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39 results for “Scientific Literature”
RegEl corpus: Identifying DNA regulatory elements in the scientific literature
<pre>@article{garda2022regel, title={RegEl corpus: identifying DNA regulatory elements in the scientific literature}, author={Garda, Samuele and Lenihan-Geels, Freyda and Proft, Sebastian and Hochmuth, Stefanie and Sch{\"u}lke, Markus and Seelow, Dominik and Leser, Ulf}, journal={Database}, volume={2022}, year={2022}, publisher={Oxford Academic} } </pre>
Irreproducibility in searches of scientific literature: a comparative analysis
<p>1. Repeatability is the cornerstone of science and it is particularly important for systematic reviews. However, little is known on how researchers' choice of database and search platform influence the repeatability of systematic reviews. Here, we aim to unveil how the computer environment and the location where the search was initiated from influence hit results.</p> <p>2. We present a comparative analysis of time-synchronized searches at different institutional locations in the world, and evaluate the consistency of hits obtained within each of the search terms using different search platforms.</p> <p>3. We revealed a large variation among search platforms and showed that PubMed and Scopus returned consistent results to identical search strings from different locations. Google Scholar and Web of Science's Core Collection varied substantially both in the number of returned hits and in the list of individual articles depending on the search location and computing environment. Inconsistency in Web of Science results has most likely emerged from the different licensing packages at different institutions.</p> <p>4. To maintain scientific integrity and consistency, especially in systematic reviews, action is needed from both the scientific community and scientific search platforms to increase search consistency. Researchers are encouraged to report the search location and the databases used for systematic reviews, and database providers should make search algorithms transparent and revise access rules to titles behind paywalls. Additional options for increasing the repeatability and transparency of systematic reviews are storing both search metadata and hit results in open repositories and using Application Programming Interfaces (APIs) to retrieve standardized, machine-readable search metadata.</p>
Data for "RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature"
<div> <p><strong>RegulaTome corpus</strong>: this <a href="../api/records/10808330/files/RegulaTome-corpus.tar.gz/content" target="_blank" rel="noopener">file</a> contains the RegulaTome corpus in <a href="https://brat.nlplab.org/">BRAT</a> format. The directory <strong>"splits" </strong>has the corpus split based on the train/dev/test used for the training of the relation extraction system</p> <p><strong>RegulaTome annodoc</strong>: The annotation guidelines along with the annotation configuration files for BRAT are provided in <a href="../api/records/10808330/files/annodoc+config.tar.gz/content" target="_blank" rel="noopener">annodoc+config.tar.gz</a>. The online version of the annotation documentation can be found here: <a href="https://katnastou.github.io/s1000-corpus-annotation-guidelines/">https://katnastou.github.io/regulatome-annodoc/ </a></p> <p>The tagger software can be found here: <a href="https://github.com/larsjuhljensen/tagger">https://github.com/larsjuhljensen/tagger</a>. The command used to run tagger before large-scale execution of the RE system is:</p> <p><code>gzip -cd `ls -1 pmc/*.en.merged.filtered.tsv.gz` `ls -1r pubmed/*.tsv.gz` | cat dictionary/excluded_documents.txt - | tagger/tagcorpus --threads=16 --autodetect --types=dictionary/curated_types.tsv --entities=dictionary/all_entities.tsv --names=dictionary/all_names_textmining.tsv --groups=dictionary/all_groups.tsv --stopwords=dictionary/all_global.tsv --local-stopwords=dictionary/all_local.tsv --type-pairs=dictionary/all_type_pairs.tsv --out-matches=all_matches.tsv</code></p> <p><strong>Input documents </strong>for large-scale execution, which is done on entire <a href="https://a3s.fi/March-2024-PubMed/PubMed_20230314.tar.gz" target="_blank" rel="noopener">PubMed</a> (as of March 2024) and <a href="https://a3s.fi/Jan-2024-documents/PMC_Nov_23.tar.gz" target="_blank" rel="noopener">PMC Open Access</a> (as of November 2023) articles in BioC format. The files are converted to a <a href="https://a3s.fi/March-2024-PubMed/all_documents.tsv" target="_blank" rel="noopener">tab-delimited format </a>to be compatible with the RE system input (see below).</p> <p><strong>Input dictionary files</strong>: all the files necessary to execute the command above are available in <a href="../api/records/10808330/files/tagger_dictionary_files.tar.gz/content" target="_blank" rel="noopener">tagger_dictionary_files.tar.gz </a></p> <p><strong>Tagger output</strong>: we filter the results of the tagger run down to gene/protein hits, and documents with more than 1 hit (since we are doing relation extraction) before feeding it to our RE system. The filtered output is available in <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a></p> <p><strong>Relation extraction system input</strong>: <a href="../api/records/10808330/files/combined_input_for_re.tar.gz/content" target="_blank" rel="noopener">combined_input_for_re.tar.gz</a>: these are the directories with all the .ann and .txt files used as input for the large-scale execution of the relation extraction pipeline. The files are generated from the tagger tsv output (see above, <a href="../api/records/10808330/files/tagger_matches_ggp_only_gt_1_hit.tsv.gz/content" target="_blank" rel="noopener">tagger_matches_ggp_only_gt_1_hit.tsv.gz</a>) using the <a href="https://github.com/spyysalo/string-db-tools/blob/main/tagger2standoff.py">tagger2standoff.py</a> script from the <a href="https://github.com/spyysalo/string-db-tools/">string-db-tools</a> repository.</p> <p><strong>Relation extraction models</strong>. The Transformer-based model used for large-scale relation extraction and prediction on the test set is at <a href="../api/records/10808330/files/relation_extraction_multi-label-best_model.tar.gz/content" target="_blank" rel="noopener">relation_extraction_multi-label-best_model.tar.gz</a></p> <p>The pre-trained RoBERTa model on PubMed and PMC and MIMIC-III with a BPE Vocab learned from PubMed (RoBERTa-large-PM-M3-Voc), which is used by our system is available <a href="https://github.com/facebookresearch/bio-lm/blob/main/README.md">here</a>.</p> <p><strong>Relation extraction system output</strong>: the tab-delimited outputs of the relation extraction system are found at <a href="https://a3s.fi/regulatome-ls/large_scale_relation_extraction_results.tar.gz" target="_blank" rel="noopener">large_scale_relation_extraction_results.tar.gz </a><strong>!!!ATTENTION this file is approximately 1TB in size, so make sure you have enough space to download it on your machine!!!</strong></p> <p>The relation extraction system output files have 86 columns: PMID, Entity BRAT ID1, Entity BRAT ID2, and scores per class produced by the relation extraction model. Each file has a header to denote which score is in which column.</p> </div>
Mining tortured abbreviations from the scientific literature
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Bibliometric analysis of global scientific literature on effects of COVID-19 pandemic on domestic violence
<p>Raw data for Bibliometric analysis of global scientific literature on effects of COVID-19 pandemic on domestic violence</p>
Label-free data mining of scientific literature by unsupervised syntactic distance analysis
<p>1. OER.zip: literature resources, mined data and manual annotation of OER</p> <p>2. OLED.zip: literature resources and mined data of OLED</p> <p>3. PVK_ligand.zip: literature resources, mined data and manual annotation of perovskite ligand</p> <p>4. PVK_solvent.zip: literature resources, mined data and manual annotation of perovskite solvent</p> <p>5. syn_vec.part01.rar - syn_vec.part01.rar: word vector model trained by synthesis paragraphs in USPTO</p> <p>6. crossref_vec.part01.rar - crossref_vec.part10.rar: word vector trained by abstracts in Crossref</p>
A subset of the Scientific Literature Comparison Tables Dataset
<p>The Scientific Literature Comparison Table (SLCT) dataset was collected using the arXiv and Semantic Scholar APIs. It underwent a series of processing steps, including manual inspection and editing.</p> <p>The processing steps are summarized as follows:</p> <p>1) Downloading Survey Papers’ LaTeX files using the Arxiv API.</p> <p>2) Preprocessing LaTeX files to HTML format.</p> <p>3) Extracting tables from the HTML files.</p> <p>4) Creating a Golden Table as a reference.</p> <p>5) Generating descriptions for column headers.</p> <p>6) Acquiring citation data.</p> <p>7) Finalizing the dataset.</p>
Dashboard "Communication technologies used and parameters measured in Food Supply Chains in the scientific literature"
<p>This dashboard collects information about the communication technologies used and parameters measured in several scientific papers. Data can be filtered according to different criteria:</p> <ol> <li>Year of publication of the paper.</li> <li>Main topic in which the objetive of the paper is framed within.</li> <li>Stage of the food supply chain where the study is developed.</li> <li>Product group according to the Classification of Products by Activity (CPA) of the European Union.</li> </ol> <p>This dashboard was created by Manuel Amador Cervera for the University of Deusto in the context of the Horizon 2020 project "FOODRUS". The information contained in it refers to the scientific publications outlined in the table that can be found in the "References" sheet.<br> This project has received funding from the European Union’s Horizon 2020 Research & Innovation programme under Grant Agreement no. 101000617. This dashboard is the sole responsibility of the University of Deusto, and the European Union or any user is not liable for any use that may be made of the information contained therein.</p>
Irreproducibility in searches of scientific literature: a comparative analysis
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Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
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Scientific literature, molecular-interaction and chemical features of phytochemicals and drugs.
<p>Scientific literature, molecular-interaction and chemical features of phytochemicals and drugs.</p>
Test set - 4023 PubMed abstracts (for manuscript: Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait )
<p>A .zip archive containing the set of abstracts used in the test set (4023 abstracts from PubMed) in .txt format.</p> <p>This archive contains supplementary files for the manuscript Extracting knowledge networks from plant scientific literature: Potato tuber flesh color as an exemplary trait.</p>
WheatGenomicsSLKG: Wheat Genomics Scientific Literature Knowledge Graph
<p>The <strong>Wheat Genomics Scientific Literature Knowledge Graph </strong>(<strong>WheatGenomicsSLKG</strong>) is a FAIR knowledge graph that exploits the Semantic Web technologies to integrate information about Named Entities (NE) extracted automatically from a corpus of PubMed scientific papers on wheat genetics and genomics.</p> <p>Code an details: https://github.com/Wimmics/WheatGenomicsKG</p>
Supplementary material 1 from: Ramírez-Albores JE, Badano EI, Flores J, Flores-Flores JL, Yáñez-Espinosa L (2019) Scientific literature on invasive alien species in a megadiverse country: advances and challenges in Mexico. NeoBiota 48: 113-127. https://doi.org/10.3897/neobiota.48.36201
: Data type: species data
Authorship information of open educational resources derived from peer-reviewed scientific literature
<p>This dataset evaluates the authorship of open educational resources available at 5 STEM resource web sites (EcoEd Digital Library, CourseSource, Teaching Issues and Experiments in Ecology, Teach the Earth, and the National Center for Case Study Teaching in Science). Using 20 exercises from each site, it evaluates the proportion of exercises that draw on peer-reviewed scientific studies; of those, it also evaluates the proportion in which any author of the teaching exercise overlaps with any author of a scientific article from which the open educational resource is derived. The overall goal is to evaluate overlap in authorship between scientific publications and associated open educational resources. The first tab shows how the studies were selected and the second tab presents authorship data for each study.</p>
The MAPLE Benchmark for Scientific Literature Tagging
<p>This repository contains the MAPLE benchmark introduced in the WWW 2023 paper "The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study", available at <a href="http://arxiv.org/abs/2302.03341">https://arxiv.org/abs/2302.03341</a>. It covers papers from 19 scientific fields for large-scale multi-label scientific paper classification.</p> <p>Please refer to <a href="http://github.com/yuzhimanhua/MAPLE">https://github.com/yuzhimanhua/MAPLE</a> for more details on the data format and usage.</p> <p>If you would like to use MAPLE for graph mining tasks (e.g., node classification, link prediction), please refer to the graph format of MAPLE: <a href="http://zenodo.org/record/7797563">https://zenodo.org/record/7797563</a></p> <p>If you find MAPLE useful, please cite our paper:</p> <pre><code>@inproceedings{zhang2023effect, title={The effect of metadata on scientific literature tagging: A cross-field cross-model study}, author={Zhang, Yu and Jin, Bowen and Zhu, Qi and Meng, Yu and Han, Jiawei}, booktitle={WWW'23}, pages={1626--1637}, year={2023} }</code></pre>
The Use of FHIR in Digital Health – a Review of the Scientific Literature: list of publications
<p>This file contains the publications that were included in the literature review "The Use of FHIR in Digital Health – a Review of the Scientific Literature" (<a href="https://doi.org/10.3233/SHTI190805">https://doi.org/10.3233/SHTI190805</a>).</p>
Authorship information of open educational resources derived from peer-reviewed scientific literature
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Understanding multidisciplinary product development through a synthesis mapping of approaches, processes, methods and tools from the scientific literature - Searchable map
<p>This document introduces a synthesis map of approaches, processes, methods and tools for multidisciplinary product development. The presented map is associated with an open-access article – see the citation box below – and differs from the one provided in the article in that it is searchable, which makes it easier to pinpoint the approaches, processes, methods and tools. This document comprises a legend and the map. </p> <p>To contextualize the map, multidisciplinary products arise from the integration of contributions from mechanical, electrical, electronics, software, and broadly information and communication technologies. <br>This “multidisciplinarity” implies a higher technical and organizational complexity that can invite companies to adapt their development. To support companies in the adaptation of their development and navigate the dense and fragmented scientific literature corpus on multidisciplinary product development, the authors proposed to graphically organize it.</p> <p>In a first phase, multidisciplinary product development was investigated by analyzing three specific types of multidisciplinary products that can be referred to as “cyber-physical systems”, “mechatronics”, and “smart products and systems” in the literature. This first phase led to three maps which graphically organized a total of 236 “concepts and techniques” identified from 167 scientific papers through an extensive literature review and categorized based on a four-level model paired with a decision tree. A second phase, which result is represented below, introduced some simplifications and filters to the initial maps, which narrow down the number of concepts and techniques to 56. The following map represents a preliminary database of concepts and techniques for multidisciplinary product development and serves to support companies in their transformation from the product development perspective by providing them with a synthesized overview of the related literature. This work is particularly suited for companies and researchers looking for getting acquainted with the scientific literature related to multidisciplinary product development and how the different concepts and techniques can be associated.</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.