Find research datasets worth reusing

opencc-by-4.0Dec 2018View details →

Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction

This dataset supplements the article “<a href="https://doi.org/10.1162/COLI_a_00354">Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</a>” published in the Computational Linguistics journal: <ul> <li> <code>watset-coli-lcc-performance.tsv</code>: runtime analysis </li> <li> <code>watset-coli-synsets.zip</code>: synset induction experiment (note that <code>pairwise-{en-babelnet,ru-rwn}.pkl</code> files are excluded due to the licensing issues) </li> <li> <code>watset-coli-triframes.zip</code>: semantic frame induction experiment </li> <li> <code>watset-coli-classes.zip</code>: semantic class induction experiment </li> </ul>

opencc-zeroJul 2019View details →

Datasets for Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs

Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs This are intermediary datasets used for the calculation of the Class Completeness Estimators on Wikidata. For more information see: https://github.com/eXascaleInfolab/cardinal/ edits_wikidatawiki-20181001-pages.csv This is an extract from wikidatawiki-20181001-pages-meta-history (All pages with complete page edit history (.bz2)) found at <a href="https://dumps.wikimedia.org/wikidatawiki/">https://dumps.wikimedia.org/wikidatawiki/</a>. The extract was created by the following SQL query: <pre> SELECT page_title, rev_comment, rev_user_text, rev_timestamp FROM revisions WHERE rev_comment LIKE '%[[Property:%]]%[[Q%' ORDER BY rev_id INTO OUTFILE 'edits_wikidatawiki-20181001-pages.csv'; </pre>   wikidata-20180813-all.json.bz2.universe.noattr.gt.bz2 This is a graph-tool representation of the WikiData graph. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py">https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py</a>. observations_wikidatawiki-20181001-pages.pickle Extracted observations. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py">https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py</a>.   estimates_wikidatawiki-20181001-pages.pickle Extracted estimates. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py">https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py</a>   results_wikidatawiki-20181001-pages.pickle  Results. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py">https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py</a>

opencc-by-4.0Jan 2024View details →

OpenAIRE Graph: dataset for research community in Virtual Human Twins

This dataset contains metadata records of publications, research data, software and projects relevant for the research community in Virtual Twins in health. The dump contains the records available in the <a href="https://dth.openaire.eu/" target="_blank" rel="noopener">OpenAIRE Gateway on Digital Twins in Health</a> of the <a href="https://www.edith-csa.eu/" target="_blank" rel="noopener">EDITH CSA project </a>of the European Commission (grant agreement n. 101083771). Records are identified via full-text mining and inference techniques applied to the <a href="https://graph.openaire.eu/">OpenAIRE Graph</a>. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating thousands of scholarly data sources world-wide. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.10519297">https://doi.org/10.5281/zenodo.10519297</a>.

Dataset Graph 1: Heiner Müller's Publications 1960–1998.

Dataset for Graph 1. Data collected based on the Heiner Müller Werkausgabe. Graph 1 depicts Müller’s publication record regarding his public-oriented writings from the early stages of his career in the 1960s until two years after his death in 1998.

Dataset Graph 2: Publication Type and Venue of Heiner Müller's Writings

Dataset for Graph 2. Data collected based on the Heiner Müller Werkausgabe. Graph 2 shows the different publication types and publication venues that Heiner Müller used to publish his writings (essays, articles, speeches, etc.) from 1968 until 1998.

Dataset Graph 8: Heiner Müller's Interview Partners

Dataset for Graph 8. Data collected based on the Heiner Müller Werkausgabe. Graph 8 shows Heiner Müller's interview partners and the years in which his interviews were published.

Dataset Graph 9: TV and Radio Broadcasts of Heiner Müller's Interviews.

Dataset for Graph 9. Data collected based on the Heiner Müller Werkausgabe. Graph 9 shows when and where Heiner Müller's interviews were initially broadcasted and with whom he recorded the respective interview.

opencc-by-4.0Dec 2022View details →

Dataset: an overview of knowledge graphs in NFDI

This dataset contains a list of knowledge graphs (KGs), KG software, KG publications and KG use cases in context of NFDI (German National Research Data Infrastructure). The related manuscript is submitted to a poster session at the 1st Conference on Research Data Infrastructure - Connecting Communities, 12. – 14. September 2023, Karlsruhe, Germany: 'Who is using Knowledge Graphs in NFDI? An overview by the Working Group "Knowledge Graphs"'.

opencc-by-4.0Aug 2023View details →

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"

Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks" URL: https://github.com/qianghuangwhu/benchtemp Openreview: https://openreview.net/forum?id=rnZm2vQq31 There are 19 (15+4)  benchmark temporal graph datasets: reddit, wikipedia, mooc, lastfm, enron, SocialEvo, uci, CollegeMsg, TaobaoSmall, CanParl, Contacts, Flights, UNtrade, USLegis, UNvote, DGraphFin, TaobaoLarge, YoutubeReddit, YoutubeRedditLarge   Each dataset has three files: 1. ml_{data_name}.csv - the csv file of the Temporal Graph. This file have five columns with properties: 'u': the id of the user. 'i': the id of the item. 'ts': the timestamp of the interaction (edge) between the user and the item. 'label': the label of the interaction (edge). 'idx': the index of the interaction (edge). For example: ,u,i,ts,label,idx 0,1,2,0.0,0.0,1 1,1,3,0.0,0.0,2 2,1,4,0.0,0.0,3 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph.. 3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.

opencc-by-4.0Feb 2020View details →

A dataset based on two graph coverage criteria: prime-path and edge coverage

This repository contains 462 instances from 6 projects. The dataset structure contains 43 columns, in which 18 columns are the source code metrics of the application methods under test, 18 columns are the source code metrics of test methods, and seven columns are the test case metrics.

opencc-by-4.0Jun 2019View details →

A biodiversity dataset graph: Biodiverity Heritage Library (BHL)

A biodiversity dataset graph: BHL The intended use of this archive is to facilitate (meta-)analysis of the Biodiversity Heritage Library (BHL). The Biodiversity Heritage Library improves research methodology by collaboratively making biodiversity literature openly available to the world as part of a global biodiversity community. This dataset provides versioned snapshots of the BHL network as tracked by Preston [2] between 2019-05-19 and 2020-05-09 using "preston update -u https://biodiversitylibrary.org". The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the BHL content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 .   To retrieve and verify the downloaded BHL biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using: $ java -jar preston.jar clone --remote https://zenodo.org/record/3849560/files After that, verify the index of the archive by reproducing the following provenance log history: $ java -jar preston.jar history <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> . <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> <http://purl.org/pav/previousVersion> <hash://sha256/89926f33157c0ef057b6de73f6c8be0060353887b47db251bfd28222f2fd801a> . <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> <http://purl.org/pav/previousVersion> <hash://sha256/41b19aa9456fc709de1d09d7a59c87253bc1f86b68289024b7320cef78b3e3a4> . <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> <http://purl.org/pav/previousVersion> <hash://sha256/7582d5ba23e0d498ca4f55c29408c477d0d92b4fdcea139e8666f4d78c78a525> . <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> <http://purl.org/pav/previousVersion> <hash://sha256/a70774061ccded1a45389b9e6063eb3abab3d42813aa812391f98594e7e26687> . <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> <http://purl.org/pav/previousVersion> <hash://sha256/007e065ba4b99867751d688754aa3d33fa96e6e03133a2097e8a368d613cd93a> . <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> <http://purl.org/pav/previousVersion> <hash://sha256/4fb4b4d8f1ae2961311fb0080e817adb2faa746e7eae15249a3772fbe2d662a1> . <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> <http://purl.org/pav/previousVersion> <hash://sha256/67cc329e74fd669945f503917fbb942784915ab7810ddc41105a82ebe6af5482> . <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> <http://purl.org/pav/previousVersion> <hash://sha256/e46cd4b0d7fdb51ea789fa3c5f7b73591aca62d2d8f913346d71aa6cf0745c9f> . <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> <http://purl.org/pav/previousVersion> <hash://sha256/9215d543418a80510e78d35a0cfd7939cc59f0143d81893ac455034b5e96150a> . <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> <http://purl.org/pav/previousVersion> <hash://sha256/1448656cc9f339b4911243d7c12f3ba5366b54fff3513640306682c50f13223d> . <hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1> <http://purl.org/pav/previousVersion> <hash://sha256/7ee6b16b7a5e9b364776427d740332d8552adf5041d48018eeb3c0e13ccebf27> . To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while. $ java -jar preston.jar verify hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca    file:/home/preston/preston-bhl/data/e0/c1/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca    OK    CONTENT_PRESENT_VALID_HASH    49458087    hash://sha256/e0c131ebf6ad2dce71ab9a10aa116dcedb219ae4539f9e5bf0e57b84f51f22ca hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99    file:/home/preston/preston-bhl/data/1a/57/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99    OK    CONTENT_PRESENT_VALID_HASH    25745    hash://sha256/1a57e55a780b86cff38697cf1b857751ab7b389973d35113564fe5a9a58d6a99 hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c    file:/home/preston/preston-bhl/data/85/ef/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c    OK    CONTENT_PRESENT_VALID_HASH    519892    hash://sha256/85efeb84c1b9f5f45c7a106dd1b5de43a31b3248a211675441ff584a7154b61c hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743    file:/home/preston/preston-bhl/data/25/1e/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743    OK    CONTENT_PRESENT_VALID_HASH    787414    hash://sha256/251e5032afce4f1e44bfdc5a8f0316ca1b317e8af41bdbf88163ab5bd2b52743 Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". Files in this data publication: --- start of file descriptions --- -- description of archive and its contents (this file) -- README -- executable java jar containing preston[2] v0.1.15. -- preston.jar -- preston archives containing BHL data files, associated provenance logs and a provenance index -- preston-[00-ff].tar.gz -- individual provenance index files -- 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a 2b1104cb7749e818c9afca78391b2d0099bbb0a32f2b348860a335cd2f8f6800 4081bc59dff58d63f6a86c623cb770f01e9a355a42495b205bcb538cd526190f 47a2816f8b5600b24487093adcddfea12434cc4f270f3ab09d9215fbdd546cd2 6f99a1388823fca745c9e22ac21e2da909a219aa1ace55170fa9248c0276903c 7ae46d7cd9b5a0f5889ba38bac53c82e591b0bdf8b605f5e48c0dce8fb7b717f 82903464889fea7c53f53daedf4e41fa31092f82619edeb3415eb2b473f74af3 9e8c86243df39dd4fe82a3f814710eccf73aa9291d050415408e346fa2b09e70 a8308fbf4530e287927c471d881ce0fc852f16543d46e1ee26f1caba48815f3a bcec6df2ea7f74e9a6e2830d0072e6b2fbe65323d9ddb022dd6e1349c23996e2 cfe47c25ec0210ac73c06b407beb20d9c58355cb15bae427fdc7541870ca2e4e f73fc9e70bce8f21f0c96b8ef0903749d8f223f71343ab5a8910968f99c9b8b6 --- end of file descriptions --- References [1] Biodiversity Heritage Library (BHL, https://biodiversitylibrary.org) accessed from 2019-05-19 to 2020-05-09 with provenance hash://sha256/34ccd7cf7f4a1ea35ac6ae26a458bb603b2f6ee8ad36e1a58aa0261105d630b1. [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

opencc-zeroNov 2018View details →

A biodiversity dataset graph: DataONE

A biodiversity dataset graph: DataONE The intended use of this archive is to facilitate (meta-)analysis of the Data Observation Network for Earth (DataONE). DataONE is a distributed infrastructure that provides information about earth observation data. This dataset provides versioned snapshots of the DataONE network as tracked by Preston [2] between 2018-11-06 and 2020-05-07 using "preston update -u https://dataone.org". The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. In addition, index files have been individually included in this dataset publication to facilitate remote access. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the DataONE content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 .   To retrieve and verify the downloaded DataONE biodiversity dataset graph, first concatenate all the downloaded preston-*.tar.gz files (e.g., cat preston-*.tar.gz > preston.tar.gz). Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using: $ java -jar preston.jar clone --remote https://zenodo.org/record/3849494/files After that, verify the index of the archive by reproducing the following provenance log history: $ java -jar preston.jar history <0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f> . <hash://sha256/3ed3acaca7ac57f546d0b8877c1927ab5e08c23eccaa8219600c59c77a72c685> <http://purl.org/pav/previousVersion> <hash://sha256/8c67e0741d1c90db54740e08d2e39d91dfd73566ea69c1f2da0d9ab9780a9a9f> . <hash://sha256/857753997a7595a1b372b05641b58a25d9408b7ff08d557ce1fe8b73e4bd383f> <http://purl.org/pav/previousVersion> <hash://sha256/3ed3acaca7ac57f546d0b8877c1927ab5e08c23eccaa8219600c59c77a72c685> . <hash://sha256/7ee0376f4c3f7aeeda36927a5211395e5da8201e810e8c7e638a0fe23d001e88> <http://purl.org/pav/previousVersion> <hash://sha256/857753997a7595a1b372b05641b58a25d9408b7ff08d557ce1fe8b73e4bd383f> . <hash://sha256/68b4974d8ab7c4c7a7a4305065839b60ba460aaa862590b34c67877738feba90> <http://purl.org/pav/previousVersion> <hash://sha256/7ee0376f4c3f7aeeda36927a5211395e5da8201e810e8c7e638a0fe23d001e88> . <hash://sha256/060a76d56255bf9482c951748c91291fddeeb20f180632132be1344e081b2372> <http://purl.org/pav/previousVersion> <hash://sha256/68b4974d8ab7c4c7a7a4305065839b60ba460aaa862590b34c67877738feba90> . <hash://sha256/29357bdfab4548025f8a5743301f5c3c9146fa436c39e3c9e019fb9409ac9c42> <http://purl.org/pav/previousVersion> <hash://sha256/060a76d56255bf9482c951748c91291fddeeb20f180632132be1344e081b2372> . <hash://sha256/3669cd95100d1d533eb8953ff4ec5092cbd8addb8879b3e6262191148a8a3ebb> <http://purl.org/pav/previousVersion> <hash://sha256/29357bdfab4548025f8a5743301f5c3c9146fa436c39e3c9e019fb9409ac9c42> . <hash://sha256/8dc1663299359d271cb1b4c14ad521d0f1be67743689dd18016543dc1e097efb> <http://purl.org/pav/previousVersion> <hash://sha256/3669cd95100d1d533eb8953ff4ec5092cbd8addb8879b3e6262191148a8a3ebb> . <hash://sha256/dc4903e8afee651db1d9bf509f20503bf9c8e89679c4bcffb46d5b97440cb6de> <http://purl.org/pav/previousVersion> <hash://sha256/8dc1663299359d271cb1b4c14ad521d0f1be67743689dd18016543dc1e097efb> . <hash://sha256/f3bed9db3092c744604df5f50248a2ec36e564fe78a65f45c4190283bd61c807> <http://purl.org/pav/previousVersion> <hash://sha256/dc4903e8afee651db1d9bf509f20503bf9c8e89679c4bcffb46d5b97440cb6de> . <hash://sha256/e3c7b3b14b2b792e3e2e560a1b2bef059ac93f777dee616b836317bc9cbfcbf7> <http://purl.org/pav/previousVersion> <hash://sha256/f3bed9db3092c744604df5f50248a2ec36e564fe78a65f45c4190283bd61c807> . <hash://sha256/631a4531e7bb052816d28454bbeec3428d5e7bfd1f148c4f21ce63a6cf86c650> <http://purl.org/pav/previousVersion> <hash://sha256/e3c7b3b14b2b792e3e2e560a1b2bef059ac93f777dee616b836317bc9cbfcbf7> . <hash://sha256/87de0898919d2212977a586965e930ae45bdd1366073591c808c208a635e2814> <http://purl.org/pav/previousVersion> <hash://sha256/631a4531e7bb052816d28454bbeec3428d5e7bfd1f148c4f21ce63a6cf86c650> . <hash://sha256/79ec3ee370a0d38311bc352af07a36380cd3aa04dc98154cf723bbc73d12ee77> <http://purl.org/pav/previousVersion> <hash://sha256/87de0898919d2212977a586965e930ae45bdd1366073591c808c208a635e2814> . <hash://sha256/e54b360a4ca84a4503e4c10a8a8cca062c130be7429c8fe6ea1e0e82fe113e12> <http://purl.org/pav/previousVersion> <hash://sha256/79ec3ee370a0d38311bc352af07a36380cd3aa04dc98154cf723bbc73d12ee77> . <hash://sha256/2910f784f84e112f124a56ce54bd06b76e510f90276629d2d144ce29e326d80f> <http://purl.org/pav/previousVersion> <hash://sha256/e54b360a4ca84a4503e4c10a8a8cca062c130be7429c8fe6ea1e0e82fe113e12> . <hash://sha256/bcb0bdff0689cfb06f586d057703e41d1c6ba409867232217081dd8cb5053c87> <http://purl.org/pav/previousVersion> <hash://sha256/2910f784f84e112f124a56ce54bd06b76e510f90276629d2d144ce29e326d80f> . <hash://sha256/a12f8c7fbf4fbfa71536c7e1b2614a35454dac6a7fe9e1cc0b4df41ab2269bef> <http://purl.org/pav/previousVersion> <hash://sha256/bcb0bdff0689cfb06f586d057703e41d1c6ba409867232217081dd8cb5053c87> . <hash://sha256/2b5c445f0b7b918c14a50de36e29a32854ed55f00d8639e09f58f049b85e50e3> <http://purl.org/pav/previousVersion> <hash://sha256/a12f8c7fbf4fbfa71536c7e1b2614a35454dac6a7fe9e1cc0b4df41ab2269bef> . To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while. $ java -jar preston.jar verify hash://sha256/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945    file:/home/preston/preston-dataone/data/e5/5c/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945    OK    CONTENT_PRESENT_VALID_HASH    21580    hash://sha256/e55c1034d985740926564e94decd6dc7a70f779a33e7deb931553739cda16945 hash://sha256/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f    file:/home/preston/preston-dataone/data/d0/dd/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f    OK    CONTENT_PRESENT_VALID_HASH    2035    hash://sha256/d0ddcc2111b6134a570bcc7d89375920ef4d754130cecc0727c79d2b05a9f81f hash://sha256/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53    file:/home/preston/preston-dataone/data/47/2d/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53    OK    CONTENT_PRESENT_VALID_HASH    1935    hash://sha256/472de9d1c9fd7e044aac409abfbfff9f12c6b69359df995d431009580ffb0f53 hash://sha256/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687    file:/home/preston/preston-dataone/data/b2/98/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687    OK    CONTENT_PRESENT_VALID_HASH    1553    hash://sha256/b29879462cd43862129c5cf9b149c41ecd33ffef284a4dbea4ac1c0f90108687 Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". Files in this data publication: --- start of file descriptions --- -- description of archive and its contents (this file) -- README -- executable java jar containing preston[2] v0.1.15. -- preston.jar -- preston archives containing DataONE data files, associated provenance logs and a provenance index -- preston-[00-ff].tar.gz -- individual provenance index files -- 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a 2aecaf289def0e23a27058bf7715f226ef9189905f0be13228174825633125cf 2f65ae542401d4c2daf1bca70de640211da6749188f67d28ea71acd7d8ba070b 35eb1e17e2bf3e71212cde35bdb03e8a6545a57483ea3c1633929257b70cf637 3d38b70198e448674be6a63d14b9817f3a956f48bba7418fa7baa086a56c05b7 66ad3e5e904740f1e835ac6718dda4279e0c24b204ea0d1113cda1352a5072ba 7466a35e42dea7e2be068060ec0c926f9a8686388ed504ef5c6c990c1ba4e8d0 81161d9746c2a5823641c436e773fb4508516b055da85f4494b38c545349da39 8bf062872ce958545d361e9d53a552ffb025ac29ab875caad1157c0995d34f66 a90eed8d70c54c8e554f2dfde4fceb434eda162d9615d62de96ded2344f88a78 c33ef5e29100b323412f1f3bc66908c8e01e4f0d1db4ea3685d2fffc47981dd6 c84dffef20fec958255e759db6445fc469d73695674a33ae6f7e567a088c9fe0 d362d599d72000c4feb464db5a669b12e15fc3ca1a49b1e7d4d6f7d6d5d15411 d9378616636be3686bbabd5bf29d50f0ef0e5ceb5ddd7dfce47f7e755b596b7d da26fa6e7371385ed3f61af9a766221c833060d59dfd4869bbd7110f95f288db e4103a75627857de3ee2e317429108611c244fc448c01d1d7bf652115c3b8a55 eb368fedb8f100210dd968edcf80f4d13cab3dd64135a6ab744102cf15e68c94 f13ab4bca04f894ae8eabb51fa01b4dfbc69f717eabc9896c728e2ba39c4db27 f493baf276892a199a0b0d078359f64a38fe8ad3f807921f8d41ef73f7343b1f ff92b6c06ae5286bd2f1db679e0fcc4da294acb9bc01b2e9522378d99218c2e3 --- end of file descriptions --- References [1] Data Observation Network for Earth (DataONE, https://dataone.org) accessed from 2018-11-06 to 2020-05-07 with provenance hash://sha256/2b5c445f0b7b918c14a50de36e29a32854ed55f00d8639e09f58f049b85e50e3. [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 . This work is funded in part by grant NSF OAC 1839201 from the National Science Foundation.

opencc-by-4.0Nov 2020View details →

Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics (Training Dataset)

This dataset consists of multiple files which contain bug prediction training data. The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on). The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge. We used different threshold values from which we considered the edges to be valid. The following threshold values were used: <ul> <li>0.00</li> <li>0.05</li> <li>0.20</li> <li>0.30</li> </ul> The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values. There are four variants for all of these datasets: <ul> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are: <ul> <li>ID</li> <li>Name</li> <li>Longname</li> <li>Parent ID</li> <li>Component ID</li> <li>Path</li> <li>Line</li> <li>Column</li> <li>EndLine</li> <li>EndColumn</li> </ul> </li> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)</li> <li>Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)</li> <li>Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)</li> </ul> Static source code metrics which are contained in all dataset are the following: <ul> <li>McCC - McCabe Cyclomatic Complexity</li> <li>NL - Nesting Level</li> <li>NLE - Nesting Level Else If</li> <li>CD - Comment Density</li> <li>CLOC - Comment Lines of Code</li> <li>DLOC - Documentation Lines of Code</li> <li>TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)</li> <li>TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)</li> <li>LLOC - Logical Lines of Code (Comment and empty lines not counted)</li> <li>LOC - Lines of Code (Comment and empty lines are counted)</li> <li>NOS - Number of Statements</li> <li>NUMPAR - Number of Parameters</li> <li>TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)</li> <li>TLOC - Lines of Code (Lines in embedded functions are also counted)</li> <li>TNOS - Total Number of Statements (Statements in embedded functions are also counted)</li> </ul>

opencc-by-4.0Jan 2021View details →

LabelGit: A dataset for software repositories classification using attributed dependency graphs

A dataset for software repositories classification using attributed dependency graphs

opencc-by-4.0Nov 2023View details →

dMC-Juniata-hydroDL River Graph Dataset

This release is the data for the dMC-Juniata-hydroDL2 project (https://zenodo.org/doi/10.5281/zenodo.10183448). There is a readme inside of the .zip file which contains instructions for how to use this dataset.

opencc-by-4.0Nov 2023View details →

Dataset for monkeys A and B from AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity

ECoG data from two monkeys, affi (A) and beignet (B) used in AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity. Jingyuan Li, Leo Scholl, Trung Le, Pavithra Rajeswaran, Amy L Orsborn, and Eli Shlizerman. NeurIPS. 2023. https://openreview.net/forum?id=7ntI4kcoqGSee also https://github.com/shlizee/AMAG

opencc-by-4.0Mar 2024View details →

Datasets for Paper "MetagenomicKG: a knowledge graph for metagenomic applications"

This repository contains some required data that is used for building MetagenomicKG. Please see more details in <a href="https://github.com/KoslickiLab/MetagenomicKG">https://github.com/KoslickiLab/MetagenomicKG</a>.

opencc-by-4.0Jul 2024View details →

OpenAIRE Graph: Dataset of funded products

This dataset contains the metadata records about research products (research literature, data, software, other types of research products) with funding information available in the OpenAIRE Graph produced on July 2024. Records are grouped by funder in a dedicated archive file (<funder acronym>.tar). <ul> <li>fundRef contains the following funders</li> </ul> <div> <ul> <li> <ul> <li>100007490 Bausch and Lomb Ireland</li> <li>100007630 College of Engineering and Informatics, National University of Ireland, Galway</li> <li>100007731 Endo International</li> <li>100007819 Allergan</li> <li>100008099 Food Safety Authority of Ireland</li> <li>100008124 Department of Jobs, Enterprise and Innovation</li> <li>100008303 Department for Economics, Northern Ireland</li> <li>100009098 Department of Foreign Affairs and Trade, Ireland</li> <li>100009099 Irish Aid</li> <li>100009770 National University of Ireland</li> <li>100010399 European Society of Cataract and Refractive Surgeons</li> <li>100010546 Deparment of Children and Youth Affairs, Ireland</li> <li>100010547 Irish Youth Justice Service</li> <li>100010993 Irish Nephrology Society</li> <li>100011096 Jazz Pharmaceuticals</li> <li>100011396 Irish College of General Practitioners</li> <li>100012733 National Parks and Wildlife Service</li> <li>100012734 Department for Culture, Heritage and the Gaeltacht, Ireland</li> <li>100012754 Horizon Pharma</li> <li>100012891 Medical Research Charities Group</li> <li>100012919 Epilepsy Ireland</li> <li>100012920 GLEN</li> <li>100012921 Royal College of Surgeons in Ireland</li> <li>100013029 Iris O'Brien Foundation</li> <li>100013206 Food Institutional Research Measure</li> <li>100013381 Irish Phytochemical Food Network</li> <li>100013433 Transport Infrastructure Ireland</li> <li>100013917 Society for Musicology in Ireland</li> <li>100014251 Humanities in the European Research Area</li> <li>100014364 National Children's Research Centre</li> <li>100014384 Amarin Corporation</li> <li>100014902 Irish Association for Cancer Research</li> <li>100015023 Ireland Funds</li> <li>100015278 Pfizer Healthcare Ireland</li> <li>100015319 Sport Ireland Institute</li> <li>100015442 Global Brain Health Institute</li> <li>100015992 St. Luke's Institute of Cancer Research</li> <li>100017144 Shell E and P Ireland</li> <li>100017897 Friedreich’s Ataxia Research Alliance Ireland</li> <li>100018064 Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media</li> <li>100018172 Department of the Environment, Climate and Communications</li> <li>100018175 Dairy Processing Technology Centre</li> <li>100018270 Health Service Executive</li> <li>100018529 Alkermes</li> <li>100018542 Irish Endocrine Society</li> <li>100018754 An Roinn Sláinte</li> <li>100019428 Nabriva Therapeutics</li> <li>100019637 Horizon Therapeutics</li> <li>100020174 Health Research Charities Ireland</li> <li>100020202 UCD Foundation</li> <li>100020233 Ireland Canada University Foundation</li> <li>100022895 Health Research Institute, University of Limerick</li> <li>100022943 National Cancer Registry Ireland</li> <li>501100001581 Arts Council of Ireland</li> <li>501100001582 Centre for Ageing Research and Development in Ireland</li> <li>501100001583 Cystinosis Foundation Ireland</li> <li>501100001584 Department of Agriculture, Food and the Marine, Ireland</li> <li>501100001586 Department of Education and Skills, Ireland</li> <li>501100001587 Economic and Social Research Institute</li> <li>501100001588 Enterprise Ireland</li> <li>501100001591 Heritage Council</li> <li>501100001592 Higher Education Authority</li> <li>501100001593 Irish Cancer Society</li> <li>501100001594 Irish Heart Foundation</li> <li>501100001595 Irish Hospice Foundation</li> <li>501100001596 Irish Research Council for Science, Engineering and Technology</li> <li>501100001598 Mental Health Commission</li> <li>501100001599 National Council for Forest Research and Development</li> <li>501100001600 Research and Education Foundation, Sligo General Hospital</li> <li>501100001601 Royal Irish Academy</li> <li>501100001603 Sustainable Energy Authority of Ireland</li> <li>501100001604 Teagasc</li> <li>501100001627 Marine Institute</li> <li>501100001628 Central Remedial Clinic</li> <li>501100001629 Royal Dublin Society</li> <li>501100001630 Dublin Institute for Advanced Studies</li> <li>501100001631 University College Dublin</li> <li>501100001633 National University of Ireland, Maynooth</li> <li>501100001634 University of Galway</li> <li>501100001635 University of Limerick</li> <li>501100001636 University College Cork</li> <li>501100001637 Trinity College Dublin</li> <li>501100001638 Dublin City University</li> <li>501100002736 Covidien</li> <li>501100002755 Brennan and Company</li> <li>501100002919 Cork Institute of Technology</li> <li>501100002959 Dublin City Council</li> <li>501100003036 Perrigo Company Charitable Foundation</li> <li>501100003037 Elan</li> <li>501100003496 HeyStaks Technologies</li> <li>501100003553 Gaelic Athletic Association</li> <li>501100003840 Irish Institute of Clinical Neuroscience</li> <li>501100003956 Aspect Medical Systems</li> <li>501100004162 Meath Foundation</li> <li>501100004210 Our Lady's Children's Hospital, Crumlin</li> <li>501100004321 Shire</li> <li>501100004981 Athlone Institute of Technology</li> <li>501100006518 Department of Communications, Energy and Natural Resources, Ireland</li> <li>501100006553 Collaborative Centre for Applied Nanotechnology</li> <li>501100006554 IDA Ireland</li> <li>501100006759 CLARITY Centre for Sensor Web Technologies</li> <li>501100009246 Technological University Dublin</li> <li>501100009269 Programme of Competitive Forestry Research for Development</li> <li>501100009315 Cystinosis Ireland</li> <li>501100010808 Geological Survey of Ireland</li> <li>501100011030 Alimentary Glycoscience Research Cluster</li> <li>501100011031 Alimentary Health</li> <li>501100011103 Rannís</li> <li>501100011626 Energy Policy Research Centre, Economic and Social Research Institute</li> <li>501100012354 Inland Fisheries Ireland</li> <li>501100014384 X-Bolt Orthopaedics</li> <li>501100014531 Physical Education and Sport Sciences Department, University of Limerick</li> <li>501100014710 PrecisionBiotics Group</li> <li>501100014745 APC Microbiome Institute</li> <li>501100014826 ADAPT - Centre for Digital Content Technology</li> <li>501100014827 Dormant Accounts Fund</li> <li>501100017501 FotoNation</li> <li>501100018641 Dairy Research Ireland</li> <li>501100018839 Irish Centre for High-End Computing</li> <li>501100019905 Galway University Foundation</li> <li>501100020270 Advanced Materials and Bioengineering Research</li> <li>501100020403 Irish Composites Centre</li> <li>501100020425 Irish Thoracic Society</li> <li>501100020570 College of Medicine, Nursing and Health Sciences, National University of Ireland, Galway</li> <li>501100020871 Bernal Institute, University of Limerick</li> <li>501100021102 Waterford Institute of Technology</li> <li>501100021110 Irish MPS Society</li> <li>501100021525 Insight SFI Research Centre for Data Analytics</li> <li>501100021694 Elan Pharma International</li> <li>501100021838 Royal College of Physicians of Ireland</li> <li>501100022542 Breakthrough Cancer Research</li> <li>501100022610 Breast Cancer Ireland</li> <li>501100022728 Munster Technological University</li> <li>501100023273 HRB Clinical Research Facility Galway</li> <li>501100023551 Cystic Fibrosis Ireland</li> <li>501100023970 Tyndall National Institute</li> <li>501100024242 Synthesis and Solid State Pharmaceutical Centre</li> <li>501100024313 Irish Rugby Football Union</li> <li>501100024834 Tusla - Child and Family Agency</li> </ul> </li> <li>AKA Academy of Finland</li> <li>ANR French National Research Agency (ANR)</li> <li>ARC Australian Research Council (ARC)</li> <li>ASAP Aligning Science Across Parkinson's</li> <li>CHISTERA CHIST-ERA</li> <li>CIHR Canadian Institutes of Health Research</li> <li>EC_ERASMUS+ European Commission - Erasmus+ funding stream</li> <li>EC_FP7 European Commission - FP7 funding stream</li> <li>EC_H2020 European Commission - H2020 funding stream</li> <li>EC_HE European Commission - HE funding stream</li> <li>EEA European Environment Agency</li> <li>EPA Environmental Protection Agency</li> <li>FCT Fundação para a Ciência e a Tecnologia, I.P.</li> <li>FWF Austrian Science Fund</li> <li>HRB             Health Research Board</li> <li>HRZZ            Croatian Science Foundation</li> <li>INCA Institut National du Cancer</li> <li>IRC         Irish Research Council</li> <li>IReL         Irish Research eLibrary</li> <li>MESTD Ministry of Education, Science and Technological Development of Republic of Serbia</li> <li>MZOS TOADDNAME</li> <li>NHMRC National Health and Medical Research Council (NHMRC)</li> <li>NIH National Institutes of Health</li> <li>NSERC Natural Sciences and Engineering Research Council of Canada</li> <li>NSF National Science Foundation</li> <li>NWO Netherlands Organisation for Scientific Research (NWO)</li> <li>SFI Science Foundation Ireland</li> <li>SNSF Swiss National Science Foundation</li> <li>SSHRC Social Sciences and Humanities Research Council</li> <li>TARA Tara Expeditions Foundation</li> <li>TIBITAK Türkiye Bilimsel ve Teknolojik Araştırma Kurumu</li> <li>UKRI UK Research and Innovation</li> <li>WT Wellcome Trust</li> </ul> </div> Each tar archive contains gzip files with one json record per line. Json records are compliant with the schema available at <a href="https://doi.org/10.5281/zenodo.14608710">https://doi.org/10.5281/zenodo.14608710</a>. You can also search and browse this dataset (and more) in the <a href="https://explore.openaire.eu">OpenAIRE EXPLORE portal</a> and via the <a href="https://develop.openaire.eu">OpenAIRE API</a>.

opencc-by-4.0Aug 2024View details →

PrimeKGQA, the dataset from paper: Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset

Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total, we create 83999 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA system.