))) + return true; + return false; + } - private static boolean containsTitleNotebook(Software s) { - if (s.getTitle().stream().anyMatch(t -> t.getValue().toLowerCase().contains("jupyter") && - t.getValue().toLowerCase().contains("notebook"))) - return true; - return false; - } + private static boolean containsCriteriaGalaxy(Result r) { + Set words = getWordsSP(r.getTitle()); + words.addAll(getWordsF(r.getDescription())); + if (words.contains("galaxy") && + (words.contains("workflow") || words.contains("software"))) + return true; + if (r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("galaxy")) && + (r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("workflow"))) || + r.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("software"))) + return true; + return false; + } + + private static boolean containsCriteriaNotebook(Software s) { + if (s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("jupyter"))) + return true; + if (s + .getSubject() + .stream() + .anyMatch( + sbj -> sbj.getValue().toLowerCase().contains("python") && + sbj.getValue().toLowerCase().contains("notebook"))) + return true; + if (s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("python")) && + s.getSubject().stream().anyMatch(sbj -> sbj.getValue().toLowerCase().contains("notebook"))) + return true; + return false; + } + + private static Set getSubjects(List s) { + Set subjects = new HashSet<>(); + s.stream().forEach(sbj -> subjects.addAll(Arrays.asList(sbj.getValue().toLowerCase().split(" ")))); + s.stream().forEach(sbj -> subjects.add(sbj.getValue().toLowerCase())); + return subjects; + } + + private static Set getWordsSP(List elem) { + Set words = new HashSet<>(); + elem + .forEach( + t -> words.addAll(Arrays.asList(t.getValue().toLowerCase().replaceAll("[^a-zA-Z ]", "").split(" ")))); + return words; + } + + private static Set getWordsF(List> elem) { + Set words = new HashSet<>(); + elem + .forEach( + t -> words.addAll(Arrays.asList(t.getValue().toLowerCase().replaceAll("[^a-zA-Z ]", "").split(" ")))); + return words; + + } } diff --git a/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java b/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java index 2c2334a6c..d96dc7f6c 100644 --- a/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java +++ b/dhp-workflows/dhp-enrichment/src/test/java/eu/dnetlib/dhp/bulktag/EOSCTagJobTest.java @@ -1,8 +1,13 @@ package eu.dnetlib.dhp.bulktag; -import com.fasterxml.jackson.databind.ObjectMapper; -import eu.dnetlib.dhp.schema.oaf.*; +import static eu.dnetlib.dhp.bulktag.community.TaggingConstants.ZENODO_COMMUNITY_INDICATOR; + +import java.io.IOException; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.List; + import org.apache.commons.io.FileUtils; import org.apache.commons.io.IOUtils; import org.apache.spark.SparkConf; @@ -11,6 +16,7 @@ import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.MapFunction; import org.apache.spark.sql.Encoders; import org.apache.spark.sql.Row; +import org.apache.spark.sql.SaveMode; import org.apache.spark.sql.SparkSession; import org.junit.jupiter.api.AfterAll; import org.junit.jupiter.api.Assertions; @@ -19,26 +25,20 @@ import org.junit.jupiter.api.Test; import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import java.io.IOException; -import java.nio.file.Files; -import java.nio.file.Path; -import java.util.List; +import com.fasterxml.jackson.databind.ObjectMapper; -import static eu.dnetlib.dhp.bulktag.community.TaggingConstants.ZENODO_COMMUNITY_INDICATOR; +import eu.dnetlib.dhp.schema.oaf.*; public class EOSCTagJobTest { private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper(); - - private static SparkSession spark; private static Path workingDir; private static final Logger log = LoggerFactory.getLogger(EOSCTagJobTest.class); - @BeforeAll public static void beforeAll() throws IOException { workingDir = Files.createTempDirectory(EOSCTagJobTest.class.getSimpleName()); @@ -70,23 +70,39 @@ public class EOSCTagJobTest { @Test void jupyterUpdatesTest() throws Exception { - spark.read().textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/jupyter/software").getPath()) - .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, Software.class), Encoders.bean(Software.class)) - .write() - .option("compression","gzip") - .json(workingDir.toString() + "/input/software"); + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/jupyter/software").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER.readValue(value, Software.class), + Encoders.bean(Software.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/software"); - spark.read().textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/jupyter/dataset").getPath()) - .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, Dataset.class), Encoders.bean(Dataset.class)) - .write() - .option("compression","gzip") - .json(workingDir.toString() + "/input/dataset"); + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/jupyter/dataset").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER.readValue(value, Dataset.class), + Encoders.bean(Dataset.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/dataset"); - spark.read().textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/jupyter/otherresearchproduct").getPath()) - .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, OtherResearchProduct.class), Encoders.bean(OtherResearchProduct.class)) - .write() - .option("compression","gzip") - .json(workingDir.toString() + "/input/otherresearchproduct"); + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/jupyter/otherresearchproduct").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER + .readValue(value, OtherResearchProduct.class), + Encoders.bean(OtherResearchProduct.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/otherresearchproduct"); SparkEoscTag .main( @@ -106,36 +122,109 @@ public class EOSCTagJobTest { Assertions.assertEquals(10, tmp.count()); - Assertions.assertEquals(4, tmp.filter(s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Jupyter Notebook"))).count()); + Assertions + .assertEquals( + 4, + tmp + .filter( + s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Jupyter Notebook"))) + .count()); - Assertions.assertEquals(2, tmp.filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(tmp.filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); + Assertions + .assertEquals( + 2, tmp + .filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); + Assertions + .assertEquals( + 5, tmp + .filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertFalse( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); - Assertions.assertEquals(5, tmp.filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")).collect() - .get(0).getSubject().size()); - Assertions.assertFalse(tmp.filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); + Assertions + .assertEquals( + 9, tmp + .filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); - Assertions.assertEquals(9, tmp.filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(tmp.filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); + Assertions + .assertEquals( + 5, tmp + .filter(sw -> sw.getId().equals("50|od______1582::5aec1186054301b66c0c5dc35972a589")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertFalse( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::5aec1186054301b66c0c5dc35972a589")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); - Assertions.assertEquals(5, tmp.filter(sw -> sw.getId().equals("50|od______1582::5aec1186054301b66c0c5dc35972a589")).collect() - .get(0).getSubject().size()); - Assertions.assertFalse(tmp.filter(sw -> sw.getId().equals("50|od______1582::5aec1186054301b66c0c5dc35972a589")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); + Assertions + .assertEquals( + 9, tmp + .filter(sw -> sw.getId().equals("50|od______1582::639909adfad9d708308f2aedb733e4a0")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::639909adfad9d708308f2aedb733e4a0")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); - Assertions.assertEquals(9, tmp.filter(sw -> sw.getId().equals("50|od______1582::639909adfad9d708308f2aedb733e4a0")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(tmp.filter(sw -> sw.getId().equals("50|od______1582::639909adfad9d708308f2aedb733e4a0")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); - - List subjects = tmp.filter(sw -> sw.getId().equals("50|od______1582::6e7a9b21a2feef45673890432af34244")).collect() - .get(0).getSubject(); + List subjects = tmp + .filter(sw -> sw.getId().equals("50|od______1582::6e7a9b21a2feef45673890432af34244")) + .collect() + .get(0) + .getSubject(); Assertions.assertEquals(8, subjects.size()); Assertions.assertTrue(subjects.stream().anyMatch(s -> s.getValue().equals("EOSC::Jupyter Notebook"))); Assertions.assertTrue(subjects.stream().anyMatch(s -> s.getValue().equals("jupyter"))); @@ -146,104 +235,313 @@ public class EOSCTagJobTest { Assertions.assertTrue(subjects.stream().anyMatch(s -> s.getValue().equals("flux de gaz"))); Assertions.assertTrue(subjects.stream().anyMatch(s -> s.getValue().equals("flux de liquide"))); + Assertions + .assertEquals( + 10, sc + .textFile(workingDir.toString() + "/input/dataset") + .map(item -> OBJECT_MAPPER.readValue(item, Dataset.class)) + .count()); - Assertions.assertEquals(10, sc - .textFile(workingDir.toString() + "/input/dataset") - .map(item -> OBJECT_MAPPER.readValue(item, Dataset.class)).count()); + Assertions + .assertEquals( + 0, sc + .textFile(workingDir.toString() + "/input/dataset") + .map(item -> OBJECT_MAPPER.readValue(item, Dataset.class)) + .filter( + ds -> ds.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Jupyter Notebook"))) + .count()); - Assertions.assertEquals(0, sc - .textFile(workingDir.toString() + "/input/dataset") - .map(item -> OBJECT_MAPPER.readValue(item, Dataset.class)).filter(ds -> ds.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Jupyter Notebook"))).count()); + Assertions + .assertEquals( + 10, sc + .textFile(workingDir.toString() + "/input/otherresearchproduct") + .map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)) + .count()); + Assertions + .assertEquals( + 0, sc + .textFile(workingDir.toString() + "/input/otherresearchproduct") + .map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)) + .filter( + ds -> ds.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Jupyter Notebook"))) + .count()); - Assertions.assertEquals(10, sc - .textFile(workingDir.toString() + "/input/otherresearchproduct") - .map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)).count()); - - Assertions.assertEquals(0, sc - .textFile(workingDir.toString() + "/input/otherresearchproduct") - .map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)).filter(ds -> ds.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Jupyter Notebook"))).count()); + // spark.stop(); } - @Test void galaxyUpdatesTest() throws Exception { - spark.read().textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/galaxy/software").getPath()) - .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, Software.class), Encoders.bean(Software.class)) - .write() - .option("compression","gzip") - .json(workingDir.toString() + "/input/software"); + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/galaxy/software").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER.readValue(value, Software.class), + Encoders.bean(Software.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/software"); - spark.read().textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/galaxy/dataset").getPath()) - .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, Dataset.class), Encoders.bean(Dataset.class)) - .write() - .option("compression","gzip") - .json(workingDir.toString() + "/input/dataset"); + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/galaxy/dataset").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER.readValue(value, Dataset.class), + Encoders.bean(Dataset.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/dataset"); - spark.read().textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/galaxy/otherresearchproduct").getPath()) - .map((MapFunction) value -> OBJECT_MAPPER.readValue(value, OtherResearchProduct.class), Encoders.bean(OtherResearchProduct.class)) - .write() - .option("compression","gzip") - .json(workingDir.toString() + "/input/otherresearchproduct"); + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/galaxy/otherresearchproduct").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER + .readValue(value, OtherResearchProduct.class), + Encoders.bean(OtherResearchProduct.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/otherresearchproduct"); SparkEoscTag - .main( - new String[] { - "-isSparkSessionManaged", Boolean.FALSE.toString(), - "-sourcePath", - workingDir.toString() + "/input", - "-workingPath", workingDir.toString() + "/working" + .main( + new String[] { + "-isSparkSessionManaged", Boolean.FALSE.toString(), + "-sourcePath", + workingDir.toString() + "/input", + "-workingPath", workingDir.toString() + "/working" - }); + }); final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext()); JavaRDD tmp = sc - .textFile(workingDir.toString() + "/input/software") - .map(item -> OBJECT_MAPPER.readValue(item, Software.class)); + .textFile(workingDir.toString() + "/input/software") + .map(item -> OBJECT_MAPPER.readValue(item, Software.class)); Assertions.assertEquals(10, tmp.count()); - Assertions.assertEquals(2, tmp.filter(s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Galaxy Workflow"))).count()); + Assertions + .assertEquals( + 2, + tmp + .filter( + s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Galaxy Workflow"))) + .count()); - Assertions.assertEquals(2, tmp.filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(tmp.filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); + Assertions + .assertEquals( + 2, tmp + .filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::4132f5ec9496f0d6adc7b00a50a56ff4")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); + Assertions + .assertEquals( + 6, tmp + .filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); - Assertions.assertEquals(6, tmp.filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(tmp.filter(sw -> sw.getId().equals("50|od______1582::501b25d420f808c8eddcd9b16e917f11")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); + Assertions + .assertEquals( + 8, tmp + .filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertFalse( + tmp + .filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); - Assertions.assertEquals(8, tmp.filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")).collect() - .get(0).getSubject().size()); - Assertions.assertFalse(tmp.filter(sw -> sw.getId().equals("50|od______1582::581621232a561b7e8b4952b18b8b0e56")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); - - JavaRDD orp = sc.textFile(workingDir.toString() + "/input/otherresearchproduct").map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)); + JavaRDD orp = sc + .textFile(workingDir.toString() + "/input/otherresearchproduct") + .map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)); Assertions.assertEquals(10, orp.count()); - Assertions.assertEquals(2, orp.filter(s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Galaxy Workflow"))).count()); + Assertions + .assertEquals( + 2, + orp + .filter( + s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Galaxy Workflow"))) + .count()); + Assertions + .assertEquals( + 3, orp + .filter(sw -> sw.getId().equals("50|od______2017::0750a4d0782265873d669520f5e33c07")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + orp + .filter(sw -> sw.getId().equals("50|od______2017::0750a4d0782265873d669520f5e33c07")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); - Assertions.assertEquals(3, orp.filter(sw -> sw.getId().equals("50|od______2017::0750a4d0782265873d669520f5e33c07")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(orp.filter(sw -> sw.getId().equals("50|od______2017::0750a4d0782265873d669520f5e33c07")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); + Assertions + .assertEquals( + 2, orp + .filter(sw -> sw.getId().equals("50|od______2017::1bd97baef19dbd2db3203b112bb83bc5")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertFalse( + orp + .filter(sw -> sw.getId().equals("50|od______2017::1bd97baef19dbd2db3203b112bb83bc5")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); - Assertions.assertEquals(2, orp.filter(sw -> sw.getId().equals("50|od______2017::1bd97baef19dbd2db3203b112bb83bc5")).collect() - .get(0).getSubject().size()); - Assertions.assertFalse(orp.filter(sw -> sw.getId().equals("50|od______2017::1bd97baef19dbd2db3203b112bb83bc5")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); - - Assertions.assertEquals(3, orp.filter(sw -> sw.getId().equals("50|od______2017::1e400f1747487fd15998735c41a55c72")).collect() - .get(0).getSubject().size()); - Assertions.assertTrue(orp.filter(sw -> sw.getId().equals("50|od______2017::1e400f1747487fd15998735c41a55c72")).collect() - .get(0).getSubject().stream().anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); + Assertions + .assertEquals( + 3, orp + .filter(sw -> sw.getId().equals("50|od______2017::1e400f1747487fd15998735c41a55c72")) + .collect() + .get(0) + .getSubject() + .size()); + Assertions + .assertTrue( + orp + .filter(sw -> sw.getId().equals("50|od______2017::1e400f1747487fd15998735c41a55c72")) + .collect() + .get(0) + .getSubject() + .stream() + .anyMatch(s -> s.getValue().equals("EOSC::Galaxy Workflow"))); } + @Test + void twitterUpdatesTest() throws Exception { + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/twitter/software").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER.readValue(value, Software.class), + Encoders.bean(Software.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/software"); + + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/twitter/dataset").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER.readValue(value, Dataset.class), + Encoders.bean(Dataset.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/dataset"); + + spark + .read() + .textFile(getClass().getResource("/eu/dnetlib/dhp/eosctag/twitter/otherresearchproduct").getPath()) + .map( + (MapFunction) value -> OBJECT_MAPPER + .readValue(value, OtherResearchProduct.class), + Encoders.bean(OtherResearchProduct.class)) + .write() + .mode(SaveMode.Overwrite) + .option("compression", "gzip") + .json(workingDir.toString() + "/input/otherresearchproduct"); + + SparkEoscTag + .main( + new String[] { + "-isSparkSessionManaged", Boolean.FALSE.toString(), + "-sourcePath", + workingDir.toString() + "/input", + "-workingPath", workingDir.toString() + "/working" + + }); + + final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext()); + + JavaRDD tmp = sc + .textFile(workingDir.toString() + "/input/software") + .map(item -> OBJECT_MAPPER.readValue(item, Software.class)); + + Assertions.assertEquals(10, tmp.count()); + + Assertions + .assertEquals( + 0, + tmp + .filter(s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Twitter Data"))) + .count()); + + JavaRDD orp = sc + .textFile(workingDir.toString() + "/input/otherresearchproduct") + .map(item -> OBJECT_MAPPER.readValue(item, OtherResearchProduct.class)); + + Assertions.assertEquals(10, orp.count()); + + Assertions + .assertEquals( + 3, + orp + .filter(s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Twitter Data"))) + .count()); + + JavaRDD dats = sc + .textFile(workingDir.toString() + "/input/dataset") + .map(item -> OBJECT_MAPPER.readValue(item, Dataset.class)); + + Assertions.assertEquals(10, dats.count()); + + Assertions + .assertEquals( + 2, + dats + .filter(s -> s.getSubject().stream().anyMatch(sbj -> sbj.getValue().equals("EOSC::Twitter Data"))) + .count()); + + } } diff --git a/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/twitter/dataset/dataset_10.json b/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/twitter/dataset/dataset_10.json index 0243a4e3d..b441bc423 100644 --- a/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/twitter/dataset/dataset_10.json +++ b/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/twitter/dataset/dataset_10.json @@ -1,5 +1,5 @@ -{"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}},"lastupdatetimestamp":1585055868909,"id":"50|od______3989::02dd5d2c222191b0b9bd4f33c8e96529","originalId":["od______3989::02dd5d2c222191b0b9bd4f33c8e96529"],"collectedfrom":[{"key":"opendoar____::3989","value":"Depósito Digital 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Fisiología del Ejercicio","qualifier":{"classid":"","classname":"","schemeid":"","schemename":""},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"title":[{"value":"Investigación - Efectos positivos y negativos del uso de la cafeína en el deporte","qualifier":{"classid":"main title","classname":"main title","schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"relevantdate":[],"description":[{"value":"Proyecto de investigación que se están desarrollando en la Universidad Camilo José Cela (UCJC) sobre la cafeína y sus efectos cuando se realiza deporte. https://www.ucjc.edu/2016/08/la-bbc-s...\n\nJuan Del Coso, director del Laboratorio de Fisiología del Ejercicio de la Universidad Camilo José Cela, nos describe cómo está siendo la investigación que desarrolla con deportistas de alto rendimiento y el consumo de cafeína.\n\n\"Nuestra investigación se enmarca dentro del campo de la fisiología del ejercicio y el objetivo que tenemos es investigar los efectos que tiene la cafeína en los deportistas, principalmente saber si es una ayuda ergogénica en la mayor parte de los deportes, pero también conocer qué perjuicios existen para aquellos deportistas que la utilizan para incrementar el rendimiento deportivo\", explica Del Coso.\n\nY es que los datos son muy llamativos: tres de cada cuatro deportistas de alto rendimiento utilizan la cafeína antes de la competición y muchas veces se utiliza sin saber los perjuicios que puede tener para el deportista. \"Lo que estamos investigando es cómo una sustancia, que te puede hacer rendir mejor, cómo puede afectar negativamente cuando termina una competición\".\n\nPero esta investigación no sólo puede extraer conclusiones interesantes y útiles para los deportistas profesionales, \"investigando el deporte de alto rendimiento podemos conocer los beneficios y perjuicios de la cafeína que también afectarían al deportista amateur y hacer un deporte en general más seguro\".\n\nPara el desarrollo de este proyecto de investigación están colaborando el Laboratorio de Fisiología del Ejercicio de la Universidad Camilo José Cela y la Agencia para la Protección de la Salud en el Deporte.\nPara el desarrollo de este proyecto de investigación están colaborando el Laboratorio de Fisiología del Ejercicio de la Universidad Camilo José Cela y la Agencia para la Protección de la Salud en el 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Que caminhos?","qualifier":{"classid":"main title","classname":"main title","schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"relevantdate":[],"description":[{"value":"A agricultura está inevitavelmente associada a um conjunto de variáveis no ecossistema, onde estão incluídos as pragas e doenças nas culturas e presença de outras plantas que competem pelo alimento, água e luz. Estes agentes são responsáveis por perdas no rendimento que, normalmente, não são aceites pelo agricultor. E é, por isso, que se recorre aos pesticidas: é preciso proteger as culturas do ataque dos seus inimigos.\nTambém é verdade que a agricultura é a atividade desenvolvida pelo Homem que mais se aproxima dos sistemas naturais e tem, assim, um papel central na produção de diversos serviços do ecossistema, como a biodiversidade agrícola, qualidade do solo e água, estabilidade climática e manutenção da paisagem. Ora, o uso dos pesticidas introduz efeitos negativos em todo este sistema: degradam o solo, reduzem a qualidade e disponibilidade de água, causam poluição e perda de habitats selvagens e de biodiversidade. Aqui reside a encruzilhada na proteção das 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Evidences about their quality of life (QoL) are, nevertheless, still limited. Different conceptualizations of QoL, the overvaluation of the negative aspects of advanced age and the heterogeneity of the oldest old are some issues that could explain the dif􏰀culties in this assessment. Objectives: This study aims to analyze the perceived QoL in a sample of centenarians and analyze if there are significant life context (community vs institutional) and health (objective and subjective perception) differences.\nMethods: A subsample of 59 Portuguese centenarians who participated in the PT100_Oporto Centenarian Study, conducted in 2013 was considered. Descriptive and chi-square analysis were conducted.\nResults: Most centenarians perceived their QoL in a very positive manner (50.8% rated it as “good”, 6.8% as “very good” and 3.4% as “excellent”); 28.8% perceived it as “acceptable” and 10.2% as “bad”. No significant statistical differences were found for the considered variables.\nConclusions: Centenarians seem to present high levels of perceived QoL, which can be associated with a great ability to adapt to adverse situations that occur through their lives. Neither contextual and health aspects seem to in􏰂uence the perception of QoL in this particular sample study. Nonetheless, more research is \nneeded to understand the speci􏰀cities of the QoL in the centenarian population, namely the role of psychological and social variables.\ninfo:eu-repo/semantics/publishedVersion","dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"dateofacceptance":{"value":"2014-01-01","dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}},"publisher":null,"embargoenddate":null,"source":[],"fulltext":[],"format":[],"contributor":[],"resourcetype":null,"coverage":[],"bestaccessright":null,"context":[{"id":"http://zenodo.org/communities/edenis"},{"id":"http://zenodo.org/communities/aginfra"}, {"id":"http://zenodo.org/communities/primefish"}],"externalReference":[],"instance":[{"license":null,"accessright":{"classid":"OPEN","classname":"Open Access","schemeid":"dnet:access_modes","schemename":"dnet:access_modes"},"instancetype":{"classid":"0020","classname":"Other ORP type","schemeid":"dnet:publication_resource","schemename":"dnet:publication_resource"},"hostedby":{"key":"opendoar____::2017","value":"Repositório Científico do Instituto Politécnico de Viseu","dataInfo":null},"url":["http://hdl.handle.net/10400.19/5415"],"distributionlocation":"","collectedfrom":{"key":"opendoar____::2017","value":"Repositório Científico do Instituto Politécnico de Viseu","dataInfo":null},"dateofacceptance":{"value":"2014-01-01","dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}},"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"contactperson":[],"contactgroup":[],"tool":[]} -{"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}},"lastupdatetimestamp":1585055423836,"id":"50|od______2017::1e400f1747487fd15998735c41a55c72","originalId":["od______2017::1e400f1747487fd15998735c41a55c72"],"collectedfrom":[{"key":"opendoar____::2017","value":"Repositório Científico do Instituto Politécnico de Viseu","dataInfo":null}],"pid":[],"dateofcollection":"2019-02-25T04:30:56.996Z","dateoftransformation":"2019-11-30T04:19:59.556Z","extraInfo":[],"oaiprovenance":{"originDescription":{"harvestDate":"2019-02-25T04:30:56.996Z","altered":true,"baseURL":"http://repositorio.ipv.pt/oai/openaire","identifier":"oai:repositorio.ipv.pt:10400.19/5422","datestamp":"2019-02-23T03:01:29Z","metadataNamespace":"http://www.openarchives.org/OAI/2.0/oai_dc/"}},"author":[{"fullname":"Brandão, Daniela","name":"Daniela","surname":"Brandão","rank":1,"pid":null,"affiliation":null},{"fullname":"Ribeiro, Oscar","name":"Oscar","surname":"Ribeiro","rank":2,"pid":null,"affiliation":null},{"fullname":"Araújo, Lia","name":"Lia","surname":"Araújo","rank":3,"pid":null,"affiliation":null},{"fullname":"Paul, Constança","name":"Constança","surname":"Paul","rank":4,"pid":null,"affiliation":null}],"resulttype":{"classid":"other","classname":"other","schemeid":"dnet:result_typologies","schemename":"dnet:result_typologies"},"language":{"classid":"eng","classname":"English","schemeid":"dnet:languages","schemename":"dnet:languages"},"country":[],"subject":[{"value":"Oldest old","qualifier":{"classid":"keyword","classname":"keyword","schemeid":"dnet:result_subject","schemename":"dnet:result_subject"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}},{"value":"Census","qualifier":{"classid":"keyword","classname":"keyword","schemeid":"dnet:result_subject","schemename":"dnet:result_subject"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"title":[{"value":"What about the Portuguese oldest old? A global overview using census data","qualifier":{"classid":"main title","classname":"main title","schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"relevantdate":[],"description":[{"value":"Background\nThe older population is itself aging, and achieving an advanced age is becoming more common worldwide. In Portugal, individuals aged 80+ represent 5.6 % of the total population and 26.5 % of the popu- lation were aged 65 and over in 2011. Having a national profile on this population will give important information to develop interven- tion programs and identify the areas requiring most attention. Ob- jective: This study aims to provide a profile of the Portuguese oldest old, as given by the last national census data.\nMethods\nThe characteristics of all residents aged 80+ (N = 532,219) were ana- lysed considering socio-demographic information (gender, marital status, education, type of residence, place of birth, income) and the existence of difficulties in functional, sensorial and cognitive activities due to health problems or ageing.\nResults\nThe majority of the most aged are females (64.5 %), widowed (53.9 %), and present low educational levels (46.1 % never attended school and 31.6 % do not know how to read/write). Own pensions constitute the main source of income (96.3 %) and the majority live in private households (88.8 %), with 43.2 % currently living in the place where they were born. The majority (73.0 %) reported major difficulties in at least one functional activity – bathing/dressing, walk- ing/climbing stairs, seeing, hearing, memory/concentration, under- standing others/being understood.\nConclusions\nThe high percentage of oldest old living in private households and the presence of functional limitations point to the importance of in- formal care and community care services to support this population. Further studies paying attention to their needs and utilisation of ser- vices are 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Estes agentes são responsáveis por perdas no rendimento que, normalmente, não são aceites pelo agricultor. E é, por isso, que se recorre aos pesticidas: é preciso proteger as culturas do ataque dos seus inimigos.\nTambém é verdade que a agricultura é a atividade desenvolvida pelo Homem que mais se aproxima dos sistemas naturais e tem, assim, um papel central na produção de diversos serviços do ecossistema, como a biodiversidade agrícola, qualidade do solo e água, estabilidade climática e manutenção da paisagem. Ora, o uso dos pesticidas introduz efeitos negativos em todo este sistema: degradam o solo, reduzem a qualidade e disponibilidade de água, causam poluição e perda de habitats selvagens e de biodiversidade. 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Evidences about their quality of life (QoL) are, nevertheless, still limited. Different conceptualizations of QoL, the overvaluation of the negative aspects of advanced age and the heterogeneity of the oldest old are some issues that could explain the dif􏰀culties in this assessment. Objectives: This study aims to analyze the perceived QoL in a sample of centenarians and analyze if there are significant life context (community vs institutional) and health (objective and subjective perception) differences.\nMethods: A subsample of 59 Portuguese centenarians who participated in the PT100_Oporto Centenarian Study, conducted in 2013 was considered. Descriptive and chi-square analysis were conducted.\nResults: Most centenarians perceived their QoL in a very positive manner (50.8% rated it as “good”, 6.8% as “very good” and 3.4% as “excellent”); 28.8% perceived it as “acceptable” and 10.2% as “bad”. No significant statistical differences were found for the considered variables.\nConclusions: Centenarians seem to present high levels of perceived QoL, which can be associated with a great ability to adapt to adverse situations that occur through their lives. Neither contextual and health aspects seem to in􏰂uence the perception of QoL in this particular sample study. Nonetheless, more research is \nneeded to understand the speci􏰀cities of the QoL in the centenarian population, namely the role of psychological and social variables.\ninfo:eu-repo/semantics/publishedVersion","dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"dateofacceptance":{"value":"2014-01-01","dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}},"publisher":null,"embargoenddate":null,"source":[],"fulltext":[],"format":[],"contributor":[],"resourcetype":null,"coverage":[],"bestaccessright":null,"context":[{"id":"http://zenodo.org/communities/edenis"},{"id":"http://zenodo.org/communities/aginfra"}, {"id":"http://zenodo.org/communities/primefish"}],"externalReference":[],"instance":[{"license":null,"accessright":{"classid":"OPEN","classname":"Open Access","schemeid":"dnet:access_modes","schemename":"dnet:access_modes"},"instancetype":{"classid":"0020","classname":"Other ORP type","schemeid":"dnet:publication_resource","schemename":"dnet:publication_resource"},"hostedby":{"key":"opendoar____::2017","value":"Repositório Científico do Instituto Politécnico de Viseu","dataInfo":null},"url":["http://hdl.handle.net/10400.19/5415"],"distributionlocation":"","collectedfrom":{"key":"opendoar____::2017","value":"Repositório Científico do Instituto Politécnico de Viseu","dataInfo":null},"dateofacceptance":{"value":"2014-01-01","dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}},"processingchargeamount":null,"processingchargecurrency":null,"refereed":null}],"contactperson":[],"contactgroup":[],"tool":[]} +{"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}},"lastupdatetimestamp":1585055423836,"id":"50|od______2017::1e400f1747487fd15998735c41a55c72","originalId":["od______2017::1e400f1747487fd15998735c41a55c72"],"collectedfrom":[{"key":"opendoar____::2017","value":"Repositório Científico do Instituto Politécnico de Viseu","dataInfo":null}],"pid":[],"dateofcollection":"2019-02-25T04:30:56.996Z","dateoftransformation":"2019-11-30T04:19:59.556Z","extraInfo":[],"oaiprovenance":{"originDescription":{"harvestDate":"2019-02-25T04:30:56.996Z","altered":true,"baseURL":"http://repositorio.ipv.pt/oai/openaire","identifier":"oai:repositorio.ipv.pt:10400.19/5422","datestamp":"2019-02-23T03:01:29Z","metadataNamespace":"http://www.openarchives.org/OAI/2.0/oai_dc/"}},"author":[{"fullname":"Brandão, Daniela","name":"Daniela","surname":"Brandão","rank":1,"pid":null,"affiliation":null},{"fullname":"Ribeiro, Oscar","name":"Oscar","surname":"Ribeiro","rank":2,"pid":null,"affiliation":null},{"fullname":"Araújo, Lia","name":"Lia","surname":"Araújo","rank":3,"pid":null,"affiliation":null},{"fullname":"Paul, Constança","name":"Constança","surname":"Paul","rank":4,"pid":null,"affiliation":null}],"resulttype":{"classid":"other","classname":"other","schemeid":"dnet:result_typologies","schemename":"dnet:result_typologies"},"language":{"classid":"eng","classname":"English","schemeid":"dnet:languages","schemename":"dnet:languages"},"country":[],"subject":[{"value":"TWITTER","qualifier":{"classid":"keyword","classname":"keyword","schemeid":"dnet:result_subject","schemename":"dnet:result_subject"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}},{"value":"Census","qualifier":{"classid":"keyword","classname":"keyword","schemeid":"dnet:result_subject","schemename":"dnet:result_subject"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"title":[{"value":"What about the Portuguese oldest old? A global overview using census data","qualifier":{"classid":"main title","classname":"main title","schemeid":"dnet:dataCite_title","schemename":"dnet:dataCite_title"},"dataInfo":{"invisible":false,"inferred":false,"deletedbyinference":false,"trust":"0.9","inferenceprovenance":"","provenanceaction":{"classid":"sysimport:crosswalk:repository","classname":"sysimport:crosswalk:repository","schemeid":"dnet:provenanceActions","schemename":"dnet:provenanceActions"}}}],"relevantdate":[],"description":[{"value":"Background\nThe older population is itself aging, and achieving an advanced age is becoming more common worldwide. In Portugal, individuals aged 80+ represent 5.6 % of the total population and 26.5 % of the popu- lation were aged 65 and over in 2011. Having a national profile on this population will give important information to develop interven- tion programs and identify the areas requiring most attention. Ob- jective: This study aims to provide a profile of the Portuguese oldest old, as given by the last national census data.\nMethods\nThe characteristics of all residents aged 80+ (N = 532,219) were ana- lysed considering socio-demographic information (gender, marital status, education, type of residence, place of birth, income) and the existence of difficulties in functional, sensorial and cognitive activities due to health problems or ageing.\nResults\nThe majority of the most aged are females (64.5 %), widowed (53.9 %), and present low educational levels (46.1 % never attended school and 31.6 % do not know how to read/write). Own pensions constitute the main source of income (96.3 %) and the majority live in private households (88.8 %), with 43.2 % currently living in the place where they were born. The majority (73.0 %) reported major difficulties in at least one functional activity – bathing/dressing, walk- ing/climbing stairs, seeing, hearing, memory/concentration, under- standing others/being understood.\nConclusions\nThe high percentage of oldest old living in private households and the presence of functional limitations point to the importance of in- formal care and community care services to support this population. Further studies paying attention to their needs and utilisation of ser- vices are 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Different patterns of education level were found on countries where this infor- mation was reliable: Portugal and Greece are the countries with lower levels of education, with more than half without formal education; Finland, UK and Iceland are the countries with higher levels of education, with all centenarians having at least lower secondary education. Analysing the residence situation (living in the community or living in the institu- tion), Romania, Bulgaria, Lithuania, Greece and Latvia are the countries that present higher percentages of centenarians living in the community; Iceland is the country with lower percentage of centenarians living in the community. A posi- tive correlation between the number of centenarians and the percentage of Total Health Expenditure of GDP is observed. 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Different patterns of education level were found on countries where this infor- mation was reliable: Portugal and Greece are the countries with lower levels of education, with more than half without formal education; Finland, UK and Iceland are the countries with higher levels of education, with all centenarians having at least lower secondary education. Analysing the residence situation (living in the community or living in the institu- tion), Romania, Bulgaria, Lithuania, Greece and Latvia are the countries that present higher percentages of centenarians living in the community; Iceland is the country with lower percentage of centenarians living in the community. A posi- tive correlation between the number of centenarians and the percentage of Total Health Expenditure of GDP is observed. 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This phenomena lead to an increasing number of centenarian studies, improving the knowledge about this population, validating the idea of heterogeneity that char- acterizes this group. However, the factors influencing their aging survival remains understudied. The present study aims to identify factors associated with survival after 100 years old in a sample of Portuguese centenarians. After being included in a population-based study on centenarians (PT100–Oporto Centenarian Study), each participant was assessed by a tel- ephone interview every 6 months in order to assess his/her health condition. Follow-up was considered as the time (in months) between 100th birthday and death or the last tel- ephone contact. Survival analysis was performed to iden- tify factors associated with survival after 100 years old. Sociological and medical factors were considered. 140 cen- tenarians were assessed and 115 (82.1%) died. The median survival time after 100 years old was 38 months. Survival after age 100 was associated with acute disease (last month), self-perception of physical condition and drinking status. This study intends do identify factors that predict longevity in long-lived individuals. Findings recognize the importance of factors related with health status, self-perception of physi- cal condition and life style (no alcohol consume) for survival. Further studies should explore the predictive value of these factors, as well as psychosocial aspects, considering living longer but also the quality of time beyond one 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Therefore, the analysis of survival time after becoming a centenarian and related factors constitute an import- ant issue for the quality of care provision. The objective of this study is to identify health related factors associated with the time lived after being 100 years of age. Methods: Data come from the population-based study PT100 (Oporto Centenarian Study) and considers information gathered through face-to-face interviews with centenarians and their proxies during 2013. Study eligibility criteria included being 100 and more years old and living in the Oporto Metropolitan Area. Survival analyses were performed in order to identify factors associated with survival after the 100. Functional status (e.g., walking, being bedrid- den) and specific health conditions (e.g. presence/absence of cognitive impairment) were considered as potential factors. Results: The sam- ple comprises 140 centenarians with approximately 14.5 months as median survival time after their 100th anniversary. Centenarians who were bedridden presented a higher probability to live after 100 years when compared with functionally independent centenarians. Presence of cognitive impairment was not associated with the time lived after 100 years old. Conclusions: Given the increasing number of centenarians in Portugal, different studies based on this population need to be consid- ered. The present study, focused on the life after 100 years old, provides new information about this topic contributing to the formulation of new scientific questions for this 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a/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/twitter/software/software_10.json +++ b/dhp-workflows/dhp-enrichment/src/test/resources/eu/dnetlib/dhp/eosctag/twitter/software/software_10.json @@ -1,5 +1,5 @@ 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expected heterozygosity, or by R, the allelic richness). This contribution is splitted in two components: one due to the diversity of the population, the other due to its differentiation from the remaining populations. The program may also be used to obtain allelic richness after rarefaction (to a sample size chosen by the investigator) for a set of populations. It can be used in conjunction with the program haplodiv, based on the paper by Pons & Petit 1995, TAG 90, 462-470, which will provide standard errors for the diversity and differentiation parameters. The input file is a text file (see example: rartest.txt), where the first line indicates the number of haplotypes (here it is 18), the number of populations (here it is 4), and the rarefaction size (it should not be larger than the smallest population sample size; here the rarefaction size is 10, and the smallest sample size is 20). Then follows the data for each population (line), with the number of each haplotype in each population (don't use relative frequencies): 18 4 10 1 0 1 0 0 0 1 1 ...(18 columns) 0 1 2 1 1 0 13 0 ... 0 0 8 0 0 3 6 0 ... 1 0 9 0 0 3 7 1 Results can be seen in the output file (rartest.out here; to be printed horizontally). General measures are given first: within population diversity (Hs), total diversity (Ht), and Gst are given, followed by similar measures based on allelic richness. Then you get the results for each population : H, its standard error, allelic richness after rarefaction, the divergence from the other populations (DHs, DHt, DGst, see the paper in Conservation Biology), and the contributions Ct, Cs, Cd followed by the contributions for allelic richness measures. The program is written for an haploid gene but may be used for nuclear genes, assuming Hardy-Weinberg equilibrium. How to proceed when there are several loci? Do not take the mean across Gst or across Contributions. They are ratios, so you should take the mean of the numerator and the mean of the denominator separately. For the denominator: take the mean of hT and Rt-1 across loci. For the numerator: multiply the contributions by hT or Rt-1 (respectively for contributions to diversity or to allelic richness) and take the mean of these products across loci. Then compute the ratio of the two 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into account the distances between haplotypes (ie, that consider all haplotypes equally divergent).[br/] The source file should be an ASCII file (its name should have 8 characters maximum: 12345678.txt) and should include the following information:[br/] First line :[br/] Number of cytotypes Number of populations Number of characters distinguishing the variants (for instance number of polymorphic fragments, or of polymorphic nucleotide sites). The program asks for the number of permutations to be made.[br/] see the example (\\ExamplePermut\\input.txt and \\ExamplePermut\\output.out).[br/] Then follows the number of individuals having a given cytotype (column) in a given population (row). Finally, and without interruption, provide the table of character states for all haplotypes, where each line corresponds to one haplotype, and each column to a character. No column should be empty (no missing haplotype) and each population (row) should be composed of AT LEAST 3 individuals![br/] The output file provides permutated values of Nst in a single row, and the value of the last 5% and last 1%. The mean of the permutated values is also given and should be close to the Gst value (by construction). To test if the observed Nst value is larger than the Gst, we count how many permutated values are larger than the observed Nst. If you have 5% of the permutated values greater than the observed value of Nst, then your test is not significant, otherwise it is and you know the P-value. This is akin to testing if Gst = Nst.[br/] [br/] README CpSSR :[br/] It computes measures of diversity and differenciation from haploid population genetic data, when the difference in number of repeats between alleles is available, and tests whether the differentiation and diversity measures differ from the equivalent measures when the distances between haplotypes is not considered (ie, when all haplotypes are considered equally divergent). The source file should be an ASCII file (its name should have 8 characters maximum: 12345678.txt) and should include the following information:[br/] First line :[br/] Number of cytotypes Number of populations Number of cpSSR loci. The program asks for the number of permutations to be made. See the example (\\ExampleCpSSR\\input.txt and \\ExamplePermut\\CpSSR.out).[br/] Then follows the number of individuals having a given haplotype (column) in a given population (row). Finally, and without interruption, provide the table of length variant states for all haplotypes, where each line corresponds to one haplotype, and each column to a character. No column should be empty (no missing haplotype) and each population (row) should be composed of AT LEAST 3 individuals![br/] The output file provides permutated values of Rst in a single row, and the value of the last 5% and last 1%. The mean of the permutated values is also given and should be close to the Gst value (by construction). To test if the observed Rst value is larger than the Gst, you count how many permutated values are larger than the observed Rst. If you have 5% of the permutated values greater than the observed value of Rst, then your test is not significant, otherwise it is and you know the P-value. This is akin to testing if Gst = Rst. 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