[SKG-IF] tries to make the process finish. need to change the strategy
This commit is contained in:
parent
98bec3d2d2
commit
3c4c4e8ce0
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@ -13,6 +13,15 @@ public class MinProduct implements Serializable {
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private String doi;
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private String pmcid;
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private String arxivid;
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private String pmid;
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public String getPmid() {
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return pmid;
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}
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public void setPmid(String pmid) {
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this.pmid = pmid;
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}
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public String getLocal_identifier() {
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return local_identifier;
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@ -17,6 +17,8 @@ import org.apache.spark.api.java.function.FlatMapFunction;
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import org.apache.spark.api.java.function.MapFunction;
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import org.apache.spark.sql.*;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.types.DataType;
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import org.apache.spark.sql.types.StructType;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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@ -103,63 +105,56 @@ public class SelectConnectedEntities implements Serializable {
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Utils
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.getFunderName(p.getFundingtree().get(0).getValue())
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.equalsIgnoreCase("European Commission"));
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Dataset<Datasource> datasources = Utils
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.readPath(spark, inputPath + "datasource", Datasource.class)
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.filter((FilterFunction<Datasource>) d -> !d.getDataInfo().getDeletedbyinference());
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// select relations having source in the set of identifiers selected for eosc
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Dataset<Relation> resultSource = resultIds
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.joinWith(relation, resultIds.col("value").equalTo(relation.col("source")))
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.map((MapFunction<Tuple2<String, Relation>, Relation>) t2 -> t2._2(), Encoders.bean(Relation.class));
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// write relations having source and target in the set
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resultIds
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.joinWith(resultSource, resultIds.col("value").equalTo(resultSource.col("target")))
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.map((MapFunction<Tuple2<String, Relation>, Relation>) t2 -> t2._2(), Encoders.bean(Relation.class))
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Dataset<Row> relationSource = relation
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.join(resultIds, resultIds.col("value").equalTo(relation.col("source")), "left_semi");
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relationSource
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.join(resultIds, resultIds.col("value").equalTo(relation.col("target")), "left_semi")
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.write()
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.option("compression", "gzip")
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.mode(SaveMode.Overwrite)
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.json(workingDir + "relation");
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// write relations between results and organizations
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resultSource
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.joinWith(organizations, resultSource.col("target").equalTo(organizations.col("id")))
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.map((MapFunction<Tuple2<Relation, Organization>, Relation>) t2 -> t2._1(), Encoders.bean(Relation.class))
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.json(workingDir + "resultrelation");
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//
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// // write relations between results and organizations
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relationSource
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.joinWith(organizations, relation.col("target").equalTo(organizations.col("id")), "left_semi")
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.write()
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.mode(SaveMode.Append)
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "relation");
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.json(workingDir + "organizaitonrelation");
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resultSource
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.joinWith(projects, resultSource.col("target").equalTo(projects.col("id")))
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.map((MapFunction<Tuple2<Relation, Project>, Relation>) t2 -> t2._1(), Encoders.bean(Relation.class))
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relationSource
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.joinWith(projects, relation.col("target").equalTo(projects.col("id")), "left_semi")
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.write()
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.mode(SaveMode.Append)
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "relation");
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.json(workingDir + "projectrelation");
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// write organizations linked to results in the set
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resultSource
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.joinWith(organizations, resultSource.col("target").equalTo(organizations.col("id")))
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.map(
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(MapFunction<Tuple2<Relation, Organization>, Organization>) t2 -> t2._2(),
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Encoders.bean(Organization.class))
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organizations
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.join(relationSource, relationSource.col("target").equalTo(organizations.col("id")), "left_semi")
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.write()
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "organization");
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// write projects linked to results in the set
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resultSource
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.joinWith(projects, resultSource.col("target").equalTo(projects.col("id")))
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.map((MapFunction<Tuple2<Relation, Project>, Project>) t2 -> t2._2(), Encoders.bean(Project.class))
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projects
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.join(relationSource, relationSource.col("target").equalTo(projects.col("id")))
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.write()
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.mode(SaveMode.Append)
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "project");
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// read the results and select all the distinct instance.hostedbykey
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Dataset<String> datasourceReferencedIds = spark.emptyDataset(Encoders.STRING());
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for (EntityType entity : ModelSupport.entityTypes.keySet())
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if (ModelSupport.isResult(entity)) {
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Class<R> resultClazz = ModelSupport.entityTypes.get(entity);
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@ -171,45 +166,88 @@ public class SelectConnectedEntities implements Serializable {
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(FlatMapFunction<R, String>) r -> r
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.getInstance()
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.stream()
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.flatMap(i -> Stream.of(i.getHostedby().getKey(), i.getCollectedfrom().getKey()))
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.filter(i -> i.getHostedby() != null && i.getHostedby().getKey() != null)
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.map(i -> i.getHostedby().getKey())
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.collect(Collectors.toList())
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.iterator(),
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Encoders.STRING()));
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datasourceReferencedIds = datasourceReferencedIds
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.union(
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Utils
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.readPath(spark, workingDir + entity.name(), resultClazz)
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.flatMap(
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(FlatMapFunction<R, String>) r -> r
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.getInstance()
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.stream()
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.filter(i -> i.getCollectedfrom() != null && i.getCollectedfrom().getKey() != null)
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.map(i -> i.getCollectedfrom().getKey())
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.collect(Collectors.toList())
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.iterator(),
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Encoders.STRING()));
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}
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datasourceReferencedIds = datasourceReferencedIds.distinct();
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// join with the datasources and write the datasource in the join
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datasourceReferencedIds
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.joinWith(datasources, datasourceReferencedIds.col("value").equalTo(datasources.col("id")))
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.map((MapFunction<Tuple2<String, Datasource>, Datasource>) t2 -> t2._2(), Encoders.bean(Datasource.class))
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datasources
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.joinWith(
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datasourceReferencedIds, datasourceReferencedIds.col("value").equalTo(datasources.col("id")),
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"left_semi")
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.write()
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "datasource");
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// selecting relations between organizations and projects in the selected set
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Dataset<Organization> organizationSbs = Utils.readPath(spark, workingDir + "organization", Organization.class);
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Dataset<Project> projectSbs = Utils.readPath(spark, workingDir + "project", Project.class);
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Dataset<Relation> orgSourceRels = organizationSbs
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.joinWith(relation, organizationSbs.col("id").equalTo(relation.col("source")))
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.map((MapFunction<Tuple2<Organization, Relation>, Relation>) t2 -> t2._2(), Encoders.bean(Relation.class));
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orgSourceRels
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.joinWith(projectSbs, orgSourceRels.col("target").equalTo(projectSbs.col("id")))
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.map((MapFunction<Tuple2<Relation, Project>, Relation>) t2 -> t2._1(), Encoders.bean(Relation.class))
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StructType tp = StructType.fromDDL("`id` STRING");
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Dataset<Row> organizationSbs = spark
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.read()
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.schema(tp)
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.json(workingDir + "organization")
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.select("id");
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Dataset<Row> projectSbs = spark
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.read()
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.schema(tp)
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.json(workingDir + "project")
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.select("id");
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//
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Dataset<Row> tmpRel;
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tmpRel = relation
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.join(
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organizationSbs, organizationSbs
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.col("id")
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.equalTo(relation.col("source")),
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"left_semi");
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tmpRel
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.join(projectSbs, tmpRel.col("target").equalTo(projectSbs.col("id")), "left_semi")
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.write()
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.mode(SaveMode.Append)
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "relation");
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.json(workingDir + "orgprojelation");
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// selecting relations between datasources and organizations in the selected set
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Dataset<Datasource> datasourceSbs = Utils.readPath(spark, workingDir + "datasource", Datasource.class);
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Dataset<Relation> dsSourceRels = datasourceSbs
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.joinWith(relation, datasourceSbs.col("id").as("dsId").equalTo(relation.col("source")))
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.map((MapFunction<Tuple2<Datasource, Relation>, Relation>) t2 -> t2._2(), Encoders.bean(Relation.class));
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dsSourceRels
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.joinWith(organizationSbs, dsSourceRels.col("target").equalTo(organizations.col("id").as("orgId")))
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.map((MapFunction<Tuple2<Relation, Organization>, Relation>) t2 -> t2._1(), Encoders.bean(Relation.class))
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Dataset<Row> datasourceSbs = spark
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.read()
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.schema(tp)
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.json(workingDir + "datasource")
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.select("id");
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tmpRel = relation
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.join(datasourceSbs, datasourceSbs.col("id").equalTo(relation.col("source")), "left_semi");
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tmpRel
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.join(organizationSbs, tmpRel.col("target").equalTo(organizationSbs.col("id")), "left_semi")
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.write()
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.mode(SaveMode.Append)
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "datsorgrelation");
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Utils
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.readPath(spark, workingDir + "resultrelation", Relation.class)
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.union(Utils.readPath(spark, workingDir + "organizaitonrelation", Relation.class))
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.union(Utils.readPath(spark, workingDir + "projectrelation", Relation.class))
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.union(Utils.readPath(spark, workingDir + "orgprojelation", Relation.class))
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.union(Utils.readPath(spark, workingDir + "datsorgrelation", Relation.class))
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.write()
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "relation");
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@ -2,6 +2,7 @@
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package eu.dnetlib.dhp.oa.graph.dump.skgif;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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import static eu.dnetlib.dhp.oa.graph.dump.skgif.ResultMapper.map;
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import java.io.Serializable;
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import java.util.*;
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@ -9,12 +10,15 @@ import java.util.stream.Collectors;
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import org.apache.commons.io.IOUtils;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.function.FilterFunction;
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import org.apache.spark.api.java.function.MapFunction;
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import org.apache.spark.api.java.function.MapGroupsFunction;
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import org.apache.spark.rdd.RDD;
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import org.apache.spark.sql.*;
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import org.apache.spark.sql.Dataset;
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import org.jetbrains.annotations.NotNull;
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import org.apache.spark.sql.types.DataTypes;
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import org.apache.spark.sql.types.StructType;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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@ -24,10 +28,8 @@ import eu.dnetlib.dhp.schema.common.EntityType;
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import eu.dnetlib.dhp.schema.common.ModelSupport;
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import eu.dnetlib.dhp.schema.oaf.*;
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import eu.dnetlib.dhp.schema.oaf.Datasource;
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import eu.dnetlib.dhp.schema.oaf.Organization;
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import eu.dnetlib.dhp.skgif.model.*;
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import eu.dnetlib.dhp.skgif.model.AccessRight;
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import eu.dnetlib.dhp.utils.DHPUtils;
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import scala.Tuple2;
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/**
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@ -323,7 +325,7 @@ public class DumpResult implements Serializable {
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results
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.joinWith(prr, results.col("id").equalTo(prr.col("resultId")), "left")
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.map((MapFunction<Tuple2<R, PartialResearchProduct>, ResearchProduct>) t2 -> {
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ResearchProduct rp = ResultMapper.map(t2._1());
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ResearchProduct rp = map(t2._1());
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if (Optional.ofNullable(t2._2()).isPresent()) {
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if (Optional.ofNullable(t2._2().getRelated_products()).isPresent())
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rp.setRelated_products(t2._2().getRelated_products());
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@ -357,47 +359,283 @@ public class DumpResult implements Serializable {
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}
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private static void selectRelations(SparkSession spark, String inputPath, String workingDir) {
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Dataset<Relation> relation = Utils
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.readPath(
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spark,
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inputPath + "relation", Relation.class)
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.filter(
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(FilterFunction<Relation>) r -> !r.getDataInfo().getDeletedbyinference() &&
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!r.getDataInfo().getInvisible())
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.filter(
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(FilterFunction<Relation>) r -> r
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.getRelClass()
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.equalsIgnoreCase(RelationType.RESULT_AFFILIATIED_TO_ORGANIZATION.label) ||
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r.getRelClass().equalsIgnoreCase(RelationType.RESULT_OUTCOME_FUNDING.label) ||
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r.getRelClass().equalsIgnoreCase(RelationType.SUPPLEMENT.label) ||
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r.getRelClass().equalsIgnoreCase(RelationType.DOCUMENTS.label) ||
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r.getRelClass().equalsIgnoreCase(RelationType.PART.label) ||
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r.getRelClass().equalsIgnoreCase(RelationType.VERSION.label) ||
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r.getRelClass().equalsIgnoreCase(RelationType.CITATION.label));
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List<String> relationsProducts = Arrays
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.asList(
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RelationType.CITATION.label,
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// RelationType.RESULT_AFFILIATIED_TO_ORGANIZATION.label,
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RelationType.SUPPLEMENT.label,
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// RelationType.RESULT_OUTCOME_FUNDING.label,
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RelationType.DOCUMENTS.label,
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RelationType.PART.label,
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RelationType.VERSION.label);
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Dataset<EncloseMinElement> encloseMinEntity = Utils
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.readPath(spark, workingDir + "minEntity", EncloseMinElement.class);
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// relationsProducts
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// .stream()
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// .forEach(r -> buildRelationPerProducts(spark, inputPath, workingDir, r));
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// buildRelationPerAffiliation(
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// spark, inputPath, workingDir, RelationType.RESULT_AFFILIATIED_TO_ORGANIZATION.label);
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buildRelationPerGrant(spark, inputPath, workingDir, RelationType.RESULT_OUTCOME_FUNDING.label);
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relation
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.joinWith(encloseMinEntity, relation.col("target").equalTo(encloseMinEntity.col("enclosedEntityId")))
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.map((MapFunction<Tuple2<Relation, EncloseMinElement>, EncloseMinElement>) t2 -> {
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EncloseMinElement eme = t2._2();
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eme.setResultId(t2._1().getSource());
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eme.setSemantics(t2._1().getRelClass());
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return eme;
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}, Encoders.bean(EncloseMinElement.class))
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.groupByKey((MapFunction<EncloseMinElement, String>) eme -> eme.getResultId(), Encoders.STRING())
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.mapGroups((MapGroupsFunction<String, EncloseMinElement, RelationPerProduct>) (k, v) -> {
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RelationPerProduct rpp = new RelationPerProduct();
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rpp.setResultId(k);
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insertEnclosedElement(rpp, v.next());
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v.forEachRemaining(e -> insertEnclosedElement(rpp, e));
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return rpp;
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}, Encoders.bean(RelationPerProduct.class))
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RDD<RelationPerProduct> temp = spark
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.read()
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.schema(Encoders.bean(RelationPerProduct.class).schema())
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.json(workingDir + "aggrelation_temp")
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.as(Encoders.bean(RelationPerProduct.class))
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.toJavaRDD()
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.mapToPair(v -> new Tuple2<>(v.getResultId(), v))
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.reduceByKey((a, b) -> {
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mergeRelationPerProduct(a, b);
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return a;
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})
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.map(v -> v._2())
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.rdd();
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spark
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.createDataset(temp, Encoders.bean(RelationPerProduct.class))
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.write()
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.mode(SaveMode.Overwrite)
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.option("compression", "gzip")
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.json(workingDir + "/aggrelation");
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}
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private static void buildRelationPerGrant(SparkSession spark, String inputPath, String workingDir,
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String relationType) {
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log.info("Relation: {}", relationType);
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final StructType relationstructureSchema = new StructType()
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.add(
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"dataInfo", new StructType()
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.add("deletedbyinference", DataTypes.BooleanType))
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.add("source", DataTypes.StringType)
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.add("target", DataTypes.StringType)
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.add("relClass", DataTypes.StringType);
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final StructType grantSchema = new StructType()
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.add(
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"local_identifier", DataTypes.StringType)
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.add("funder", DataTypes.StringType)
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.add("code", DataTypes.StringType)
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.add("title", DataTypes.StringType)
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;
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Dataset<Row> relation = spark
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.read()
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.schema(relationstructureSchema)
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.json(inputPath + "relation")
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.filter(
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"datainfo.deletedbyinference != true and " +
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"relclass == '" + relationType + "'")
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.drop("dataInfo");
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Dataset<Row> minProduct = spark
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.read()
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.schema(grantSchema)
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.json(workingDir + "minGrant");
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relation
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.joinWith(
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minProduct, relation.col("target").equalTo(minProduct.col("local_identifier")))
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.selectExpr("_1.source as sourceResult", "_1.relClass as relClass", "_2.*")
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.groupByKey((MapFunction<Row, String>) r -> r.getAs("sourceResult"), Encoders.STRING())
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.mapGroups((MapGroupsFunction<String, Row, RelationPerProduct>) (k, it) -> {
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RelationPerProduct rpp = new RelationPerProduct();
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rpp.setResultId(k);
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rpp.setRelatedProduct(new HashMap<>());
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updateRelevantGrant(rpp, it.next());
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it.forEachRemaining(r -> updateRelevantGrant(rpp, r));
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return rpp;
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}, Encoders.bean(RelationPerProduct.class))
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// .show(false);
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.write()
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.mode(SaveMode.Append)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "aggrelation_temp");
|
||||
}
|
||||
|
||||
private static void updateRelevantGrant(RelationPerProduct rpp, Row next) {
|
||||
if (!Optional.ofNullable(rpp.getFunding()).isPresent())
|
||||
rpp.setFunding(new ArrayList<>());
|
||||
MinGrant mo = new MinGrant();
|
||||
mo.setLocal_identifier(next.getAs("local_identifier"));
|
||||
mo.setTitle(next.getAs("title"));
|
||||
mo.setFunder(next.getAs("fundet"));
|
||||
mo.setCode(next.getAs("code"));
|
||||
|
||||
rpp.getFunding().add(mo);
|
||||
}
|
||||
|
||||
private static void buildRelationPerAffiliation(SparkSession spark, String inputPath, String workingDir,
|
||||
String relationType) {
|
||||
log.info("Relation: {}", relationType);
|
||||
final StructType relationstructureSchema = new StructType()
|
||||
.add(
|
||||
"dataInfo", new StructType()
|
||||
.add("deletedbyinference", DataTypes.BooleanType))
|
||||
.add("source", DataTypes.StringType)
|
||||
.add("target", DataTypes.StringType)
|
||||
.add("relClass", DataTypes.StringType);
|
||||
|
||||
final StructType orgSchema = new StructType()
|
||||
.add(
|
||||
"local_identifier", DataTypes.StringType)
|
||||
|
||||
.add("name", DataTypes.StringType)
|
||||
.add("ror", DataTypes.StringType)
|
||||
.add("isni", DataTypes.StringType)
|
||||
.add("fundRef", DataTypes.StringType)
|
||||
.add("rinGold", DataTypes.StringType)
|
||||
.add("wikidata", DataTypes.StringType);
|
||||
|
||||
Dataset<Row> relation = spark
|
||||
.read()
|
||||
.schema(relationstructureSchema)
|
||||
.json(inputPath + "relation")
|
||||
.filter(
|
||||
"datainfo.deletedbyinference != true and " +
|
||||
"relclass == '" + relationType + "'")
|
||||
.drop("dataInfo");
|
||||
|
||||
Dataset<Row> minOrganization = spark
|
||||
.read()
|
||||
.schema(orgSchema)
|
||||
.json(workingDir + "minOrganization");
|
||||
|
||||
relation
|
||||
.joinWith(
|
||||
minOrganization, relation.col("target").equalTo(minOrganization.col("local_identifier")))
|
||||
.selectExpr("_1.source as sourceResult", "_1.relClass as relClass", "_2.*")
|
||||
.groupByKey((MapFunction<Row, String>) r -> r.getAs("sourceResult"), Encoders.STRING())
|
||||
.mapGroups((MapGroupsFunction<String, Row, RelationPerProduct>) (k, it) -> {
|
||||
RelationPerProduct rpp = new RelationPerProduct();
|
||||
rpp.setResultId(k);
|
||||
rpp.setRelatedProduct(new HashMap<>());
|
||||
updateRelevantOrganization(rpp, it.next());
|
||||
it.forEachRemaining(r -> updateRelevantOrganization(rpp, r));
|
||||
return rpp;
|
||||
}, Encoders.bean(RelationPerProduct.class))
|
||||
// .show(false);
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "aggrelation_temp");
|
||||
}
|
||||
|
||||
private static void updateRelevantOrganization(RelationPerProduct rpp, Row next) {
|
||||
if (!Optional.ofNullable(rpp.getOrganizations()).isPresent())
|
||||
rpp.setOrganizations(new ArrayList<>());
|
||||
MinOrganization mo = new MinOrganization();
|
||||
mo.setLocal_identifier(next.getAs("local_identifier"));
|
||||
mo.setIsni(next.getAs("isni"));
|
||||
mo.setRor(next.getAs("ror"));
|
||||
mo.setName(next.getAs("name"));
|
||||
mo.setWikidata(next.getAs("wikidata"));
|
||||
mo.setFundRef(next.getAs("fundRef"));
|
||||
mo.setRinGold(next.getAs("rinGold"));
|
||||
|
||||
rpp.getOrganizations().add(mo);
|
||||
}
|
||||
|
||||
private static void buildRelationPerProducts(SparkSession spark, String inputPath, String workingDir,
|
||||
String relationType) {
|
||||
log.info("Relation: {}", relationType);
|
||||
final StructType relationstructureSchema = new StructType()
|
||||
.add(
|
||||
"dataInfo", new StructType()
|
||||
.add("deletedbyinference", DataTypes.BooleanType))
|
||||
.add("source", DataTypes.StringType)
|
||||
.add("target", DataTypes.StringType)
|
||||
.add("relClass", DataTypes.StringType);
|
||||
|
||||
final StructType productSchema = new StructType()
|
||||
.add(
|
||||
"local_identifier", DataTypes.StringType)
|
||||
|
||||
.add("title", DataTypes.StringType)
|
||||
.add("doi", DataTypes.StringType)
|
||||
.add("pmcid", DataTypes.StringType)
|
||||
.add("arxivid", DataTypes.StringType)
|
||||
.add("pmid", DataTypes.StringType);
|
||||
|
||||
Dataset<Row> relation = spark
|
||||
.read()
|
||||
.schema(relationstructureSchema)
|
||||
.json(inputPath + "relation")
|
||||
.filter(
|
||||
"datainfo.deletedbyinference != true and " +
|
||||
"relclass == '" + relationType + "'")
|
||||
.drop("dataInfo");
|
||||
|
||||
Dataset<Row> minProduct = spark
|
||||
.read()
|
||||
.schema(productSchema)
|
||||
.json(workingDir + "minProduct");
|
||||
|
||||
relation
|
||||
.joinWith(
|
||||
minProduct, relation.col("target").equalTo(minProduct.col("local_identifier")))
|
||||
.selectExpr("_1.source as sourceResult", "_1.relClass as relClass", "_2.*")
|
||||
.groupByKey((MapFunction<Row, String>) r -> r.getAs("sourceResult"), Encoders.STRING())
|
||||
.mapGroups((MapGroupsFunction<String, Row, RelationPerProduct>) (k, it) -> {
|
||||
RelationPerProduct rpp = new RelationPerProduct();
|
||||
rpp.setResultId(k);
|
||||
rpp.setRelatedProduct(new HashMap<>());
|
||||
updateRelatedProduct(rpp, it.next());
|
||||
it.forEachRemaining(r -> updateRelatedProduct(rpp, r));
|
||||
return rpp;
|
||||
}, Encoders.bean(RelationPerProduct.class))
|
||||
// .show(false);
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "aggrelation_temp");
|
||||
// .map((MapFunction<Tuple2<Row, EncloseMinElement>, RelationPerProduct>) t2 -> {
|
||||
// RelationPerProduct rpp = new RelationPerProduct();
|
||||
// t2._2().setResultId(t2._1().getAs("source"));
|
||||
// t2._2().setSemantics(t2._1().getAs("relClass"));
|
||||
// insertEnclosedElement(rpp, t2._2());
|
||||
// rpp.setResultId(t2._1().getAs("source"));
|
||||
// return rpp;
|
||||
// }, Encoders.bean(RelationPerProduct.class))
|
||||
// .filter(Objects::nonNull)
|
||||
// .toJavaRDD()
|
||||
// .mapToPair(value -> new Tuple2<>(value.getResultId(), value))
|
||||
// .reduceByKey((a, b) -> {
|
||||
// mergeRelationPerProduct(a, b);
|
||||
// return a;
|
||||
// })
|
||||
//
|
||||
// .map(value -> value._2)
|
||||
// .rdd(),
|
||||
// Encoders.bean(RelationPerProduct.class))
|
||||
//// .saveAsTextFile(workingDir + "/aggrelation", GzipCodec.class);
|
||||
//// .groupByKey((MapFunction<EncloseMinElement, String>) eme -> eme.getResultId(), Encoders.STRING())
|
||||
//// .mapGroups((MapGroupsFunction<String, EncloseMinElement, RelationPerProduct>) (k, v) -> {
|
||||
//// RelationPerProduct rpp = new RelationPerProduct();
|
||||
//// rpp.setResultId(k);
|
||||
//// insertEnclosedElement(rpp, v.next());
|
||||
//// v.forEachRemaining(e -> insertEnclosedElement(rpp, e));
|
||||
//// return rpp;
|
||||
//// }, Encoders.bean(RelationPerProduct.class))
|
||||
// .write()
|
||||
// .mode(SaveMode.Append)
|
||||
// .option("compression", "gzip")
|
||||
// .json(workingDir + "/aggrelation_temp");
|
||||
}
|
||||
|
||||
private static void updateRelatedProduct(RelationPerProduct rpp, Row next) {
|
||||
String key = next.getAs("relClass");
|
||||
if (!rpp.getRelatedProduct().keySet().contains(key))
|
||||
rpp.getRelatedProduct().put(key, new ArrayList<>());
|
||||
MinProduct mp = new MinProduct();
|
||||
mp.setLocal_identifier(next.getAs("local_identifier"));
|
||||
mp.setTitle(next.getAs("title"));
|
||||
mp.setPmid(next.getAs("pmid"));
|
||||
mp.setArxivid(next.getAs("arxivid"));
|
||||
mp.setPmcid(next.getAs("pmcid"));
|
||||
mp.setDoi(next.getAs("doi"));
|
||||
rpp.getRelatedProduct().get(key).add(mp);
|
||||
}
|
||||
|
||||
private static void insertEnclosedElement(RelationPerProduct rpp, EncloseMinElement element) {
|
||||
|
|
@ -414,19 +652,21 @@ public class DumpResult implements Serializable {
|
|||
|
||||
}
|
||||
|
||||
private static <R extends Result> Dataset<EncloseMinElement> getMinProduct(SparkSession spark, String inputPath,
|
||||
Class<R> clazz) {
|
||||
return Utils
|
||||
.readPath(spark, inputPath, clazz)
|
||||
.filter(
|
||||
(FilterFunction<R>) r -> !r.getDataInfo().getDeletedbyinference() &&
|
||||
!r.getDataInfo().getInvisible())
|
||||
.map((MapFunction<R, EncloseMinElement>) r -> {
|
||||
EncloseMinElement eme = new EncloseMinElement();
|
||||
eme.setEnclosedEntityId(r.getId());
|
||||
eme.setMinProduct(Utils.getMinProduct(r));
|
||||
return eme;
|
||||
}, Encoders.bean(EncloseMinElement.class));
|
||||
private static void mergeRelationPerProduct(RelationPerProduct rpp1, RelationPerProduct rpp2) {
|
||||
if (Optional.ofNullable(rpp2.getOrganizations()).isPresent())
|
||||
rpp1.getOrganizations().addAll(rpp2.getOrganizations());
|
||||
if (Optional.ofNullable(rpp2.getFunding()).isPresent())
|
||||
rpp1.getFunding().addAll(rpp2.getFunding());
|
||||
if (Optional.ofNullable(rpp2.getRelatedProduct()).isPresent()) {
|
||||
Map<String, List<MinProduct>> temp = rpp2.getRelatedProduct();
|
||||
for (String key : temp.keySet()) {
|
||||
if (!rpp1.getRelatedProduct().containsKey(key))
|
||||
rpp1.getRelatedProduct().put(key, new ArrayList<>());
|
||||
for (MinProduct mp : rpp2.getRelatedProduct().get(key))
|
||||
rpp1.getRelatedProduct().get(key).add(mp);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -13,10 +13,9 @@ import org.apache.spark.api.java.function.FilterFunction;
|
|||
import org.apache.spark.api.java.function.FlatMapFunction;
|
||||
import org.apache.spark.api.java.function.MapFunction;
|
||||
import org.apache.spark.api.java.function.MapGroupsFunction;
|
||||
import org.apache.spark.sql.*;
|
||||
import org.apache.spark.sql.Dataset;
|
||||
import org.apache.spark.sql.Encoders;
|
||||
import org.apache.spark.sql.SaveMode;
|
||||
import org.apache.spark.sql.SparkSession;
|
||||
import org.apache.spark.sql.types.StructType;
|
||||
import org.slf4j.Logger;
|
||||
import org.slf4j.LoggerFactory;
|
||||
|
||||
|
|
@ -24,6 +23,7 @@ import eu.dnetlib.dhp.application.ArgumentApplicationParser;
|
|||
import eu.dnetlib.dhp.oa.graph.dump.skgif.beans.EmitPerManifestation;
|
||||
import eu.dnetlib.dhp.oa.graph.dump.skgif.beans.EncloseMinElement;
|
||||
import eu.dnetlib.dhp.schema.common.EntityType;
|
||||
import eu.dnetlib.dhp.schema.common.ModelConstants;
|
||||
import eu.dnetlib.dhp.schema.common.ModelSupport;
|
||||
import eu.dnetlib.dhp.schema.oaf.*;
|
||||
import eu.dnetlib.dhp.schema.oaf.Datasource;
|
||||
|
|
@ -83,16 +83,17 @@ public class EmitFromEntities implements Serializable {
|
|||
Utils
|
||||
.readPath(spark, inputPath + "project", Project.class)
|
||||
.filter((FilterFunction<Project>) p -> !p.getDataInfo().getDeletedbyinference())
|
||||
.map((MapFunction<Project, EncloseMinElement>) p -> {
|
||||
EncloseMinElement eme = new EncloseMinElement();
|
||||
eme.setEnclosedEntityId(p.getId());
|
||||
eme.setMinGrant(Utils.getMinGrant(p));
|
||||
return eme;
|
||||
}, Encoders.bean(EncloseMinElement.class))
|
||||
.map((MapFunction<Project, MinGrant>) p -> {
|
||||
// EncloseMinElement eme = new EncloseMinElement();
|
||||
// eme.setEnclosedEntityId(p.getId());
|
||||
// eme.setMinGrant(Utils.getMinGrant(p));
|
||||
// return eme;
|
||||
return Utils.getMinGrant(p);
|
||||
}, Encoders.bean(MinGrant.class))
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.mode(SaveMode.Overwrite)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "/minEntity");
|
||||
.json(workingDir + "/minGrant");
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -100,39 +101,56 @@ public class EmitFromEntities implements Serializable {
|
|||
Utils
|
||||
.readPath(spark, inputPath + "organization", Organization.class)
|
||||
.filter((FilterFunction<Organization>) o -> !o.getDataInfo().getDeletedbyinference())
|
||||
.map((MapFunction<Organization, EncloseMinElement>) o -> {
|
||||
EncloseMinElement eme = new EncloseMinElement();
|
||||
eme.setMinOrganization(Utils.getMinOrganization(o));
|
||||
eme.setEnclosedEntityId(o.getId());
|
||||
return eme;
|
||||
},
|
||||
Encoders.bean(EncloseMinElement.class))
|
||||
.map(
|
||||
(MapFunction<Organization, MinOrganization>) o -> {
|
||||
// EncloseMinElement eme = new EncloseMinElement();
|
||||
return Utils.getMinOrganization(o);
|
||||
// eme.setMinOrganization(Utils.getMinOrganization(o));
|
||||
// eme.setEnclosedEntityId(o.getId());
|
||||
// return eme;
|
||||
},
|
||||
Encoders.bean(MinOrganization.class))
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.mode(SaveMode.Overwrite)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "/minEntity");
|
||||
.json(workingDir + "/minOrganization");
|
||||
}
|
||||
|
||||
private static void emitFromDatasource(SparkSession spark, String inputPath, String workingDir) {
|
||||
// StructType daSchema = StructType
|
||||
// .fromDDL("`id` STRING, `dataInfo` STRUCT<`deletedbyinference`:BOOLEAN,`invisible`:BOOLEAN>, `officialname` STRUCT<`value` STRING>");
|
||||
// spark
|
||||
// .read()
|
||||
// .schema(Encoders.bean(Datasource.class).schema())
|
||||
// .json(inputPath + "datasource")
|
||||
// .filter(
|
||||
// "dataInfo.deletedbyinference != true " )
|
||||
// .selectExpr("id as enclosedEntity", "<id as local_identifier> as minDatasource" )
|
||||
// .select(new Column("id").as("minDatasource.local_identifier"), new Column("officialname.value").as("minDatasource.name"), new Column("id").as("enclosedEntityId"))
|
||||
// .write()
|
||||
// .mode(SaveMode.Append)
|
||||
// .option("compression","gzip")
|
||||
// .json(workingDir + "/minEntity");
|
||||
|
||||
Utils
|
||||
.readPath(spark, inputPath + "datasource", Datasource.class)
|
||||
.filter((FilterFunction<Datasource>) d -> !d.getDataInfo().getDeletedbyinference())
|
||||
.map((MapFunction<Datasource, EncloseMinElement>) d -> {
|
||||
EncloseMinElement eme = new EncloseMinElement();
|
||||
eme
|
||||
.setMinDatsource(
|
||||
MinVenue
|
||||
.newInstance(
|
||||
// Utils.getIdentifier(Prefixes.DATASOURCE, d.getId()),
|
||||
// d.getOfficialname().getValue()));
|
||||
d.getId(), d.getOfficialname().getValue()));
|
||||
eme.setEnclosedEntityId(d.getId());
|
||||
return eme;
|
||||
}, Encoders.bean(EncloseMinElement.class))
|
||||
.map((MapFunction<Datasource, MinVenue>) d -> {
|
||||
// EncloseMinElement eme = new EncloseMinElement();
|
||||
// eme
|
||||
// .setMinDatsource(
|
||||
return MinVenue
|
||||
.newInstance(
|
||||
// Utils.getIdentifier(Prefixes.DATASOURCE, d.getId()),
|
||||
// d.getOfficialname().getValue()));
|
||||
d.getId(), d.getOfficialname().getValue());// );
|
||||
// eme.setEnclosedEntityId(d.getId());
|
||||
// return eme;
|
||||
}, Encoders.bean(MinVenue.class))
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.mode(SaveMode.Overwrite)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "/minEntity");
|
||||
.json(workingDir + "/minDatasource");
|
||||
|
||||
Utils
|
||||
.readPath(spark, inputPath + "datasource", Datasource.class)
|
||||
|
|
@ -142,36 +160,36 @@ public class EmitFromEntities implements Serializable {
|
|||
.getEoscdatasourcetype()
|
||||
.getClassid()
|
||||
.equalsIgnoreCase("Journal archive"))
|
||||
.map((MapFunction<Datasource, EncloseMinElement>) d -> {
|
||||
EncloseMinElement eme = new EncloseMinElement();
|
||||
eme.setEnclosedEntityId(d.getId());
|
||||
.map((MapFunction<Datasource, MinVenue>) d -> {
|
||||
// EncloseMinElement eme = new EncloseMinElement();
|
||||
// eme.setEnclosedEntityId(d.getId());
|
||||
if (Optional.ofNullable(d.getJournal()).isPresent() &&
|
||||
Optional.ofNullable(d.getJournal().getIssnPrinted()).isPresent()) {
|
||||
eme
|
||||
.setMinVenue(
|
||||
MinVenue
|
||||
.newInstance(
|
||||
Utils.getIdentifier(Prefixes.VENUE, d.getJournal().getIssnPrinted()),
|
||||
d.getOfficialname().getValue()));
|
||||
return eme;
|
||||
// eme
|
||||
// .setMinVenue(
|
||||
return MinVenue
|
||||
.newInstance(
|
||||
Utils.getIdentifier(Prefixes.VENUE, d.getJournal().getIssnPrinted()),
|
||||
d.getOfficialname().getValue());// );
|
||||
// return eme;
|
||||
}
|
||||
if (Optional.ofNullable(d.getJournal()).isPresent() &&
|
||||
Optional.ofNullable(d.getJournal().getIssnOnline()).isPresent()) {
|
||||
eme
|
||||
.setMinVenue(
|
||||
MinVenue
|
||||
.newInstance(
|
||||
Utils.getIdentifier(Prefixes.VENUE, d.getJournal().getIssnOnline()),
|
||||
d.getOfficialname().getValue()));
|
||||
return eme;
|
||||
// eme
|
||||
// .setMinVenue(
|
||||
return MinVenue
|
||||
.newInstance(
|
||||
Utils.getIdentifier(Prefixes.VENUE, d.getJournal().getIssnOnline()),
|
||||
d.getOfficialname().getValue());// );
|
||||
// return eme;
|
||||
}
|
||||
return null;
|
||||
}, Encoders.bean(EncloseMinElement.class))
|
||||
}, Encoders.bean(MinVenue.class))
|
||||
.filter(Objects::nonNull)
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.mode(SaveMode.Overwrite)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "/minEntity");
|
||||
.json(workingDir + "/minVenue");
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -186,23 +204,20 @@ public class EmitFromEntities implements Serializable {
|
|||
}
|
||||
|
||||
private static <R extends Result> void emitMinProduct(SparkSession spark, String inputPath, String workingDir) {
|
||||
Utils.removeOutputDir(spark, workingDir + "minEntity");
|
||||
Utils.removeOutputDir(spark, workingDir + "minProduct");
|
||||
ModelSupport.entityTypes.keySet().forEach(e -> {
|
||||
if (ModelSupport.isResult(e)) {
|
||||
Class<R> resultClazz = ModelSupport.entityTypes.get(e);
|
||||
|
||||
Utils
|
||||
.readPath(spark, inputPath + e.name(), resultClazz)
|
||||
.map((MapFunction<R, EncloseMinElement>) p -> {
|
||||
EncloseMinElement eme = new EncloseMinElement();
|
||||
eme.setMinProduct(Utils.getMinProduct(p));
|
||||
eme.setEnclosedEntityId(p.getId());
|
||||
return eme;
|
||||
}, Encoders.bean(EncloseMinElement.class))
|
||||
.map(
|
||||
(MapFunction<R, MinProduct>) p -> Utils.getMinProduct(p),
|
||||
Encoders.bean(MinProduct.class))
|
||||
.write()
|
||||
.mode(SaveMode.Append)
|
||||
.option("compression", "gzip")
|
||||
.json(workingDir + "/minEntity");
|
||||
.json(workingDir + "/minProduct");
|
||||
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -155,6 +155,9 @@ public class Utils implements Serializable {
|
|||
case "arxiv":
|
||||
mp.setArxivid(pid.getValue());
|
||||
break;
|
||||
case "pmid":
|
||||
mp.setPmid(pid.getValue());
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -85,4 +85,5 @@ public class EncloseMinElement implements Serializable {
|
|||
public void setMinProduct(MinProduct minProduct) {
|
||||
this.minProduct = minProduct;
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@
|
|||
</property>
|
||||
</configuration>
|
||||
</global>
|
||||
<start to="select_relevant_graph_subset"/>
|
||||
<start to="dump_result"/>
|
||||
|
||||
|
||||
<decision name="filter_set">
|
||||
|
|
@ -218,19 +218,90 @@
|
|||
<arg>--filterPath</arg><arg>${filterPath}</arg>
|
||||
</spark>
|
||||
<!-- <ok to="emit_from_result"/>-->
|
||||
<ok to="copy_graph_subset"/>
|
||||
<ok to="copy_graph_publication"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_subset">
|
||||
<action name="copy_graph_publication">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/*</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/</arg>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/publication</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/publication</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_dataset"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_dataset">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/dataset</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/dataset</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_otherresearchproduct"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_otherresearchproduct">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/otherresearchproduct</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/otherresearchproduct</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_software"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_software">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/software</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/software</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_datasource"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_datasource">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/datasource</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/datasource</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_project"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_project">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/project</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/project</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_organization"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_organization">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/organization</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/organization</arg>
|
||||
</distcp>
|
||||
<ok to="copy_graph_relation"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
<action name="copy_graph_relation">
|
||||
<distcp xmlns="uri:oozie:distcp-action:0.2">
|
||||
<job-tracker>${jobTracker}</job-tracker>
|
||||
<name-node>${nameNode}</name-node>
|
||||
<arg>${nameNode}/user/miriam.baglioni/oa/graph/dump/temp/graph/relation</arg>
|
||||
<arg>${nameNode}/${workingDir}/graph/relation</arg>
|
||||
</distcp>
|
||||
<ok to="emit_from_result"/>
|
||||
<error to="Kill"/>
|
||||
</action>
|
||||
|
||||
<action name="emit_from_result">
|
||||
<spark xmlns="uri:oozie:spark-action:0.2">
|
||||
<master>yarn</master>
|
||||
|
|
@ -248,9 +319,11 @@
|
|||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
--conf spark.sql.warehouse.dir=${sparkSqlWarehouseDir}
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>
|
||||
<arg>--workingDir</arg><arg>${workingDir}/</arg>
|
||||
<!-- <arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>-->
|
||||
<!-- <arg>--workingDir</arg><arg>${workingDir}/</arg>-->
|
||||
<arg>--outputPath</arg><arg>${outputPath}</arg>
|
||||
<arg>--workingDir</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/</arg>
|
||||
<arg>--sourcePath</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/graph/</arg>
|
||||
</spark>
|
||||
<ok to="dump_result"/>
|
||||
<error to="Kill"/>
|
||||
|
|
@ -263,18 +336,22 @@
|
|||
<class>eu.dnetlib.dhp.oa.graph.dump.skgif.DumpResult</class>
|
||||
<jar>dump-${projectVersion}.jar</jar>
|
||||
<spark-opts>
|
||||
--executor-memory=${sparkExecutorMemory}
|
||||
--executor-cores=${sparkExecutorCores}
|
||||
--executor-cores=4
|
||||
--executor-memory=8G
|
||||
--driver-memory=${sparkDriverMemory}
|
||||
--conf spark.executor.memoryOverhead=5G
|
||||
--conf spark.extraListeners=${spark2ExtraListeners}
|
||||
--conf spark.sql.queryExecutionListeners=${spark2SqlQueryExecutionListeners}
|
||||
--conf spark.yarn.historyServer.address=${spark2YarnHistoryServerAddress}
|
||||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
--conf spark.sql.warehouse.dir=${sparkSqlWarehouseDir}
|
||||
--conf spark.sql.shuffle.partitions=15000
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>
|
||||
<!-- <arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>-->
|
||||
<arg>--sourcePath</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/graph/</arg>
|
||||
<arg>--outputPath</arg><arg>${outputPath}</arg>
|
||||
<arg>--workingDir</arg><arg>${workingDir}/</arg>
|
||||
<!-- <arg>--workingDir</arg><arg>${workingDir}/</arg>-->
|
||||
<arg>--workingDir</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/</arg>
|
||||
</spark>
|
||||
<ok to="dump_datasource"/>
|
||||
<error to="Kill"/>
|
||||
|
|
@ -296,9 +373,11 @@
|
|||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
--conf spark.sql.warehouse.dir=${sparkSqlWarehouseDir}
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>
|
||||
<!-- <arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>-->
|
||||
<arg>--sourcePath</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/graph/</arg>
|
||||
<arg>--outputPath</arg><arg>${outputPath}</arg>
|
||||
<arg>--workingDir</arg><arg>${workingDir}/</arg>
|
||||
<!-- <arg>--workingDir</arg><arg>${workingDir}/</arg>-->
|
||||
<arg>--workingDir</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/</arg>
|
||||
</spark>
|
||||
<ok to="dump_venue"/>
|
||||
<error to="Kill"/>
|
||||
|
|
@ -320,9 +399,11 @@
|
|||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
--conf spark.sql.warehouse.dir=${sparkSqlWarehouseDir}
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>
|
||||
<!-- <arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>-->
|
||||
<arg>--sourcePath</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/graph/</arg>
|
||||
<arg>--outputPath</arg><arg>${outputPath}</arg>
|
||||
<arg>--workingDir</arg><arg>${workingDir}/</arg>
|
||||
<!-- <arg>--workingDir</arg><arg>${workingDir}/</arg>-->
|
||||
<arg>--workingDir</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/</arg>
|
||||
</spark>
|
||||
<ok to="dump_organization"/>
|
||||
<error to="Kill"/>
|
||||
|
|
@ -345,9 +426,11 @@
|
|||
--conf spark.eventLog.dir=${nameNode}${spark2EventLogDir}
|
||||
--conf spark.sql.warehouse.dir=${sparkSqlWarehouseDir}
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>
|
||||
<!-- <arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>-->
|
||||
<arg>--sourcePath</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/graph/</arg>
|
||||
<arg>--outputPath</arg><arg>${outputPath}</arg>
|
||||
<arg>--workingDir</arg><arg>${workingDir}/</arg>
|
||||
<!-- <arg>--workingDir</arg><arg>${workingDir}/</arg>-->
|
||||
<arg>--workingDir</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/</arg>
|
||||
</spark>
|
||||
<ok to="dump_grant"/>
|
||||
<error to="Kill"/>
|
||||
|
|
@ -370,9 +453,11 @@
|
|||
--conf spark.sql.warehouse.dir=${sparkSqlWarehouseDir}
|
||||
|
||||
</spark-opts>
|
||||
<arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>
|
||||
<!-- <arg>--sourcePath</arg><arg>${workingDir}/graph/</arg>-->
|
||||
<arg>--sourcePath</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/graph/</arg>
|
||||
<arg>--outputPath</arg><arg>${outputPath}</arg>
|
||||
<arg>--workingDir</arg><arg>${workingDir}/</arg>
|
||||
<!-- <arg>--workingDir</arg><arg>${workingDir}/</arg>-->
|
||||
<arg>--workingDir</arg><arg>/user/miriam.baglioni/oa/graph/dump/temp/working_dir/</arg>
|
||||
</spark>
|
||||
<ok to="End"/>
|
||||
<error to="Kill"/>
|
||||
|
|
|
|||
|
|
@ -4,6 +4,8 @@ package eu.dnetlib.dhp.oa.graph.dump.skgif;
|
|||
import java.io.IOException;
|
||||
import java.nio.file.Files;
|
||||
import java.nio.file.Path;
|
||||
import java.util.ArrayList;
|
||||
import java.util.HashMap;
|
||||
import java.util.stream.Collectors;
|
||||
|
||||
import javax.validation.constraints.AssertTrue;
|
||||
|
|
@ -14,9 +16,14 @@ import org.apache.spark.api.java.JavaRDD;
|
|||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
import org.apache.spark.api.java.function.FilterFunction;
|
||||
import org.apache.spark.api.java.function.ForeachFunction;
|
||||
import org.apache.spark.api.java.function.MapFunction;
|
||||
import org.apache.spark.api.java.function.MapGroupsFunction;
|
||||
import org.apache.spark.sql.Dataset;
|
||||
import org.apache.spark.sql.Encoders;
|
||||
import org.apache.spark.sql.Row;
|
||||
import org.apache.spark.sql.SparkSession;
|
||||
import org.apache.spark.sql.types.DataTypes;
|
||||
import org.apache.spark.sql.types.StructType;
|
||||
import org.junit.jupiter.api.AfterAll;
|
||||
import org.junit.jupiter.api.Assertions;
|
||||
import org.junit.jupiter.api.BeforeAll;
|
||||
|
|
@ -28,6 +35,7 @@ import com.fasterxml.jackson.databind.ObjectMapper;
|
|||
|
||||
import eu.dnetlib.dhp.oa.graph.dump.skgif.beans.EmitPerManifestation;
|
||||
import eu.dnetlib.dhp.oa.graph.dump.skgif.beans.RelationPerProduct;
|
||||
import eu.dnetlib.dhp.oa.model.graph.Relation;
|
||||
import eu.dnetlib.dhp.skgif.model.*;
|
||||
import eu.dnetlib.dhp.utils.DHPUtils;
|
||||
|
||||
|
|
@ -489,6 +497,54 @@ public class DumpResultTest {
|
|||
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testEmitFromApi() throws Exception {
|
||||
final String sourcePath = getClass()
|
||||
.getResource("/eu/dnetlib/dhp/oa/graph/dump/skgif/graphForAPIExample/")
|
||||
.getPath();
|
||||
|
||||
final String workingDir = getClass()
|
||||
.getResource("/eu/dnetlib/dhp/oa/graph/dump/skgif/workingDirApiExample/")
|
||||
.getPath();
|
||||
|
||||
final StructType relationstructureSchema = new StructType()
|
||||
.add(
|
||||
"dataInfo", new StructType()
|
||||
.add("deletedbyinference", DataTypes.BooleanType))
|
||||
.add("source", DataTypes.StringType)
|
||||
.add("target", DataTypes.StringType)
|
||||
.add("relClass", DataTypes.StringType);
|
||||
|
||||
final StructType productSchema = new StructType()
|
||||
.add("local_identifier", DataTypes.StringType)
|
||||
.add("title", DataTypes.StringType)
|
||||
.add("doi", DataTypes.StringType)
|
||||
.add("pmcid", DataTypes.StringType)
|
||||
.add("arxivid", DataTypes.StringType)
|
||||
.add("pmid", DataTypes.StringType);
|
||||
|
||||
Dataset<Row> relation = spark
|
||||
.read()
|
||||
.schema(relationstructureSchema)
|
||||
.json(sourcePath + "relation")
|
||||
.filter(
|
||||
"datainfo.deletedbyinference != true and " +
|
||||
"relclass == '" + RelationType.CITATION.label + "'")
|
||||
.drop("dataInfo");
|
||||
|
||||
Dataset<Row> minProduct = spark
|
||||
.read()
|
||||
.schema(productSchema)
|
||||
.json(workingDir + "minProduct");
|
||||
relation
|
||||
.joinWith(
|
||||
minProduct, relation.col("target").equalTo(minProduct.col("local_identifier")))
|
||||
.selectExpr("_1.source as sourceResult", "_2.*")
|
||||
|
||||
.show(false);
|
||||
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testEmitFromApiDump() throws Exception {
|
||||
final String sourcePath = getClass()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,32 @@
|
|||
{"enclosedEntityId":"50|59796a29bd90::390b69a1e20d5af206fd6849c9b97d13","minProduct":{"local_identifier":"50|59796a29bd90::390b69a1e20d5af206fd6849c9b97d13","title":"Power to gas"}}
|
||||
{"enclosedEntityId":"50|59796a29bd90::6098357dcb0f21a492ae2689961268ef","minProduct":{"local_identifier":"50|59796a29bd90::6098357dcb0f21a492ae2689961268ef","title":"Vrije Tijd? Tijd voor Deventer!"}}
|
||||
{"enclosedEntityId":"50|59796a29bd90::d0545b3c7166358e16599aec2284b101","minProduct":{"local_identifier":"50|59796a29bd90::d0545b3c7166358e16599aec2284b101","title":"Sepsis at ICU admission does not decrease 30-day survival in very old patients"}}
|
||||
{"enclosedEntityId":"50|73490d0e0f82::1f1e664683ae0035b0d64487d1e1bcbe","minProduct":{"local_identifier":"50|73490d0e0f82::1f1e664683ae0035b0d64487d1e1bcbe","title":"The Effects of Membrane Physical Properties on Insulin Receptor Autophosphorylation and Signalling"}}
|
||||
{"enclosedEntityId":"50|73490d0e0f82::62d68e6a826109387d9b24d125dee5d1","minProduct":{"local_identifier":"50|73490d0e0f82::62d68e6a826109387d9b24d125dee5d1","title":"Protection of Migrant Agricultural Workers' Rights"}}
|
||||
{"enclosedEntityId":"50|CSC_________::288c3eab977e8b76a7abe31f989c9901","minProduct":{"local_identifier":"50|CSC_________::288c3eab977e8b76a7abe31f989c9901","title":"A fabric design for Armani Casa"}}
|
||||
{"enclosedEntityId":"50|CSC_________::710a52c4e2a9b3b697c682dd15595a4c","minProduct":{"local_identifier":"50|CSC_________::710a52c4e2a9b3b697c682dd15595a4c","title":"''Archpelago'' Apron, Navy : formgivning"}}
|
||||
{"enclosedEntityId":"50|CSC_________::7a3ea1f6a35d738fb193f6fae3303d32","minProduct":{"local_identifier":"50|CSC_________::7a3ea1f6a35d738fb193f6fae3303d32","title":"Ragna Róbertsdóttir | Configured Landscapes"}}
|
||||
{"enclosedEntityId":"50|RECOLECTA___::02e2efbf7bdfbdc708995bf8cb3c038b","minProduct":{"local_identifier":"50|RECOLECTA___::02e2efbf7bdfbdc708995bf8cb3c038b","title":"Por qué la educación a distancia (en línea)"}}
|
||||
{"enclosedEntityId":"50|RECOLECTA___::04e03c6d2756db9d75874947a7ef224c","minProduct":{"local_identifier":"50|RECOLECTA___::04e03c6d2756db9d75874947a7ef224c","title":"Sistemas recomendadores aplicados a productos de ropa"}}
|
||||
{"enclosedEntityId":"50|RECOLECTA___::05d59bf33bc35aeb975ca399180a051f","minProduct":{"local_identifier":"50|RECOLECTA___::05d59bf33bc35aeb975ca399180a051f","title":"Origen y desarrollo de los derechos fundamentales laborales en Venezuela (1830-Constitución de 1999) = Origin and development of fundamental labor rights in Venezuela (1830-1999 Constitution)"}}
|
||||
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{"local_identifier":"50|dedup_wf_002::b82b856ebd89cdd36b8107e475280e9e","title":"snmalloc software on GitHub"}
|
||||
{"doi":"10.24433/co.2868322.v1","local_identifier":"50|doi_________::07ca00c92c3093a76fcae84b2185ed85","title":"Image Quality Environment for FPGA Floating-Point Video Processing Library"}
|
||||
{"doi":"10.5281/zenodo.3724469","local_identifier":"50|doi_________::094b6aff40ccf64df47063a59ab83da1","title":"stonerlab/Stoner-PythonCode: v0.9.5 Bugfix and improvement release"}
|
||||
{"doi":"10.5281/zenodo.4763538","local_identifier":"50|doi_________::15b9e4e4e1102ae67e1b0e567a7593fc","title":"poliastro/poliastro: poliastro 0.15.0 (Earth edition)"}
|
||||
{"doi":"10.5281/zenodo.1251581","local_identifier":"50|doi_________::16be0dfa4fa679bed49d01fc4471cabd","title":"sotorrent/posthistory-extractor: Refactored release"}
|
||||
{"doi":"10.5281/zenodo.3979689","local_identifier":"50|doi_________::185d0f8b38dfeb9f567f30a3190a916c","title":"InsightSoftwareConsortium/ITKElastix: ITKElastix 0.6.3"}
|
||||
{"local_identifier":"50|__bioTools__::022faad7a573c4f12301c486815a3fa5","title":"petalNet"}
|
||||
{"local_identifier":"50|__bioTools__::0c42b0be275910c096d2bacaccff89ad","title":"Diversity Workbench"}
|
||||
{"local_identifier":"50|__bioTools__::58861f6ae561f40b585e44c0734b4a70","title":"HMCan"}
|
||||
{"local_identifier":"50|__bioTools__::85fb610b34dc395431f8eda781566c4e","title":"iAssembler"}
|
||||
{"local_identifier":"50|__bioTools__::97b678c1b70fe0b13197be61111a3513","title":"DNA Baser"}
|
||||
{"local_identifier":"50|__bioTools__::9961460fe6c9852c36bdbfea8b7fbf2c","title":"lncRNAdb"}
|
||||
{"local_identifier":"50|__bioTools__::c802825f3b3a94343e30a18ed23d4786","title":"Gene Set Builder"}
|
||||
{"local_identifier":"50|dedup_wf_002::629f6117216829e2582934ac1d845d9e","title":"Support tool for the overall assessment of animal welfare"}
|
||||
{"local_identifier":"50|dedup_wf_002::6ae16f2f40fa342f71a5e1a0a11eb962","title":"transforms3d software on GitHub"}
|
||||
{"local_identifier":"50|dedup_wf_002::b4f06f3b28fa9cd2d7b9185e3f6604eb","title":"varcmp software on GitHub"}
|
||||
{"local_identifier":"50|dedup_wf_002::e3373c97b277d3f0f3f6c21d688ae520","title":"OpenDSS-G software on SourceForge"}
|
||||
{"doi":"10.5281/zenodo.7254817","local_identifier":"50|doi_________::0abc081de6a2fe9506b20e8b58389cb2","title":"napari/magicgui: v0.6.0"}
|
||||
{"doi":"10.18129/b9.bioc.adam","local_identifier":"50|doi_________::0c6f89082f07ea5c737eaf25d0712e01","title":"ADAM"}
|
||||
{"doi":"10.5281/zenodo.573248","local_identifier":"50|doi_________::115332ff9a5e3d15a4c79bc3e819e165","title":"PeterMulhair/planets: First release of Science software"}
|
||||
{"doi":"10.5281/zenodo.50641","local_identifier":"50|doi_________::115a8028953b0488dbb04c0f99b9dd62","title":"libfreenect2: Release 0.2"}
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
{"local_identifier":"50|475c1990cbb2::100f1774e6061b0926892da5e93d6c9d","title":"CAGDB - NATGAM - Campbellton/Bathurst"}
|
||||
{"local_identifier":"50|475c1990cbb2::4646d85eae5eec4bf2f18fcbad21f57b","title":"Travel Activities and Motivation Survey, 2006"}
|
||||
{"local_identifier":"50|475c1990cbb2::4f36c055af3184c6216cc4eae5988556","title":"Centres d'artistes autogérés du Québec"}
|
||||
{"local_identifier":"50|475c1990cbb2::aaf64bbe32caceb6975d06961daa8755","title":"Profile for Statistical Area Classification, 2006 Census"}
|
||||
{"local_identifier":"50|475c1990cbb2::c3851713a5d4af37f222d512402b92ce","title":"Territorial provincial borders - 1M"}
|
||||
{"local_identifier":"50|475c1990cbb2::c857f2c92ae1fd825eb7d5ac4a49d528","title":"Lithoprobe AG Transect MT; 13 sites from AG pontiac; magnetotelluric"}
|
||||
{"local_identifier":"50|475c1990cbb2::d766f95e68bbbc591c182617ab1d8c17","title":"Cadastral Information for Chemawawin 1"}
|
||||
{"local_identifier":"50|475c1990cbb2::dc450205363661be28029a55eca89e21","title":"Bedrock geology compilation of the eastern Rackla belt, NTS 105N/15, 105N/16, 105O/13, 106B/4, 106C/1, 106C/2, east-central Yukon"}
|
||||
{"local_identifier":"50|475c1990cbb2::df09e45d51554cc803b2a83d1cde48bb","title":"Petroleum and Natural Gas Grid Unit"}
|
||||
{"local_identifier":"50|475c1990cbb2::dfc9ec4f6f0dd6b7b7319065b976cb6a","title":"Open Data Goal 3"}
|
||||
{"local_identifier":"50|475c1990cbb2::eab4efdde43a6e6755a5bb8f13819257","title":"Chronic Wasting Disease Survey of Caribou"}
|
||||
{"local_identifier":"50|RECOLECTA___::0c2e85681af5fcd303b56c26d0f1df8c","title":"Braque : 5 juillet-30 septembre 1980 : Fondation Maeght"}
|
||||
{"local_identifier":"50|_____OmicsDI::0faf5884d90f019521587b6f43a4f32c","title":"Mus musculus"}
|
||||
{"local_identifier":"50|_____OmicsDI::10c057b3c8b3c2ed707a115547ed27da","title":"ST5 MRSA in New Zealand"}
|
||||
{"local_identifier":"50|_____OmicsDI::10c733d1dd55d609a1fe24d9b85df85a","title":"Expression data from CtBP knockdown MCF-7 cells"}
|
||||
{"local_identifier":"50|_____OmicsDI::17cd78747bf733510ed811513532838c","title":"Notch pathway is overexpressed and is a therapeutic target in clear cell renal cancer"}
|
||||
{"local_identifier":"50|_____OmicsDI::1b52cbcc49ff394df4c7e1dcaaa9b171","title":"Homo sapiens"}
|
||||
{"local_identifier":"50|475c1990cbb2::2cc74f33f75b047c6194ae4fc56aa462","title":"Purchasing Card Expenditures - Ministry of Forests and Range - FYE11"}
|
||||
{"local_identifier":"50|RECOLECTA___::13d528828cfb560910b3a5d6c7bc0598","title":"Tanger: Place de France = Plaza de Francia = France's Place"}
|
||||
{"local_identifier":"50|RECOLECTA___::93c8f349e12876e593e24ac7ab25de5a","title":"Caja para cenizas de cocina baja"}
|
||||
{"local_identifier":"50|_____OmicsDI::04a55d471467fd107e560eeaea5e5d2b","title":"Dictyostelium intermedium"}
|
||||
{"local_identifier":"50|_____OmicsDI::060082611f4b9ee955d9ae70096bf5b8","title":"Anopheles melas strain:CM1002058"}
|
||||
{"local_identifier":"50|_____OmicsDI::06df8fca5361d17a5515a72982acd41b","title":"Mus musculus"}
|
||||
{"local_identifier":"50|_____OmicsDI::09c6e0e564c4b51d71724e79604f88ca","title":"alpha proteobacterium SCGC AAA298-K06"}
|
||||
{"local_identifier":"50|_____OmicsDI::0a95732def325605b25c71cda5e5089f","title":"Draft Genome Sequence of Burkholderia gladioli Strain UCD-UG_CHAPALOTE"}
|
||||
{"local_identifier":"50|_____OmicsDI::0d1703ba052d60c2acc74a97e75d33c3","title":"Pseudomonas sp. WW_13 strain:WW_13"}
|
||||
{"local_identifier":"50|_____OmicsDI::0e36ef668edffce1b2cb31d2ed27032c","title":"Vitis vinifera"}
|
||||
{"local_identifier":"50|_____OmicsDI::0e68778fcdd0b9560347598c0e9b4278","title":"Bacillus cereus 03BB102"}
|
||||
{"local_identifier":"50|_____OmicsDI::131469bb810daf1e4c1158eacdfe07fb","title":"The MEF2B Regulatory Network - Expression microarray data"}
|
||||
{"local_identifier":"50|_____OmicsDI::1c3313b790f4cea27f4d075525878871","title":"null"}
|
||||
{"local_identifier":"50|_____OmicsDI::21a36a20149a35a70d4beb358af18443","title":"The Paramecium Ezl1 protein mediates dual methylation of H3 lysines 9 and 27"}
|
||||
{"local_identifier":"50|_____OmicsDI::2298e7316cdc24a7ad529fafc9f5477b","title":"Deinococcus maricopensis"}
|
||||
{"local_identifier":"50|_____OmicsDI::36e02acd25698bd97082f438db5d4cf0","title":"Homo sapiens"}
|
||||
{"local_identifier":"50|_____OmicsDI::3b95d6d56f1866c4d00ab5d70d6dff56","title":"Puccinia graminis f. sp. tritici 06ND76C"}
|
||||
{"local_identifier":"50|_____OmicsDI::40255e6a0ce5be99ede6905950f48f33","title":"Mus musculus"}
|
||||
{"local_identifier":"50|_____OmicsDI::44046a700c424550af38a3eedf5e250b","title":"Nutritional effects by beta-carotene in lung in males and females of control mice versus BCMO knockout mice"}
|
||||
{"local_identifier":"50|_____OmicsDI::46fbcedddb9ad77014ef2e286015d84f","title":"null"}
|
||||
{"local_identifier":"50|_____OmicsDI::4a1cdf0609094d699f4c36f3605265e8","title":"Homo sapiens"}
|
||||
{"local_identifier":"50|_____OmicsDI::4bd8625a0db3e7f35b27c131d928a9c4","title":"POG_patient_31043 - samples"}
|
||||
{"local_identifier":"50|_____OmicsDI::4dd82a0bb25bc5d8e3ba6a32f430738c","title":"Solanum lycopersicum strain:TLB220"}
|
||||
{"local_identifier":"50|_____OmicsDI::52b13189a46d8a7148436757d0b2bee1","title":"Homo sapiens"}
|
||||
{"local_identifier":"50|_____OmicsDI::5a5e0168b632c354799e9ade6d23865b","title":"null"}
|
||||
{"local_identifier":"50|_____OmicsDI::6a7dd028c859df2e976d5406f76f8b0b","title":"freshwater metagenome"}
|
||||
{"local_identifier":"50|_____OmicsDI::6fa118bd596b943d802562bd3f8104aa","title":"Proliferating Langerhans cells dampen inflammation in established mouse psoriatic lesions"}
|
||||
{"local_identifier":"50|_____OmicsDI::710f99d06d7433275c1c123b3845480d","title":"Characterizing commonalities and differences between the breast and prostate cancer metabotypes in African-American cohorts"}
|
||||
{"local_identifier":"50|_____OmicsDI::71c6f3c958a052a3bc88ea6bee442ad1","title":"GNPS - Mouse BXD liver metabolomics"}
|
||||
{"local_identifier":"50|_____OmicsDI::7b4accbdae8e4782d6395b3b024641d8","title":"Neurospora crassa strain:2489x8790_390a"}
|
||||
{"local_identifier":"50|_____OmicsDI::7ee47638c285148bda6764f37b3a5537","title":"Homo sapiens"}
|
||||
{"local_identifier":"50|_____OmicsDI::89d0864591fabb3d90be26af88ba8081","title":"Salmonella enterica strain:SEH-16"}
|
||||
{"local_identifier":"50|_____OmicsDI::8cb322209941ef6e8b107b5fc5ca800c","title":"Methylobacter luteus IMV-B-3098"}
|
||||
{"local_identifier":"50|_____OmicsDI::8de599bd144ef54d1d954cb83274fa2f","title":"Homo sapiens"}
|
||||
{"local_identifier":"50|_____OmicsDI::94010edd576f861d025bf0aecf97d54e","title":"A regulatory CD9+ B cell subset controls HDM-induced allergic airway inflammation"}
|
||||
{"local_identifier":"50|_____OmicsDI::95f4b23d1e4410d6d3b192a03fd89692","title":"Bartonella bacilliformis"}
|
||||
{"local_identifier":"50|_____OmicsDI::9e9f2b443aaf8b637500d28b86122b6d","title":"Genetics of gene expression in primary human immune cells"}
|
||||
Loading…
Reference in New Issue