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54 results for “Text Mining”
Invasion Biology WikiProject Scientific Papers: Text Data Mining and LLM-based Information Extraction of Species, Locations, Habitats, and Ecosystems
<p>This dataset contains the abstract and full-text for publication DOIs from the Invasion Biology WikiProject (DOI: <a href="https://www.doi.org/10.5281/zenodo.12518036">10.5281/zenodo.12518036</a>). The data was retrieved using the <a href="https://ask.orkg.org/">ask.orkg.org</a> <a href="https://api.ask.orkg.org/docs#tag/Semantic-Neural-Search/operation/explore_documents_index_explore_get">API</a>. For the <a href="https://github.com/jd-coderepos/invasion-biology-IE/blob/main/scripts/ask-doi-list-fulltext-search.py">script</a> used to obtain the data, refer to the accompanying GitHub repository: <a href="https://github.com/jd-coderepos/invasion-biology-IE/" target="_blank" rel="noopener">https://github.com/jd-coderepos/invasion-biology-IE/</a>.</p> <p>The resulting CSV file includes the following fields: <code>"ASK ID"</code>, <code>"DOI"</code>, <code>"Title"</code>, <code>"Abstract"</code>, and <code>"Full-text"</code>.</p> <p>Of the 49,438 queried DOIs, the ASK database provided:</p> <ul> <li><strong>Total DOIs processed:</strong> 12,636</li> <li><strong>DOIs with neither abstract nor full-text:</strong> 36 (abstract token count was less than 10)</li> <li><strong>DOIs with abstracts but no full-text:</strong> 12,636</li> <li><strong>DOIs with both abstract and full-text:</strong> 2,834</li> </ul> <p>The second part of the dataset contains structured information extracted from the publications using the GPT-4o Large Language Model. This structured data is included in the zipped folder <code>structured-publications.zip</code>.</p> <p>The accompanying GitHub repository provides access to the code and scripts used at various stages of the information extraction (IE) process.</p> <p><strong>Theme of the Study:</strong><br>"Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models."</p>
Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction [dataset]
<p>This dataset contains the extension of a publicly available dataset that was published initially by Ferenc et al. in their paper:</p> <p><em>“Ferenc, R.; Hegedus, P.; Gyimesi, P.; Antal, G.; Bán, D.; Gyimóthy, T. Challenging machine learning algorithms in predicting vulnerable javascript functions. 2019 IEEE/ACM 7th InternationalWorkshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). IEEE, 2019, pp. 8–14.”</em></p> <p>The dataset contained software metrics for source code functions written in JavaScript (JS) programming language. Each function was labeled as vulnerable or clean. The authors gathered vulnerabilities from publicly available vulnerability databases.</p> <p>In our paper entitled: “<strong>Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction</strong>” and cited as:</p> <p><em>“Kalouptsoglou I, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A. Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction. Entropy. 2022; 24(5):651. <a href="https://doi.org/10.3390/e24050651">https://doi.org/10.3390/e24050651</a>”</em></p> <p>, we presented an extended version of the dataset by extracting textual features for the labeled JS functions. In particular, we got the dataset provided by Ferenc et al. in CSV format and then we gathered all the GitHub URLs of the dataset's functions (i.e., methods). Using these URLs, we collected the source code of the corresponding JS files from GitHub. Subsequently, by utilizing the start and end line information for every function, we cut off the code of the functions. Each function was then tokenized to construct a list of tokens per function.</p> <p>To extract text features, we used a text mining technique called sequences of tokens. As a result, we created a repository with all methods' source code, the token sequences of each method, and their labels. To boost the generalizability of type-specific tokens, all comments were eliminated, as well as all integers and strings, which were replaced with two unique IDs.</p> <p>The dataset contains 12,106 JavaScript functions, from which 1,493 are considered vulnerable.</p> <p>This dataset was created and utilized during the Vulnerability Prediction Task of the Horizon2020 IoTAC Project as training and evaluation data for the construction of vulnerability prediction models. The dataset is provided in the csv format. Each row of the csv file has the following parts:</p> <ul> <li>Label: Flag with values ‘1’ for vulnerable and ‘0’ for non-vulnerable methods</li> <li>Name: The name of the JavaScript method</li> <li>Longname: The longname of the JavaScript method</li> <li>Path: The path of the file of the method in the repository</li> <li>Full_repo_path: The GitHub URL of the file of the method</li> <li>TokenX: Each next row corresponds to each token included in the method</li> </ul>
Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
<p>Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.</p>
Time-resolved compound repositioning predictions on a text-mined knowledge network
<p><strong>gs_positives.csv</strong>: The re-processed version of DrugCentral indications, utilized as training and testing positives in the analysis.</p> <p><strong>top_5000_predictions.csv</strong>: The top 5000 drug-disease pairs, by probability, produced by this analysis pipeline.</p> <p><strong>file_info.txt</strong>: Information about the column headings in of the two files.</p> <p> </p>
Umfrageergebnisse "Bedarf und Anforderungen an Ressourcen für Text und Data Mining"
<p>Daten der Umfrage "Bedarf und Anforderungen an Ressourcen für Text und Data Mining" der Schwerpunktinitiative "Digitale Information" der Allianz der deutschen Wissenschaftsorganisationen, Arbeitsgruppe Text und Data Mining, vom Mai 2015</p>
BioVAE: a pre-trained latent variable language model for biomedical text mining
<p>We release BioVAE, the first large-scale pre-trained latent variable language model for the biomedical domain, which uses the OPTIMUS framework to train on large volumes of biomedical text.</p> <p>This version contains the pre-trained models for text mining tasks such as named entity recognition or relation extraction, and text generation task.</p> <p>Explanation of each file: (lt32: latent_size = 32, beta05: beta=0.5)</p> <ul> <li>pm-full-lt32-beta00</li> <li>pm-full-lt32-beta05</li> <li>pm-full-lt768-beta00</li> <li>pm-full-lt768-beta05</li> <li>pm-full-generation</li> </ul>
Text-mining-derived cancer associations - PMED
<p>JSON file containing gene::cancer, variant::cancer and gene/variant::cancer:drug-response associations derived from an in-house text-mining pipeline utilizing full-text articles from PMC.</p>
Classification of unstructured text in types of violence against women using text mining and Machine learning techniques
<p>These are the data used for the development of the investigation.</p> <p>This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.</p>
A topic modelling application for violence against the women through text mining techniques
<p>This research was focused on a social problem that affects worldwide. Violence Against Women (VAW) and girls occurs in many forms with different levels of aggressiveness. There is a wide amount of data on the internet about real cases of VAW that can be gathered for further study. Many researchers have done studies on structured data sets which facilitates their manipulation and understanding. But in this work digital news (data source large non-structured text) was used.</p> <p>These are the data used for the development of the investigation.This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.</p>
Text-fig. 4. Porechye open pit coal mine. a: photography of the Lipovtsy Formation section; b: stratigraphic column, 1 – sandstone, 2 – conglomerate, 3 – coal, 4 – tuff dated by U-Th-Pb geochronology, 5 – siltstone, 6 – mudstone, 7 – palynological samples with angiosperm pollen, 8 – dispersed angiosperm cuticles. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia
Text-fig. 4. Porechye open pit coal mine. a: photography of the Lipovtsy Formation section; b: stratigraphic column, 1 – sandstone, 2 – conglomerate, 3 – coal, 4 – tuff dated by U-Th-Pb geochronology, 5 – siltstone, 6 – mudstone, 7 – palynological samples with angiosperm pollen, 8 – dispersed angiosperm cuticles.
Text-fig. 3. Map of Razdolnaya coal basin with early angiosperm localities. a: Lipovtsy Formation, Aptian; b: Galenki Formation, early-middle Albian. 1 – Porechye coal mine; 2 – Konstantinovka; 3 – Aleksee-Nikolskoe coal mine; 4 – Fedorovsky Rudnik; 5 – Podgorodenka coalfield; 6 – Firsov Cape. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia
Text-fig. 3. Map of Razdolnaya coal basin with early angiosperm localities. a: Lipovtsy Formation, Aptian; b: Galenki Formation, early-middle Albian. 1 – Porechye coal mine; 2 – Konstantinovka; 3 – Aleksee-Nikolskoe coal mine; 4 – Fedorovsky Rudnik; 5 – Podgorodenka coalfield; 6 – Firsov Cape.
Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s).
Text-fig. 5. a: coal seam "Rabochy" with three tuff layers in upper part of Lipovtsy Formation in Porechye open pit coal mine; b: same section, enlarged view, upper tuff layer, sample 160/4 site; c: tuff layer in upper part of Frentsevka Formation below conglomerate at Palets Cape; d: same section, enlarged view, sample 1 site. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia
Text-fig. 5. a: coal seam "Rabochy" with three tuff layers in upper part of Lipovtsy Formation in Porechye open pit coal mine; b: same section, enlarged view, upper tuff layer, sample 160/4 site; c: tuff layer in upper part of Frentsevka Formation below conglomerate at Palets Cape; d: same section, enlarged view, sample 1 site.
Appendix for Review Paper Entitled: "A literature review of "lawful" text and data mining."
<p><span>This appendix complements the review paper entitled ““A literature review of “lawful” text and data mining” with 8 Tables highlighting which scholarly works were used for each section of the literature review, but also how those works were used. </span></p>
Text-fig. 4. Dendrogram (Ward's method, squared Euclidean distance) showing the relationship between the studied fossil vegetation assemblages of Hrádek/N. (48), Wackersdorf (49), Berzdorf and Wiesa (50) and the Mydlovary Fm. (51) and the studied modern vegetation units from SE China and Japan (Teodoridis et al. 2011a, 2012, Appendix – this volume). in A Review Of The Early Miocene Mastixioid Flora Of The Kristina Mine At Hrádek Nad Nisou In North Bohemia (The Czech Republic)
Text-fig. 4. Dendrogram (Ward's method, squared Euclidean distance) showing the relationship between the studied fossil vegetation assemblages of Hrádek/N. (48), Wackersdorf (49), Berzdorf and Wiesa (50) and the Mydlovary Fm. (51) and the studied modern vegetation units from SE China and Japan (Teodoridis et al. 2011a, 2012, Appendix – this volume).
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified). in A Review Of The Early Miocene Mastixioid Flora Of The Kristina Mine At Hrádek Nad Nisou In North Bohemia (The Czech Republic)
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified).
Text-fig. 10. Ulmaceae. Ulmites microphylla (NEWBERRY) comb. nov. Twig with distichously attached leaves, Black Buttes Mine pit 3, Wyoming, UF 15886-14248. Scale bar = 1 cm. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. 10. Ulmaceae. Ulmites microphylla (NEWBERRY) comb. nov. Twig with distichously attached leaves, Black Buttes Mine pit 3, Wyoming, UF 15886-14248. Scale bar = 1 cm.
Appendix for Review Paper Entitled: "A literature review of "lawful" text and data mining."
<p>This appendix complements the review paper entitled ““A literature review of “lawful” text and data mining” with 8 Tables highlighting which scholarly works were used for each section of the literature review, but also how those works were used.</p>
DrugProt corpus: Biocreative VII Track 1 - Text mining drug and chemical-protein interactions
<p>Gold Standard annotations of the DrugProt corpus (training and development sets). Also, test and background sets.</p><p> </p><p><strong>Please cite if you use any DrugProt resource:</strong></p><p>Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, <i>Database</i>, Volume 2023, 2023, baad080</p><blockquote><p><i>@article{miranda2023overview, title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations}, author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, journal={Database}, volume={2023}, pages={baad080}, year={2023}, publisher={Oxford University Press UK} }</i></p></blockquote><p>Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." <i>Proceedings of the seventh BioCreative challenge evaluation workshop</i>. 2021.</p><blockquote><p><i>@inproceedings{miranda2021overview, title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations}, author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop}, year={2021} }</i></p></blockquote><p> </p><p><strong>Introduction</strong></p><p>The aim of the DrugProt track (similar to the previous CHEMPROT task of BioCreative VI) is to promote the development and evaluation of systems that are able to automatically detect in relations between chemical compounds/drug and genes/proteins. We have therefore generated a manually annotated corpus, the <i>DrugProt corpus</i>, where domain experts have exhaustively labeled:(a) all chemical and gene mentions, and (b) all binary relationships between them corresponding to a specific set of biologically relevant relation types (<i>DrugProt relation classes</i>). There is also an increasing interested in the integration of chemical and biomedical data understood as curation of relationships between biological and chemical entities from text and storing such information in form of structured annotation databases. Such databases are of key relevance not only for biological but also for pharmacological and clinical research. A range of different types chemical-protein/gene interactions are of key relevance for biology, including metabolic relations (e.g. substrates, products) inhibition, binding or induction associations.</p><p>The DrugProt track aims to address these needs and to promote the development of systems able to extract chemical-protein interactions that might be of relevance for precision medicine as well as for drug discovery and basic biomedical research.</p><p>The DrugProt track in BioCreative VII (BC VII) will explore recognition of chemical-protein entity relations from abstracts.</p><p>Teams participating in this track are provided with:</p><ul><li>PubMed abstracts</li><li>Manually annotated chemical compound mentions</li><li>Manually annotated gene/protein mentions</li><li>Manually annotated chemical compound-protein relations</li></ul><p> </p><p><strong>Zip structure:</strong></p><ul><li>Training set folder with<ul><li>drugprot_training_abstracts.tsv: PubMed records</li><li>drugprot_training_entities.tsv: manually labeled mention annotations of chemical compounds and genes/proteins</li><li>drugprot_training_relations.tsv: chemical-protein relation annotations</li></ul></li><li>Development set folder with<ul><li>drugprot_development_abstracts.tsv</li><li>drugprot_development_entities.tsv</li><li>drugprot_development_relations.tsv</li></ul></li><li>Test+background set folder with<ul><li>test_background_abstracts.tsv</li><li>test_background_entities.tsv</li></ul></li></ul><p> </p><p><strong>Data format description</strong></p><p>The <strong>input text files</strong> for the DrugProt track are plain-text, UTF8-encoded PubMed records in a tab-separated format with the following three columns:</p><ol><li>Article identifier (PMID, PubMed identifier)</li><li>Title of the article</li><li>Abstract of the article</li></ol><p> </p><p>DrugProt <strong>entity mention annotation files</strong> contain manually labeled mention annotations of chemical compounds and genes/proteins. Such files consist of tab-separated fields containing the following six columns:</p><ol><li>Article identifier (PMID)</li><li>Term number (for this record)</li><li>Type of entity mention (CHEMICAL, GENE-Y, GENE-N)</li><li>Start character offset of the entity mention</li><li>End character offset of the entity mention</li><li>Text string of the entity mention</li></ol><p>Each line contains one entity, and <i>each entity is uniquely identified by its PMID and the Term Number</i>. Besides, each annotation contains an annotation type, the start-offset -the index of the first character of the annotated span in the text-, the end-offset -the index of the first character after the annotated span- and the text spanned by the annotation.</p><p>Example DrugProt <i>training</i> entity mention annotations:</p><p>11808879 T1 GENE-Y 1860 1866 KIR6.2 11808879 T2 GENE-N 1993 2016 glutamate dehydrogenase 11808879 T3 GENE-Y 2242 2253 glucokinase 23017395 T1 CHEMICAL 216 223 HMG-CoA 23017395 T2 CHEMICAL 258 261 EPA</p><p> </p><p>Example DrugProt <i>development</i> entity mention annotations (no distinction between GENE-Y and GENE-N):</p><p>11808879 T1 GENE 1860 1866 KIR6.2 11808879 T2 GENE 1993 2016 glutamate dehydrogenase 11808879 T3 GENE 2242 2253 glucokinase 23017395 T1 CHEMICAL 216 223 HMG-CoA 23017395 T2 CHEMICAL 258 261 EPA</p><p><br>DrugProt <strong>relation annotations</strong> are distributed as a file that contains the detailed chemical-protein relation annotations prepared for the DrugProt track. There are no relation annotations for the test+background set (the goal of the task is to predict them). It consists of tab-separated columns containing:</p><ol><li>Article identifier (PMID)</li><li>DrugProt relation</li><li>Interactor argument 1 (<i>of type CHEMICAL</i>)</li><li>Interactor argument 2 (<i>of type GENE</i>)</li></ol><p>Each line contains one relation, and <i>each relation is identified by the PMID, the relation type and the two related entities</i>. In the below example, to find the entities involved in the first relation, you must find the entities with Term Identifier T1 and T52 <i>within the PMID 12488248.</i></p><p>Example DrugProt relation annotations:</p><p>12488248 INHIBITOR Arg1:T1 Arg2:T52 12488248 INHIBITOR Arg1:T2 Arg2:T52 23220562 ACTIVATOR Arg1:T12 Arg2:T42 23220562 ACTIVATOR Arg1:T12 Arg2:T43 23220562 INDIRECT-DOWNREGULATOR Arg1:T1 Arg2:T14</p><p> </p><p>Please, cite:</p><p>@inproceedings{krallinger2017overview, title={Overview of the BioCreative VI chemical-protein interaction Track}, author={Krallinger, Martin and Rabal, Obdulia and Akhondi, Saber A and P{\'e}rez, Mart{\i}n P{\'e}rez and Santamar{\'\i}a, Jes{\'u}s and Rodr{\'\i}guez, Gael P{\'e}rez and others}, booktitle={Proceedings of the sixth BioCreative challenge evaluation workshop}, volume={1}, pages={141--146}, year={2017}}</p><p> </p><p><strong>Summary statistics:</strong></p><p>Training set Development set Documents 3500 750 Tokens 1001168 199620 Annotated Entities 89529 18858 Annotated Relations 17288 3765</p><p> </p><p>Annotated Entities:</p><p>Training Entities Development Entities CHEMICAL 46274 9853 GENE-Y [Normalizable] 28421 - GENE-N [Non-Normalizable] 14834 - Gene Total (N+Y) 43255 9005 Total 89529 18858</p><p> </p><p>Annotated Relations:</p><p>Training Relations Development Relations INDIRECT-DOWNREGULATOR 1330 332 INDIRECT-UPREGULATOR 1379 302 DIRECT-REGULATOR 2250 458 ACTIVATOR 1429 246 INHIBITOR 5392 1152 AGONIST 659 131 AGONIST-ACTIVATOR 29 10 AGONIST-INHIBITOR 13 2 ANTAGONIST 972 218 PRODUCT-OF 921 158 SUBSTRATE 2003 495 SUBSTRATE_PRODUCT-OF 25 3 PART-OF 886 258 Total 17288 3765</p><p> </p><p>For further information, please visit <a href="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/">https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/</a> or email us at krallinger.martin@gmail.com and antoniomiresc@gmail.com</p><p> </p><p><strong>Related resources:</strong></p><ul><li><a href="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/">Web</a></li><li><a href="https://github.com/tonifuc3m/drugprot-evaluation-library">Evaluation library</a></li><li><a href="https://codalab.lisn.upsaclay.fr/competitions/8293">Online evaluation (CodaLab)</a></li><li><a href="https://doi.org/10.5281/zenodo.4957137">Relation annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957576">Gene and protein annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957518">Chemicals and drugs annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.7252201">DrugProt Silver Standard Knowledge Graph</a></li><li><a href="https://doi.org/10.5281/zenodo.5042178">FAQ</a></li><li><a href="https://doi.org/10.5281/zenodo.5119878">DrugProt Large Scale Additional SubTrack</a></li><li><a href="https://doi.org/10.5281/zenodo.5656991">DrugProt Large Scale document collection protocol</a></li><li><a href="https://doi.org/10.5281/zenodo.8246229">DrugProt Complete PubMed Knowledge Graph</a><br> </li></ul>
Text-fig. 2. Staniantsi open cast mine seen from south-east (a) and in a more detailed view from the south (b). Most of the studied castorid material originates from the black coal bearing areas (swamp facies). in Castor-Like Postcranial Adaptation In An Uppermost Miocene Beaver From The Staniantsi Basin (Nw Bulgaria)
Text-fig. 2. Staniantsi open cast mine seen from south-east (a) and in a more detailed view from the south (b). Most of the studied castorid material originates from the black coal bearing areas (swamp facies).
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.