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Leibniz Institut für Sozialwissenschaften", "de" : "GESIS - Leibniz Institut für Sozialwissenschaften", "en" : "GESIS - Leibniz-Institute for the Social Sciences" } } ], "natureOfContents" : [ { "uri" : "info:searchapp/vocab/researchData", "label" : { "pref" : "researchData", "de" : "researchData", "en" : "researchData" } } ], "publications" : [ { "type" : [ "PublicationEvent" ], "startDate" : "2014" } ], "subjects" : [ { "type" : [ "SubjectHeading" ], "uri" : "info:searchapp/vocab/adhoc/TmF0dXJ3aXNzZW5zY2hhZnRlbiwgVGVjaG5payh3aXNzZW5zY2hhZnRlbiksIGFuZ2V3YW5kdGUgV2lzc2Vuc2NoYWZ0ZW4=", "notation" : "", "label" : { "pref" : "Naturwissenschaften, Technik(wissenschaften), angewandte Wissenschaften", "de" : "Naturwissenschaften, Technik(wissenschaften), angewandte Wissenschaften", "en" : "Science and Engineering, Applied Sciences" } } ], "title" : { "pref" : "Survey on Common Strategies regarding Vocabulary Reuse in Linked Open Data Modeling", "de" : "Survey on Common Strategies regarding Vocabulary Reuse in Linked Open Data Modeling", "en" : "Survey on Common Strategies regarding Vocabulary Reuse in Linked Open Data Modeling" }, "universe" : { "sampled" : { "pref" : "Linked Data experts and practitioners", "de" : "Linked Data experts and practitioners", "en" : "Linked Data experts and practitioners" } }, "uri" : "https://doi.org/10.7802/64", "searchappPublisher" : { "shortName" : "GESIS", "name" : { "de" : "GESIS - Leibniz Institut für Sozialwissenschaften", "en" : "GESIS - Leibniz Institute for the Social Sciences" }, "url" : "https://www.gesis.org/" } }},{"_index":"soda-2026-05-12-06-20-01","_id":"831.json","_score":1.0,"_ignored":["abstract.pref.raw","abstract.en.raw","abstract.de.raw"],"_source":{ "abstract" : { "pref" : "Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this.", "de" : "Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this.", "en" : "Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this." }, "collectionModes" : [ { "label" : { "pref" : "Computer-basierte Beobachtung", "de" : "Computer-basierte Beobachtung", "en" : "Computer-based observation" } }, { "label" : { "pref" : "Inhaltscodierung", "de" : "Inhaltscodierung", "en" : "Content Analysis" } } ], "contributions" : [ { "type" : [ "Contribution" ], "agent" : { "type" : [ "Person" ], "uri" : "info:searchapp/vocab/adhoc/WmVuay1Nw7ZsdGdlbiwgV29sZmdhbmc=", "label" : "Zenk-Möltgen, Wolfgang", "affiliation" : { "uri" : "info:searchapp/vocab/adhoc/R0VTSVMgLSBMZWlibml6IEluc3RpdHV0ZSBmb3IgdGhlIFNvY2lhbCBTY2llbmNlcw==", "label" : { "pref" : "GESIS - Leibniz Institute for the Social Sciences", "de" : "GESIS - Leibniz Institute for the Social Sciences", "en" : "GESIS - Leibniz Institute for the Social Sciences" } } }, "role" : [ ] }, { "type" : [ "Contribution" ], "agent" : { "type" : [ "Person" ], "uri" : "info:searchapp/vocab/adhoc/TGVwdGhpZW4sIEdyZXRh", "label" : "Lepthien, Greta", "affiliation" : { "uri" : "info:searchapp/vocab/adhoc/R0VTSVMgLSBMZWlibml6IEluc3RpdHV0ZSBmb3IgdGhlIFNvY2lhbCBTY2llbmNlcw==", "label" : { "pref" : "GESIS - Leibniz Institute for the Social Sciences", "de" : "GESIS - Leibniz Institute for the Social Sciences", "en" : "GESIS - Leibniz Institute for the Social Sciences" } } }, "role" : [ ] } ], "describedBy" : { "uri" : "https://api.vitrine.gesis.org/search/soda/_doc/831.json", "type" : "ResearchData", "modifiedBy" : { "uri" : "https://searchapp-indexer", "label" : { "pref" : "Searchapp Indexer" }, "dateCreated" : "2026-05-12T06:20:50.958+02:00", "dateModified" : "2026-05-12T06:20:50.958+02:00", "inDataset" : { "uri" : "https://git.gesis.org/datorium/soda-index-data" }, "resultOf" : { "type" : [ "CreateAction" ], "endTime" : "2026-05-12T06:20:50.958+02:00", "instrument" : { "uri" : "https://git.gesis.org/datorium/searchapp-indexer", "type" : [ "SoftwareApplication" ], "label" : "Searchapp Indexer" }, "srcObject" : { "uri" : "https://git.gesis.org/datorium/soda-index-data/-/blob/main/data/831.json", "type" : [ "DataFeedItem" ], "inDataset" : { "uri" : "https://git.gesis.org/datorium/soda-index-data", "label" : "raw data" } } } } }, "documentId" : "831", "handles" : [ { "type" : "DOI", "notation" : "10.7802/65", "url" : "https://doi.org/10.7802/65", "source" : { "uri" : "info:searchapp/vocab/soda", "label" : "SowiDataNet" } } ], "issuers" : [ { "uri" : "info:searchapp/vocab/adhoc/R0VTSVMgLSBMZWlibml6IEluc3RpdHV0IGbDvHIgU296aWFsd2lzc2Vuc2NoYWZ0ZW4=", "label" : { "pref" : "GESIS - Leibniz Institut für Sozialwissenschaften", "de" : "GESIS - Leibniz Institut für Sozialwissenschaften", "en" : "GESIS - Leibniz-Institute for the Social Sciences" } } ], "natureOfContents" : [ { "uri" : "info:searchapp/vocab/researchData", "label" : { "pref" : "researchData", "de" : "researchData", "en" : "researchData" } } ], "publications" : [ { "type" : [ "PublicationEvent" ], "startDate" : "2014" } ], "rights" : { "label" : { "pref" : "CC BY-SA 4.0: Namensnennung – Weitergabe unter gleichen Bedingungen (http://creativecommons.org/licenses/by-sa/4.0/deed.de)", "de" : "CC BY-SA 4.0: Namensnennung – Weitergabe unter gleichen Bedingungen (http://creativecommons.org/licenses/by-sa/4.0/deed.de)", "en" : "CC BY-SA 4.0: Namensnennung – Weitergabe unter gleichen Bedingungen (http://creativecommons.org/licenses/by-sa/4.0/deed.de)" } }, "sampling" : { "method" : { "pref" : "Full selection of the journals in the 2013 Social Science Citation Index in the category \"sociology\"; All articles from 5 selected journals in 2012 and 2013.", "de" : "Full selection of the journals in the 2013 Social Science Citation Index in the category \"sociology\"; All articles from 5 selected journals in 2012 and 2013.", "en" : "Full selection of the journals in the 2013 Social Science Citation Index in the category \"sociology\"; All articles from 5 selected journals in 2012 and 2013." } }, "subjects" : [ { "type" : [ "SubjectHeading" ], "uri" : "info:searchapp/vocab/adhoc/U296aW9sb2dpZQ==", "notation" : "", "label" : { "pref" : "Soziologie", "de" : "Soziologie", "en" : "Sociology" } }, { "type" : [ "SubjectHeading" ], "uri" : "info:searchapp/vocab/adhoc/c29uc3RpZ2UgQmVyZWljaGUgZGVyIGFuZ2V3YW5kdGVuIFNvemlhbHdpc3NlbnNjaGFmdGVu", "notation" : "", "label" : { "pref" : "sonstige Bereiche der angewandten Sozialwissenschaften", "de" : "sonstige Bereiche der angewandten Sozialwissenschaften", "en" : "Other Fields of the Applied Social Sciences" } }, { "type" : [ "SubjectHeading" ], "uri" : "info:searchapp/vocab/adhoc/SW5mb3JtYXRpb25zd2lzc2Vuc2NoYWZ0", "notation" : "", "label" : { "pref" : "Informationswissenschaft", "de" : "Informationswissenschaft", "en" : "Information Science" } } ], "temporalCoverages" : [ { "startDate" : "2012-01", "endDate" : "2013-12", "labels" : [ ] } ], "title" : { "pref" : "Data from: Data sharing in sociology journals", "de" : "Data from: Data sharing in sociology journals", "en" : "Data from: Data sharing in sociology journals" }, "universe" : { "sampled" : { "pref" : "Journals of the 2013 Social Science Citation Index;\r\nArticles from 5 selected journals in 2012 and 2013.", "de" : "Journals of the 2013 Social Science Citation Index;\r\nArticles from 5 selected journals in 2012 and 2013.", "en" : "Journals of the 2013 Social Science Citation Index;\r\nArticles from 5 selected journals in 2012 and 2013." } }, "uri" : "https://doi.org/10.7802/65", "searchappPublisher" : { "shortName" : "GESIS", "name" : { "de" : "GESIS - Leibniz Institut für Sozialwissenschaften", "en" : "GESIS - Leibniz Institute for the Social Sciences" }, "url" : "https://www.gesis.org/" }, "calculatedTemporalCoverage" : { "label" : "2012 - 2013", "dates" : [ "2012", "2013" ] }, "projectNotes" : { "pref" : "Journal websites and articles", "de" : "Journal websites and articles", "en" : "Journal websites and articles" } }},{"_index":"soda-2026-05-12-06-20-01","_id":"2469.json","_score":1.0,"_ignored":["abstract.pref.raw","abstract.en.raw","abstract.de.raw"],"_source":{ "abstract" : { "pref" : "ClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics. \n\nThe dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).  \n\nThe latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ . \n\nThe first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations.", "de" : "ClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics. \n\nThe dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).  \n\nThe latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ . \n\nThe first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations.", "en" : "ClaimsKG is a knowledge graph of metadata information for 59580 fact-checked claims scraped from 13 fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonised and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model and statistics. \n\nThe dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org).  \n\nThe latest release of ClaimsKG covers 59580 claims. The data was scraped till August, of 2022 containing claims published between the years 1996-2022 from 13 factchecking websites. The claim-review (fact checking) period for claims ranges between the year 1996 to 2022. Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. The dataset contains a total of 1371271 entities detected and referenced with DBpedia. More information, such as detailed statistics, query examples and a user-friendly interface to explore the knowledge graph is available at: https://data.gesis.org/claimskg/ . \n\nThe first two releases of ClaimsKG are hosted at Zenodo (https://doi.org/10.5281/zenodo.3518960), ClaimsKGV1.0 (published on 04.04.2019), ClaimsKGV2.0 (published on 01.09.2019). This latest release of ClaimsKG supersedes the previous versions as it contains all the claims from the previous versions together with additional claims as well as improved entity annotations." }, "collections" : [ "GESIS - Leibniz Institute for the Social Sciences (for GESIS staff)" ], "contributions" : [ { "type" : [ "Contribution" ], "agent" : { "type" : [ "Person" ], "uri" : "info:searchapp/vocab/adhoc/R2FuZ29wYWRoeWF5LCBTdXNtaXRh", "label" : "Gangopadhyay, Susmita", "affiliation" : { "uri" : "info:searchapp/vocab/adhoc/R0VTSVMgLSBMZWlibml6LUluc3RpdHV0IGbDvHIgU296aWFsd2lzc2Vuc2NoYWZ0ZW4=", "label" : { "pref" : "GESIS - Leibniz-Institut für Sozialwissenschaften", "de" : "GESIS - Leibniz-Institut für Sozialwissenschaften", "en" : "GESIS - Leibniz-Institut für Sozialwissenschaften" } } }, "role" : [ ] }, { "type" : [ "Contribution" ], "agent" : { "type" : [ "Person" ], "uri" : "info:searchapp/vocab/adhoc/Qm9sYW5kLCBLYXRhcmluYQ==", "label" : "Boland, Katarina", "affiliation" : { "uri" : "info:searchapp/vocab/adhoc/R0VTSVMgLSBMZWlibml6LUluc3RpdHV0IGbDvHIgU296aWFsd2lzc2Vuc2NoYWZ0ZW4=", "label" : { "pref" : "GESIS - 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It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.\n\nWe also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character (\"\\t\"). The following list indicate the feature indices:\n\n1. Tweet Id: Long.\n\n2. Username: String. Encrypted for privacy issues.\n\n3. Timestamp: Format ( \"EEE MMM dd HH:mm:ss Z yyyy\" ).\n\n4. #Followers: Integer.\n\n5. #Friends: Integer.\n\n6. #Retweets: Integer.\n\n7. #Favorites: Integer.\n\n8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char \";\". Also, each entity is separated by char \":\" in order to store \"original_text:annotated_entity:score;\". If FEL did not find any entities, we have stored \"null;\".\n\n9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char \" \". Positive sentiment was stored first and then negative sentiment (i.e. \"2 -1\").\n\n10. Mentions: String. If the tweet contains mentions, we remove the char \"@\" and concatenate the mentions with whitespace char \" \". If no mentions appear, we have stored \"null;\".\n\n11. Hashtags: String. If the tweet contains hashtags, we remove the char \"#\" and concatenate the hashtags with whitespace char \" \". If no hashtags appear, we have stored \"null;\".\n\n12. URLs: String: If the tweet contains URLs, we concatenate the URLs using \":-: \". If no URLs appear, we have stored \"null;\"\n\nTo extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.\n\nYou can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .", "de" : "TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.\n\nWe also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character (\"\\t\"). The following list indicate the feature indices:\n\n1. Tweet Id: Long.\n\n2. Username: String. Encrypted for privacy issues.\n\n3. Timestamp: Format ( \"EEE MMM dd HH:mm:ss Z yyyy\" ).\n\n4. #Followers: Integer.\n\n5. #Friends: Integer.\n\n6. #Retweets: Integer.\n\n7. #Favorites: Integer.\n\n8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char \";\". Also, each entity is separated by char \":\" in order to store \"original_text:annotated_entity:score;\". If FEL did not find any entities, we have stored \"null;\".\n\n9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char \" \". Positive sentiment was stored first and then negative sentiment (i.e. \"2 -1\").\n\n10. Mentions: String. If the tweet contains mentions, we remove the char \"@\" and concatenate the mentions with whitespace char \" \". If no mentions appear, we have stored \"null;\".\n\n11. Hashtags: String. If the tweet contains hashtags, we remove the char \"#\" and concatenate the hashtags with whitespace char \" \". If no hashtags appear, we have stored \"null;\".\n\n12. URLs: String: If the tweet contains URLs, we concatenate the URLs using \":-: \". If no URLs appear, we have stored \"null;\"\n\nTo extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.\n\nYou can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .", "en" : "TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. 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