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222 results for “co-occurrence”
Data for the paper 'Reducing networks of ethnographic codes co-occurrence in anthropology'
<p>Pseudonymized data supporting the paper "Reducing networks of ethnographic codes co-occurrence in anthropology", published in "Advances in Quantitative Ethnography. Fourth International Conference on Quantitative Ethnography (ICQE 2022), Copenhagen, Denmark, October 15–19, 2022, Proceedings", and edited by Amanda Barany and Crina Damsa. The paper is part of the POPREBEL project. The data were gathered in the spring and summer of 2021, as a part of a larger research project on populism in Central and Eastern Europe, to be completed by the end of 2022. They consist of 17 semi-structured interviews with Polish-speaking Internet users, who used social media to seek and share information about health against the backdrop of the COVID-19 pandemic. Research participants were asked about their opinion on the current state of affairs in their respective countries, and their political choices over the years and at present.</p> <p><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data).</p>
Data from: Heterogeneity in habitat and nutrient availability facilitate the co-occurrence of N2 fixation and denitrification across wetland - stream - lake ecotones of Lakes Superior and Huron
Great Lakes coastlines are mosaics of wetland, stream, and lake habitats, characterized by a high degree of spatial heterogeneity that may facilitate the co-occurrence of seemingly incompatible biogeochemical processes due to variation in environmental factors that favor each process. We measured nutrient limitation and rates of N2 fixation and denitrification along transects in 5 wetland - stream - lake ecotones with different nutrient loading in Lakes Superior and Huron and hypothesized that rates of both processes would be related to nutrient limitation status, habitat type, and environmental characteristics including temperature, nutrient concentrations, and organic matter quality. This data package includes information on sampling sites, dates and locations; rates of N fixation and denitrification measured at each site, date and transect location; and biomass information from nutrient diffusing substrates deployed on the study transects.
Research Data and Code for "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles"
<p>This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`. </p>
Co-occurrences of trending keywords in popular tech media (01.2016-04.2021)
<p>Sources with weights</p> <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> <p>Methodology</p> <ul> <li>Exploring the relationship between topics</li> <li>Pairs of terms which are mentioned together in media articles</li> <li>Most trending social issues have been selected (e.g. 'metoo', 'gdpr')</li> <li>The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams</li> <li>The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles</li> <li>A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)</li> </ul>
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis during the COVID-19 pandemic (01.2020-06.2020)
<p><strong>Sources: </strong></p> <ul> <li>Euractiv</li> <li>The Conversation</li> <li>Politico Europe </li> <li>IEEE Spectrum </li> <li>Techforge </li> <li>Fastcompany </li> <li>The Guardian (Tech) </li> <li>Arstechnica </li> <li>Reuters </li> <li>Gizmodo </li> <li>ZDNet </li> <li>The Register </li> <li>The Verge </li> <li>TechCrunch </li> </ul> <p> </p> <p><strong>Methodology</strong></p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p>The analysed paragraphs are selected the following way:</p> <ul> <li>The articles containing the given social issue are identified</li> <li>The paragraphs containing the social issue are selected for sentiment analysis</li> </ul> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Co-occurrences of trending keywords in popular tech media during the COVID-19 pandemic (01.2020-06.2020)
<p>Sources: </p> <ul> <li>Euractiv</li> <li>The Conversation</li> <li>Politico Europe </li> <li>IEEE Spectrum </li> <li>Techforge </li> <li>Fastcompany </li> <li>The Guardian (Tech) </li> <li>Arstechnica </li> <li>Reuters </li> <li>Gizmodo </li> <li>ZDNet </li> <li>The Register </li> <li>The Verge </li> <li>TechCrunch </li> </ul> <p>Methodology</p> <ul> <li>Exploring the relationship between topics</li> <li>Pairs of terms which are mentioned together in media articles</li> <li>Most trending social issues and technologies have been selected (e.g. covid19)</li> <li>The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams</li> <li>The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles</li> <li>A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)</li> </ul>
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis
<p><strong>Sentiment analysis of tech media articles using VADER package and co-occurrence analysis</strong></p> <p><strong>Sources</strong>: Above 140k articles (01.2016-03.2019):</p> <ul> <li>Gigaom 0.5%</li> <li>Euractiv 0.9%</li> <li>The Conversation 1.3%</li> <li>Politico Europe 1.3%</li> <li>IEEE Spectrum 1.8%</li> <li>Techforge 4.3%</li> <li>Fastcompany 4.5%</li> <li>The Guardian (Tech) 9.2%</li> <li>Arstechnica 10.0%</li> <li>Reuters 11%</li> <li>Gizmodo 17.5%</li> <li>ZDNet 18.3%</li> <li>The Register 19.5%</li> </ul> <p><strong>Methodology</strong></p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p>The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way:</p> <ul> <li>The articles containing the given social issue are identified</li> <li>The paragraphs containing the social issue are selected for sentiment analysis</li> </ul> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p> <p> </p> <p><strong>Files</strong></p> <p>sentiments_mod11.csv sentiment score based on chosen unigrams</p> <p>sentiments_mod22.csv sentiment score based on chosen bigrams</p> <p>sentiments_cooc_mod11.csv, sentiments_cooc_mod12.csv, sentiments_cooc_mod21.csv, sentiments_cooc_mod22.csv combinations of co-occurrences: unigrams-unigrams, unigrams-bigrams, bigrams-unigrams, bigrams-bigrams</p> <p> </p>
Co-occurrences of trending keywords in popular tech media
<p><strong>Co-occurrences of trending keywords in the tech media (01.2016-03.2019)</strong></p> <p><strong>Sources</strong></p> <ul> <li>Gigaom 0.5%</li> <li>Euractiv 0.9%</li> <li>The Conversation 1.3%</li> <li>Politico Europe 1.3%</li> <li>IEEE Spectrum 1.8%</li> <li>Techforge 4.3%</li> <li>Fastcompany 4.5%</li> <li>The Guardian (Tech) 9.2%</li> <li>Arstechnica 10.0%</li> <li>Reuters 11%</li> <li>Gizmodo 17.5%</li> <li>ZDNet 18.3%</li> <li>The Register 19.5%</li> </ul> <p><strong>Methodology</strong></p> <ul> <li>Exploring the relationship between topics</li> <li>Pairs of terms which are mentioned together in media articles</li> <li>Most trending social issues have been selected (e.g. 'metoo', 'gdpr')</li> <li>The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams</li> <li>The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles</li> <li>A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)</li> </ul> <p><strong>Files</strong></p> <p>unigram-unigram co-occurrences: cooc11weighted.csv</p> <p>unigram-bigram co-occurrences: cooc12weighted.csv</p> <p>bigram-unigram co-occurrences: cooc21weighted.csv</p> <p>bigram-bigram co-occurrences: cooc22weighted.csv<br> </p> <p> </p>
UTHSC Publication Research Category (ANZSRC 2020) Co-Occurrence
<p>This data visualization is a bibliometric analysis of University of Tennessee Health Science Center publications for the years 2018-2020. It was created for senior University leadership for the purposes of strategic planning and identifying research areas of strength.</p> <p>Bibliographic data was supplied by Dimensions by Digital Science. The chord graph was created in Tableau and demonstrates relationship pairs of Fields of Research (ANZSRC 2020) categories. Each publication record may be associated with 1+ categories. The graph highlights the frequency of category pairings within a single publication record. I.e. publications categorized as "Immunology" are most commonly also categorized with "Medical Microbiology," indicating overlap in this area of research.</p> <p>This graph was created using instructions from Marc Reid's datavis.blog entry "Creating a Chord Diagram with Tableau Prep and Desktop" (<a href="https://datavis.blog/2020/07/02/creating-chord-diagram-in-tableau/">https://datavis.blog/2020/07/02/creating-chord-diagram-in-tableau/</a>).</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>
Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-12.2019)
<p>Sentiment analysis of tech media articles using VADER package and co-occurrence analysis</p> <p>Sources with weights:</p> <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> <p>Methodology</p> <p>The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.</p> <p>As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.</p> <p>The process included the following main steps:</p> <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> <p>Therefore, the procedure has been repeated for 100 words for all identified social issues.</p> <p>The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.</p> <p> </p> <p>*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</p>
Figure 1 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 1. Position of the study area in northeastern Greece (dark dot on the map) and mutual positions of the three study subsites, with the mosaics of individual biotopes. The longest single moves of three study species: Aa, Archon apollinus; Zc, Zerynthia cerisy; Zp, Zerynthia polyxena.
Figure 4 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 4. Results of model for eggs and larval records. (A) Interaction plot showing average egg batch sizes for individual butterfly species on individual species of Aristolochia plants; (B) box-plots (medians and quartiles) showing the amount of canopy closure (variable Trees10: see Material and methods) above Aristolochia plants bearing eggs of the respective butterflies; (C) numbers of larvae of the three studied butterfly species recorded during searches for larvae, note the unbalanced scale on the x-axis; (D) interaction plot showing average number of larvae of the three studied butterfly species in individual instars. For panels (A, C, D) dotted line, Archon apollinus; dashed line, Zerynthia cerisy; full line, Zerynthia polyxena.
Figure 2 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 2. Adults of the studied butterflies: (A) Archon apollinus; (B) Zerynthia cerisy; (C) Zerynthia polyxena (the small inserts stand for host plant species used by the respective species at the study locality); and drawings of their Aristolochia host plants (D) Aristolochia pallida; (E) Aristolochia rotunda; (F) Aristolochia clematitis; (G) Aristolochia hirta; (H) Dissected subterranean Aristolochia hirta flower with A. apollinus first-instar larvae; (I) Habitat mosaic at the Greek Thrace study site, showing a field in the front, and scrub with open forest in the background; (J) Silk-woven Aristolochia hirta leaves with A. apollinus larvae.
Figure 3 in Co-occurrence of three Aristolochia-feeding Papilionids (Archon apollinus, Zerynthia polyxena and Zerynthia cerisy) in Greek Thrace
Figure 3. Estimates of the adult daily population sizes based on mark–recapture data: year 2010, when only data for Archon apollinus (most of flight period) and Zerynthia cerisy (late tail of flight period) allowed the estimation; year 2011, A. apollinus, Z. cerisy, Zerynthia polyxena. The error lines present standard errors of estimates, see Table 3 for model parameters.
Data and code for the manuscript: "Varying richness need not imply non-random species co-occurrence: implications for specifying null models"
<p>Data and R code for the manuscript "Varying richness need not imply non-random species co-occurrence: implications for specifying null models".</p>
Species co-occurrences from EuPMC articles related to pines
<p>A dataset containing info on matches from full text searches by ContentMine tools, that can be mapped to Wikidata. See README.md.</p>
Functional genomics and co-occurrence in a diverse tropical tree genus: The roles of drought and defense related genes
<p>Tropical tree communities are among the most diverse in the world. A small number of genera often disproportionately contribute to this diversity. How so many species from a single genus can co-occur represents a major outstanding question in biology. Niche differences are likely to play a major role in promoting congeneric diversity, but the mechanisms of interest are often not well-characterized by the set of functional traits generally measured by ecologists. To address this knowledge gap, we used a functional genomic approach to investigate the mechanisms of co-occurrence in the hyper-diverse genus <em>Ficus</em>. Our study focused on over 800 genes related to drought and defense, providing detailed information on how these genes may contribute to the diversity of <em>Ficus</em> species. We find widespread and consistent evidence of the importance of defense gene dissimilarity in co-occurring species, providing genetic support for what would be expected under the Janzen-Connell mechanism. We also find that drought-related gene sequence similarity is related to <em>Ficus</em> co-occurrence, indicating that similar responses to drought promote co-occurrence. We provide the first detailed functional genomic evidence of how drought- and defense-related genes simultaneously contribute to the local co-occurrence in a hyper-diverse genus. Our results demonstrate the potential of community transcriptomics to identify the drivers of species co-occurrence in hyper-diverse tropical tree genera.</p>
Co-occurrence of Methods in Co-Creation: A Sankey Diagram
<p><strong>The Co-Creation Methods Sankey Diagram. </strong>This diagram was created in an online open-access tool, RAWGraphs 2.0 (DensityDesign Research Lab). This diagram provides a visual representation of the interrelationships among methods occurring together in the titles and/or abstracts of the sourced literature (n=2,590 citations). These citations are derived from a Systematic Method Overview, which encompasses empirical studies, protocols, exploratory studies, and case studies employing co-creation sourced from the Health CASCADE Co-Creation Database version 1.5. This diagram serves as a snapshot of co-creation practices, and the manuscript about this work is under peer review at JMIR (i-JMR): https://preprints.jmir.org/preprint/59772 </p> <p>For a closer examination of the depicted methods, the image file is available for download, allowing zooming in and out to navigate the intricacies of the diagram. For inquiries or additional information regarding this diagram, please reach out to Danielle M. Agnello at <a href="mailto:danielle.agnello@gcu.ac.uk">danielle.agnello@gcu.ac.uk</a>. Additionally, join the conversation and stay updated her research into co-creation, and methods used in co-creation, by following her on X: <a href="https://twitter.com/DannyAgnello_GH">https://twitter.com/DannyAgnello_GH</a> or LinkedIN: <a href="https://www.linkedin.com/in/daniellemagnello/">https://www.linkedin.com/in/daniellemagnello/ </a> <br><br><strong>Co-Creation Resources: </strong>For additional support in utilizing these co-creation methods in a co-creation process, explore our <em>Draft Evidence-based Co-Creation Guideline: PRODUCES+ </em>at: <a href="../records/8379784">https://zenodo.org/records/8379784</a><em>. </em>Additionally, engage with critical questions about method selection through my <em>Methods Selector Infographic</em> at: <a href="https://doi.org/10.5281/zenodo.7414470">https://doi.org/10.5281/zenodo.7414470</a>. For insights into how these methods align with co-creation characteristics, please refer to the pre-print manuscript on the Co-Creation Rainbow framework: <a href="../records/10391410">https://zenodo.org/records/10391410</a>. Finally, you can also conduct your study in the <em>Health CASCADE Co-Creation Database</em>: <a href="https://doi.org/10.2196/45059">https://doi.org/10.2196/45059</a>. Unlock the potential of co-creation and embark on a collaborative research journey with confidence, creativity, and innovation!</p>
Intraguild interactions and abiotic conditions mediate occupancy of mammalian carnivores: co-occurrence of coyotes-fishers-martens
<p>The widespread eradication of large carnivores and subsequent expansion of top mesopredators have the potential to impact species and community interactions with ecosystem-wide implications. An example of these trophic dynamics is the widespread establishment of coyotes following the extirpation of wolves and mountain lions in eastern North America. Here, we examined the occupancy of three carnivores in northern New York considering both environmental/habitat factors and interspecific interactions. We estimated the co-occurrence of coyotes, fishers, and martens from a landscape-scale winter camera trap survey repeatedly annually for three years. Martens occurred independently of both coyotes and fishers, while fishers and coyotes displayed positive intraguild interactions that were constant across the landscape. Both marten and fisher first-order occupancy was driven by a combination of biotic and abiotic factors, with both species displaying positive associations with forest cover but antithetical responses to average snow depth. The integral and antithetical role of snow depth in driving the occurrence of martens (positive) and fishers (negative) in the landscape indicates that future climatic warming could reduce the availability of current spatial refuges for martens created by severe winter conditions. Climate-driven alterations to established competitive interactions and co-existence patterns between marten and fishers have critical implications for the species' survival and conservation. We provide correlational evidence consistent with the potential for positive top-down effects of dominant mesocarnivores on subordinate species, with fisher occupancy increasing conditional on the presence of coyotes across the landscape. These findings align with the hypothesis that under certain conditions, coyotes may facilitate certain subordinate carnivores. The evidence produced here is consistent with hypotheses on the dynamic nature of trophic niches. We demonstrate the need to consider the interplay between climate, habitat, and interspecific interactions to understand wildlife occupancy patterns and inform wildlife management in a rapidly changing world.</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.