University Library, University of Illinois at Urbana-Champaign

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 through the XSEDE operational period, which ended August 31, 2022. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation. Because the XSEDE program has ended, the allocation_award_history file includes all allocations activity initiated via XSEDE processes through August 31, 2022. The Resource Providers and successor program to XSEDE agreed to honor all project allocations made during XSEDE. Thus, allocation awards that extend beyond the end of XSEDE may not reflect all activity that may ultimately be part of the project award. Similarly, allocation usage data only reflects usage reported through August 31, 2022, and may not reflect all activity that may ultimately be conducted by projects that were active beyond XSEDE.

Tab-separated value (TSV) file. 14745 data rows. Each data row represents publication metadata as retrieved from Crossref (http://crossref.org) 2023-04-05 when searching for retracted publications. Each row has the following columns: Index - Our index, starting with 0. DOI - Digital Object Identifier (DOI) for the publication Year - Publication year associated with the DOI. URL - Web location associated with the DOI. Title - Title associated with the DOI. May be blank. Author - Author(s) associated with the DOI. Journal - Publication venue (journal, conference, ...) associated with the DOI RetractionYear - Retraction Year associated with the DOI. May be blank. Category - One or more categories associated with the DOI. May be blank. Our search was via the Crossref REST API and searched for: Update_type=( 'retraction', 'Retraction', 'retracion', 'retration', 'partial_retraction', 'withdrawal','removal')

We used these keywords to identify categories for journals and conferences not in Scopus, for our manuscript "Assessing the agreement in retraction indexing across 4 multidisciplinary sources: Crossref, Retraction Watch, Scopus, and Web of Science". These 4 text files each contains a keywords in the form: 'keyword1', 'keyword2', 'keyword3', ..... The file title indicates the name of the category: HealthScience.txt LifeScience.txt PhysicalScience.txt SocialScience.txt Each file was generated from a combination of software and manual review in an iterative process in which we: - First included keywords found using Yet Another Keyword Extractor <https://pypi.org/project/yake/> on the Scopus source list as of January 2023 <https://www.elsevier.com/?a=91122>. - Second, assigned journals and conferences to one or more categories, when they matched a keyword from that category list. - Reviewed uncategorized items to manually curate additional keywords in English and close cognates (e.g., Kardiologie). Titles in other languages or using terminology with multiple potential meanings were left uncategorized.

Sentences and citation contexts identified from the PubMed Central open access articles ---------------------------------------------------------------------- The dataset is delivered as 24 tab-delimited text files. The files contain 720,649,608 sentences, 75,848,689 of which are citation contexts. The dataset is based on a snapshot of articles in the XML version of the PubMed Central open access subset (i.e., the PMCOA subset). The PMCOA subset was collected in May 2019. The dataset is created as described in: Hsiao TK., & Torvik V. I. (manuscript) OpCitance: Citation contexts identified from the PubMed Central open access articles. <b>Files</b>: • A_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with A. • B_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with B. • C_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with C. • D_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with D. • E_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with E. • F_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with F. • G_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with G. • H_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with H. • I_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with I. • J_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with J. • K_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with K. • L_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with L. • M_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with M. • N_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with N. • O_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with O. • P_p1_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 1). • P_p2_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 2). • Q_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with Q. • R_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with R. • S_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with S. • T_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with T. • UV_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with U or V. • W_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with W. • XYZ_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with X, Y or Z. Each row in the file is a sentence/citation context and contains the following columns: • pmcid: PMCID of the article • pmid: PMID of the article. If an article does not have a PMID, the value is NONE.  • location: The article component (abstract, main text, table, figure, etc.) to which the citation context/sentence belongs.  • IMRaD: The type of IMRaD section associated with the citation context/sentence. I, M, R, and D represent introduction/background, method, results, and conclusion/discussion, respectively; NoIMRaD indicates that the section type is not identifiable.  • sentence_id: The ID of the citation context/sentence in the article component • total_sentences: The number of sentences in the article component.  • intxt_id: The ID of the citation. • intxt_pmid: PMID of the citation (as tagged in the XML file). If a citation does not have a PMID tagged in the XML file, the value is "-". • intxt_pmid_source: The sources where the intxt_pmid can be identified. Xml represents that the PMID is only identified from the XML file; xml,pmc represents that the PMID is not only from the XML file, but also in the citation data collected from the NCBI Entrez Programming Utilities. If a citation does not have an intxt_pmid, the value is "-".  • intxt_mark: The citation marker associated with the inline citation. • best_id: The best source link ID (e.g., PMID) of the citation. • best_source: The sources that confirm the best ID. • best_id_diff: The comparison result between the best_id column and the intxt_pmid column. • citation: A citation context. If no citation is found in a sentence, the value is the sentence.  • progression: Text progression of the citation context/sentence.  <b>Supplementary Files</b> • PMC-OA-patci.tsv.gz – This file contains the best source link IDs for the references (e.g., PMID). Patci [1] was used to identify the best source link IDs. The best source link IDs are mapped to the citation contexts and displayed in the *_journal IntxtCit.tsv files as the best_id column. Each row in the PMC-OA-patci.tsv.gz file is a citation (i.e., a reference extracted from the XML file) and contains the following columns: • pmcid: PMCID of the citing article. • pos: The citation's position in the reference list. • fromPMID: PMID of the citing article. • toPMID: Source link ID (e.g., PMID) of the citation. This ID is identified by Patci. • SRC: The sources that confirm the toPMID. • MatchDB: The origin bibliographic database of the toPMID. • Probability: The match probability of the toPMID. • toPMID2: PMID of the citation (as tagged in the XML file). • SRC2: The sources that confirm the toPMID2. • intxt_id: The ID of the citation. • journal: The first letter of the journal title. This maps to the *_journal_IntxtCit.tsv files. • same_ref_string: Whether the citation string appears in the reference list more than once. • DIFF: The comparison result between the toPMID column and the toPMID2 column. • bestID: The best source link ID (e.g., PMID) of the citation. • bestSRC: The sources that confirm the best ID. • Match: Matching result produced by Patci. [1] Agarwal, S., Lincoln, M., Cai, H., & Torvik, V. (2014). Patci – a tool for identifying scientific articles cited by patents. GSLIS Research Showcase 2014. http://hdl.handle.net/2142/54885 • intxt_cit_license_fromPMC.tsv – This file contains the CC licensing information for each article. The licensing information is from PMC's file lists [2], retrieved on June 19, 2020, and March 9, 2023. It should be noted that the license information for 189,855 PMCIDs is <b>NO-CC CODE</b> in the file lists, and 521 PMCIDs are absent in the file lists. The absence of CC licensing information does not indicate that the article lacks a CC license. For example, PMCID: 6156294 (<b>NO-CC CODE</b>) and PMCID: 6118074 (absent in the PMC's file lists) are under CC-BY licenses according to their PDF versions of articles. The intxt_cit_license_fromPMC.tsv file has two columns: • pmcid: PMCID of the article. • license: The article’s CC license information provided in PMC’s file lists. The value is nan when an article is not present in the PMC’s file lists. [2] https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/ • Supplementary_File_1.zip – This file contains the code for generating the dataset.

Coups d'État are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d'État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e. realized or successful coups, unrealized coup attempts, or thwarted conspiracies) the type of actor(s) who initiated the coup (i.e. military, rebels, etc.), as well as the fate of the deposed leader. This current version, Version 2.1.2, adds 6 additional coup events that occurred in 2022 and updates the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrects a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixes this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removes two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and adds executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Changes from the previously released data (v2.0.0) also include: 1. Adding additional events and expanding the period covered to 1945-2022 2. Filling in missing actor information 3. Filling in missing information on the outcomes for the incumbent executive 4. Dropping events that were incorrectly coded as coup events <br> <b>Items in this Dataset</b> 1. <i>Cline Center Coup d'État Codebook v.2.1.2 Codebook.pdf</i> - This 16-page document provides a description of the Cline Center Coup d’État Project Dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d’État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. <i>Revised February 2023</i> 2. <i>Coup Data v2.1.2.csv</i> - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 981 observations. <i>Revised February 2023</i> 3. <i>Source Document v2.1.2.pdf</i> - This 315-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. <i>Revised February 2023</i> 4. <i>README.md</i> - This file contains useful information for the user about the dataset. It is a text file written in markdown language. <i>Revised February 2023</i> <br> <b> Citation Guidelines</b> 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2023. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.2. February 23. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V6 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2023. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.2. February 23. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V6

These processing and Pearson correlational scripts were developed to support the study that examined the correlational relationships between local journal authorship, local and external citation counts, full-text downloads, link-resolver clicks, and four global journal impact factor indices within an all-disciplines journal collection of 12,200 titles and six subject subsets at the University of Illinois at Urbana-Champaign (UIUC) Library. This study shows strong correlations in the all-disciplines set and most subject subsets. Special processing scripts and web site dashboards were created, including Pearson correlational analysis scripts for reading values from relational databases and displaying tabular results. The raw data used in this analysis, in the form of relational database tables with multiple columns, is available at <a href="https://doi.org/10.13012/B2IDB-6810203_V1">https://doi.org/10.13012/B2IDB-6810203_V1</a>.

This dataset was developed as part of a study that examined the correlational relationships between local journal authorship, local and external citation counts, full-text downloads, link-resolver clicks, and four global journal impact factor indices within an all-disciplines journal collection of 12,200 titles and six subject subsets at the University of Illinois at Urbana-Champaign (UIUC) Library. While earlier investigations of the relationships between usage (downloads) and citation metrics have been inconclusive, this study shows strong correlations in the all-disciplines set and most subject subsets. The normalized Eigenfactor was the only global impact factor index that correlated highly with local journal metrics. Some of the identified disciplinary variances among the six subject subsets may be explained by the journal publication aspirations of UIUC researchers. The correlations between authorship and local citations in the six specific subject subsets closely match national department or program rankings. All the raw data used in this analysis, in the form of relational database tables with multiple columns. Can be opned using MS Access. Description for variables can be viewed through "Design View" (by right clik on the selected table, choose "Design View"). The 2 PDF files provide an overview of tables are included in each MDB file. In addition, the processing scripts and Pearson correlation code is available at <a href="https://doi.org/10.13012/B2IDB-0931140_V1">https://doi.org/10.13012/B2IDB-0931140_V1</a>.

The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. <a href="https://github.com/infoqualitylab/Scopus_author_info_collection">https://github.com/infoqualitylab/Scopus_author_info_collection</a> that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file UPDATES IN THIS VERSION COMPARED TO V1(Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2022): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V1) In V1, we did not upload the file "article_list.csv." We uploaded the missing file in this version, and everything else remains the same.

These are similarity matrices of countries based on dfferent modalities of web use. Alexa website traffic, trending vidoes on Youtube and Twitter trends. Each matrix is a month of data aggregated

The salt controversy is the public health debate about whether a population-level salt reduction is beneficial. This dataset covers 82 publications--14 systematic review reports (SRRs) and 68 primary study reports (PSRs)--addressing the effect of sodium intake on cerebrocardiovascular disease or mortality. These present a snapshot of the status of the salt controversy as of September 2014 according to previous work by epidemiologists: The reports and their opinion classification (for, against, and inconclusive) were from Trinquart et al. (2016) (Trinquart, L., Johns, D. M., & Galea, S. (2016). Why do we think we know what we know? A metaknowledge analysis of the salt controversy. International Journal of Epidemiology, 45(1), 251–260. https://doi.org/10.1093/ije/dyv184 ), which collected 68 PSRs, 14 SRRs, 11 clinical guideline reports, and 176 comments, letters, or narrative reviews. Note that our dataset covers only the 68 PSRs and 14 SRRs from Trinquart et al. 2016, not the other types of publications, and it adds additional information noted below. This dataset can be used to construct the inclusion network and the co-author network of the 14 SRRs and 68 PSRs. A PSR is "included" in an SRR if it is considered in the SRR's evidence synthesis. Each included PSR is cited in the SRR, but not all references cited in an SRR are included in the evidence synthesis or PSRs. Based on which PSRs are included in which SRRs, we can construct the inclusion network. The inclusion network is a bipartite network with two types of nodes: one type represents SRRs, and the other represents PSRs. In an inclusion network, if an SRR includes a PSR, there is a directed edge from the SRR to the PSR. The attribute file (report_list.csv) includes attributes of the 82 reports, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Notably, 11 PSRs have never been included in any SRR in the dataset. They are unused PSRs. If visualized with the inclusion network, they will appear as isolated nodes. We used a custom-made workflow (Fu, Y. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection ) that uses the Scopus API and manual work to extract and disambiguate authorship information for the 82 reports. The author information file (salt_cont_author.csv) is the product of this workflow and can be used to compute the co-author network of the 82 reports. We also provide several other files in this dataset. We collected inclusion criteria (the criteria that make a PSR eligible to be included in an SRR) and recorded them in the file systematic_review_inclusion_criteria.csv. We provide a file (potential_inclusion_link.csv) recording whether a given PSR had been published as of the search date of a given SRR, which makes the PSR potentially eligible for inclusion in the SRR. We also provide a bibliography of the 82 publications (supplementary_reference_list.pdf). Lastly, we discovered minor discrepancies between the inclusion relationships identified by Trinquart et al. (2016) and by us. Therefore, we prepared an additional edge list (inclusion_net_edges_trinquart.csv) to preserve the inclusion relationships identified by Trinquart et al. (2016). <b>UPDATES IN THIS VERSION COMPARED TO V1</b> (Fu, Yuanxi; Hsiao, Tzu-Kun; Joshi, Manasi Ballal (2022): The Salt Controversy Systematic Review Reports and Primary Study Reports Network Dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6128763_V1) - We added two new columns in salt_cont_author.csv, "author_id_scopus" and "author_id_mannual" to indicate which author ids were from Scopus and which were assigned by us. - We corrected a few mistakes in "last_search_year," "last_search_month," and "last_search_day" column in systematic_review_inclusion_criteria.csv. - We systematically adjusted the information related to report #12 in report_list.csv, systematic_review_inclusion_criteria.csv, supplementary_reference_list.pdf, salt_cont_author.csv, and inclusion_net_edges.csv to reflect information found in Adler 2014 (Adler, A. J., Taylor, F., Martin, N., Gottlieb, S., Taylor, R. S., & Ebrahim, S. (2014). Reduced dietary salt for the prevention of cardiovascular disease. Cochrane Database of Systematic Reviews, 12. https://doi.org/10.1002/14651858.CD009217.pub3). See our explaination in section "Explanations about report #12". - We sorted the salt_cont_author.csv file by "author_id," not by "ID" (the id of the report).

One of the newest types of multimedia involves body-connected interfaces, usually termed haptics. Haptics may use stylus-based tactile interfaces, glove-based systems, handheld controllers, balance boards, or other custom-designed body-computer interfaces. How well do these interfaces help students learn Science, Technology, Engineering, and Mathematics (STEM)? We conducted an updated review of learning STEM with haptics, applying meta-analytic techniques to 21 published articles reporting on 53 effects for factual, inferential, procedural, and transfer STEM learning. This deposit includes the data extracted from those articles and comprises the raw data used in the meta-analytic analyses.

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. <b>Additional Resources:</b> - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the <a href="https://docs.google.com/forms/d/e/1FAIpQLSf-J937V6I4sMSxQt7gR3SIbUASR26KXxqSurrkBvlF-CIQnQ/viewform?usp=pp_url"><b>Archer Access Request Form</b></a> so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the <a href="https://forms.gle/6eA2yJUGFMtj5swY7"><b>Archer User Feedback Form</b></a>. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this <a href="https://groups.webservices.illinois.edu/subscribe/123172"><b>form</b></a> to subscribe to it. <b>Citation Guidelines:</b> 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2022. Global News Index and Extracted Features Repository [codebook], v1.1.0. Champaign, IL: University of Illinois. Dec. 16. doi:10.13012/B2IDB-5649852_V3 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2022. Global News Index and Extracted Features Repository [database], v1.1.0. Champaign, IL: University of Illinois. Dec. 16. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V3

Related to the raw entity mentions, this dataset represents the effects of the data cleaning process and collates all of the entity mentions which were too ambiguous to successfully link to the NCBI's taxonomy identifier system.

A set of species entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset represents the results of manual cleaning and annotation of the entity mentions contained in the raw dataset (https://doi.org/10.13012/B2IDB-4950847_V1). Each mention has been consolidated and linked to an identifier for a matching concept from the NCBI's taxonomy database.

This dataset is derived from the raw dataset (https://doi.org/10.13012/B2IDB-4950847_V1) and collects entity mentions that were manually determined to be noisy, non-species entities.

This dataset is derived from the raw entity mention dataset (https://doi.org/10.13012/B2IDB-4950847_V1) for species entities and represents those that were determined to be species (i.e., were not noisy entities) but for which no corresponding concept could be found in the NCBI taxonomy database.

Related to the raw entity mentions (https://doi.org/10.13012/B2IDB-4163883_V1), this dataset represents the effects of the data cleaning process and collates all of the entity mentions which were too ambiguous to successfully link to the ChEBI ontology.

A set of chemical entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

A set of cell-line entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset represents the results of manual cleaning and annotation of the entity mentions contained in the raw dataset (https://doi.org/10.13012/B2IDB-4163883_V1). Each mention has been consolidated and linked to an identifier for a matching concept from the NCBI's taxonomy database.

This dataset is derived from the raw dataset (https://doi.org/10.13012/B2IDB-4163883_V1) and collects entity mentions that were manually determined to be noisy, non-chemical entities.

This dataset is derived from the raw entity mention dataset (https://doi.org/10.13012/B2IDB-4163883_V1) for checmical entities and represents those that were determined to be chemicals (i.e., were not noisy entities) but for which no corresponding concept could be found in the ChEBI ontology.

A set of gene and gene-related entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset contains data used in publication "Institutional Data Repository Development, a Moving Target" submitted to Code4Lib Journal. It is a tabular data file describing attributes of data files in datasets published in Illinois Data Bank 2016-04-01 to 2021-04-01.