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23 results for “Scientific articles”
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Indexed scientific articles for the seven journals listed on the Design Society website and all papers indexed for the DESIGN and ICED conferences
<p>Includes all articles indexed by Scopus® for the seven journals listed on the Design Society website and all papers indexed for DESIGN and ICED (accessed on 05/Nov/2016).</p> <p>The full search query used to extract the articles is:</p> <p>"( SRCTITLE ( "Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM" OR "Journal of Engineering Design" OR "Design Studies" OR "Research in Engineering Design" OR "CoDesign" OR "Journal of Design Research" OR "Design Science Journal" OR "The International Journal of Design Creativity and Innovation" OR "International Conference on Engineering Design" OR "INTERNATIONAL DESIGN CONFERENCE" ) ) AND ( "data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*" ) AND ( EXCLUDE ( EXACTSRCTITLE , "Hardware Software Codesign Proceedings Of The International Workshop" ) OR EXCLUDE ( EXACTSRCTITLE , "Journal Of Engineering Design And Technology" ) OR EXCLUDE ( EXACTSRCTITLE , "Research In Engineering Design Theory Applications And Concurrent Engineering" ) OR EXCLUDE ( EXACTSRCTITLE , "Chinese Journal Of Engineering Design" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2005 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 12 Proceedings Of The 10th ACM International Conference On Hardware Software Codesign And System Synthesis Co Located With Esweek" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2006 Proceedings Of The 4th International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2007 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2008 Proceedings Of The 6th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2008" ) OR EXCLUDE ( EXACTSRCTITLE , "Second IEEE ACM IFIP International Conference On Hardware Software Codesign And Systems Synthesis Codes Isss 2004" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2011 Esweek 2011 Proceedings Of The 9th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 11" ) OR EXCLUDE ( EXACTSRCTITLE , "2010 IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2010" ) OR EXCLUDE ( EXACTSRCTITLE , "2013 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "2014 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2014" ) OR EXCLUDE ( EXACTSRCTITLE , "2015 ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "8th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2010" ) ) AND ( EXCLUDE ( EXACTSRCTITLE , "2015 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "9th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2011" ) OR EXCLUDE ( EXACTSRCTITLE , "11th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "10th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2012" ) OR EXCLUDE ( EXACTSRCTITLE , "A Practical Introduction To Hardware Software Codesign" ) )"</p> <p>The keywords used in the article "DATA-DRIVEN ENGINEERING DESIGN RESEARCH: OPPORTUNITIES USING OPEN DATA" are:</p> <p>"data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*"</p>
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
Supplemental Data from the article "The SmARTR pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data"
<h1><strong>Please, refer to <a href="https://github.com/MeVisLab/SmARTR-Networks">this GitHub repository</a> for additional info, updates, issue reports, and discussion<br></strong></h1> <p><strong>A collection of configuration files (SmARTR networks) published in "<a href="https://doi.org/10.1016/j.isci.2024.111475">The SmARTR Pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data</a>", enabling the creation of cinematic (photorealistic) renderings of 3D data in the FREE software <a href="https://www.mevislab.de/download">MeVisLab</a><br></strong></p> <ul> <li>Each folder in the archive contains one or more SmARTR network files, the scan and mask files required for the practical examples detailed in the <a href="https://www.cell.com/cms/10.1016/j.isci.2024.111475/attachment/8d79036b-acb6-4cda-a5ff-f56317691ebc/mmc1.pdf">Supplemental Data</a> of the article, and an additional folder with LUT presets.</li> </ul>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Dataset of scientific article "Variability in Arsenic Methylation Efficiency across Aerobic and Anaerobic Microorganisms"
<p>Dataset for journal paper entitled "Variability in Arsenic Methylation Efficiency across Aerobic and Anaerobic Microorganisms" (DOI: 10.1021/acs.est.0c03908).</p> <p><strong>Partial publication's abstract:</strong> "Microbially-mediated methylation of arsenic (As) plays an important role in the As biogeochemical cycle, particularly in rice paddy soils where methylated As, generated microbially, is translocated into rice grains. The presence of the arsenite (As(III)) methyltransferase gene (<em>arsM</em>) in soil microbes has been used as an indication of their capacity for As methylation. Here, we evaluate the ability of seven microorganisms encoding active ArsM enzymes to methylate As. Amongst those, only the aerobic species were efficient methylators. The anaerobic microorganisms presented high resistance to As exposure, presumably through their efficient As(III) efflux, but methylated As poorly. The only exception were methanogens, for which efficient As methylation was seemingly an artifact of membrane disruption."</p> <p>The files deposited include: the flow cytometry data and fluorescence microscopy pictures used to assess membrane disruption of the methanogen <em>Methanosarcina mazei</em>, for experimental details please refer to publication, and the supporting information of the publication. Files:</p> <ol> <li><strong>Figure 3_flowcytometry files.zip:</strong> flow cytometry measurements reported in Figure 3 of publication. Measurements were performed with a 5-laser LSRII SORP flow cytometer. SYBR Green I (SG) (Invitrogen) was excited by the Blue laser (488 nm) and detected using a 530/30 band pass filter. propidium iodide (PI) (Sigma) was excited by the YG laser (561 nm) and detected using a 610/20 band pass filter. 30’000 events per sample were analyzed into four populations (no fluorescence, SG, SG/PI, or PI). Cells could be assigned to the membrane-compromised population, based on the gating of double-stained and single-stained controls of glutaraldehyde- fixed and ethanol-permeabilized cells. Cytometric data were acquired and analyzed using BD TM FACSDiva software v. 8.0.1 (BD Biosciences, CA, USA). The files consist of the reports generated by BD TM FACSDiva software in .jpg format.</li> <li><strong>Figure S14_fluorescence microscopy files.zip:</strong> Fluorescence microscopy pictures in .lsm format of single-stained SG control (SG), single-stained PI control (PI), double-stained control (SG/PI), 16-days sample (16 days) and 20-day sample (20 days) of a <em>Methanosarcina mazei</em> culture grown with 10 μM As(III) as initial concentration. The pictures are published as Figure S14 of the publication. The pictures were taken using Zeiss LSM 700 in the upright configuration equipped with a Plan-Apochromat 63x/1.40 oil immersion objective. For more details please refer to supplementary information in publicaiton. Recommended software for .lsm format included in .zip file.</li> <li><strong>SI_tables_Viacava_et_al_for_publication:</strong> file in .xlsx format including the tables: Accession numbers for As(III)-efflux and ArsM proteins and genes; primers used in preparing mutants of <em>C. pasteurianum</em>; growth curves and growth rates values for all sampled cultures; relative abundance of flow-cytometry populations; ICP-MS settings for As analysis; primers for <em>arsM</em> gene amplifications; primers for RT-qPCR of <em>C. pasteurianum</em>; HPLC-ICP-MS spectrum values ; values of <em>arsM</em> and <em>acr3</em> expression in <em>C. pasteurianum</em> WT and <em>Δacr3</em>; and concentration of total soluble arsenic and soluble arsenic species in filtered medium from all sampled cultures.</li> <li><strong>SI_Viacava_et_al_for_publication:</strong> file in .pdf format including: <ol> <li>Materials and methods: total arsenic and arsenic speciation analysis; cloning the arsM genes and gene expression in <em>E. coli </em>AW3110 (DE3); growth conditions of <em>C. pasteurianum</em> H0D0R4, strain used for genetic modification; isolation of the <em>Δacr3</em> and <em>ΔpyrE::Δacr3</em> mutants; arsenic methylation by <em>C. pasteurianum Δacr3</em>; transcription of arsM in <em>C. pasteurianum</em> WT and <em>Δacr3</em>; and membrane-integrity assessment of <em>M. mazei</em> cells using flow cytometry.</li> <li>Figures: growth rate of each individual species; abiotic control growth curves; total soluble anaerobic bacterium culture; soluble arsenic species in filtered medium from anaerobic bacterial cultures grown with 50 μM As(III); soluble arsenic species in filtered medium and volatile arsenic species from an A. rosenii culture; soluble arsenic in filtered medium from <em>S. vietnamensis, M. mazei and M. acetivorans</em> cultures; soluble arsenic species in abiotic controls; spiked HPLC-ICP-MS spectra; growth and concentration of soluble arsenic species in ArsM-expressing <em>E. coli </em>AW3110 (DE3); fluorescence microscopy pictures of flow cytometry controls from the membrane-integrity assessment from a <em>M. mazei </em>culture grown with 50 μM As(III); expression of <em>arsM</em> and <em>acr3</em> in <em>C. pasteurianum</em> WT and <em>Δacr3</em> mutant; and alignment of ArsM proteins.</li> </ol> </li> <li><strong>README.txt:</strong> .txt file with this description text.</li> </ol>
First copy costs of scientific articles
<p>This markdown file contians information from studies and reports on the first copy costs (or pure production costs) of a scientific article. It also available in GitHub (https://github.com/scinoptica/article_costs). Please feel free to improve/ update the data or add new information in the GitHub Version.</p>
Dataset - Classification of abrupt changes along viewing profiles of scientific articles
<p>This is the dataset used to run experiments described in "Classification of abrupt changes along viewing profiles of scientific articles" (more info in https://github.com/carolmb/viewing-profiles-of-scientific-articles).</p> <p>Data original from PlosOne papers views.</p>
A Scientific Journal List at Japanese News Articles
<p><strong>Abstract</strong> (our paper)</p> <p>In Japanese scientific news articles, although the research results are described clearly, the article's sources tend to be uncited. This makes it difficult for readers to know the details of the research. In this paper, we address the task of extracting journal names from Japanese scientific news articles. We hypothesize that a journal name is likely to occur in a specific context. To support the hypothesis, we construct a character-based method and extract journal names using this method. This method only uses the left and right context features of journal names. The results of the journal name extractions suggest that the distribution hypothesis plays an important role in identifying the journal names.</p> <p><strong>Data</strong></p> <p>list.txt.gz:<br> The first column is the extraction text by our method (journal name), the second column is the cleaned text, the third column is the news date, and the fourth column is the news URL.</p> <p><strong>Publication</strong></p> <p>This data set is part of our experimental results. If you make use of this data set, please cite:</p> <ul> <li>Masato Kikuchi, Kento Kawakami, Mitsuo Yoshida, Kyoji Umemura. <a href="https://doi.org/10.14923/transinfj.2018DEP0007">Conservative Direct Estimation for Likelihood Ratios Based on Observed Frequencies</a>. <em>The IEICE Transactions on Information and Systems (Japanese edition)</em>. vol.J102-D, no.4, pp.289-301, 2019.</li> <li>Masato Kikuchi, Mitsuo Yoshida, Kyoji Umemura. <a href="http://www.apsipa.org/proceedings/2018/pdfs/0000143.pdf">Journal Name Extraction from Japanese Scientific News Articles</a>. <em>Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018</em>. pp.143-148, 2018. [<a href="https://doi.org/10.23919/APSIPA.2018.8659765">DOI</a>]</li> </ul>
SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles
<p>SciHyp is a dataset that supports researchers in understanding and identifying hypotheses in scientific literature, serving as a valuable resource across various scientific disciplines. SciHyp provides invaluable insights into the formulation and structure of hypotheses in scientific literature, making it a crucial resource for researchers in various scientific disciplines.</p> <p>This repository contains the ontology and datasets described in our paper, SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles.</p> <p>🚨 <strong>Latest Version Update</strong>: Please note that all information, data files, and SPARQL queries outlined here are based on the latest version of our paper, "SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles." This includes updates to the ontology and RDF data files available on <a href="https://gitlab.ifi.uzh.ch/DDIS-Public/scihyp/-/tree/main/data?ref_type=heads">GitLab</a>. We encourage users to refer to this most recent version to ensure compatibility and relevance in their research and analysis.</p> <p><strong>crowd.ttl</strong>: The data in this file has been curated utilizing the SciHyp pipeline, which employs a Hybrid-LLM-Crowd methodology described in the paper.</p> <p><strong>expert.ttl</strong>: Contrasting the crowd-sourced data, this file is composed of data curated through expert annotation. It reflects a more specialized and precise perspective, offering insights grounded in expert knowledge and analysis.</p> <p><strong>scihyp_VoiD.ttl</strong>: Serving as a metadata file containing a VoiD/DCAT description (Vocabulary of Interlinked Datasets/Data Catalog Vocabulary).</p> <p><strong>CrowdAlytics_7.5.owl</strong>: This file is the backbone of the dataset, outlining the underlying ontology that defines the structure and relationships within the SciHyp data.</p> <p>You can find a detailed description of the data and other resources <a href="https://gitlab.ifi.uzh.ch/DDIS-Public/scihyp/-/tree/main/data?ref_type=heads" target="_blank" rel="noopener">here</a>.</p> <p><strong>SPARQL Endpoint</strong></p> <p>For querying the current SciHyp dataset, you can use our SPARQL endpoint. This endpoint allows you to execute SPARQL queries to explore and extract data from the SciHyp dataset interactively.</p> <p>SPARQL Endpoint URL: <a href="https://crowdalytics.ifi.uzh.ch/sparql/dataset.html"><code>https://crowdalytics.ifi.uzh.ch/sparql/dataset.html</code></a></p> <p><br>Below is an example query to retrieve some annotations. </p> <p> <br><code><em> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#></em></code><br><em><code> PREFIX ca: <http://ddis.ifi.uzh.ch/ontologies/2021/crowdalytics#></code></em><br><em><code> PREFIX disk: <http://disk-project.org/ontology/disk#></code></em><br><em><code> PREFIX xsd: <http://www.w3.org/2001/XMLSchema#></code></em><br><em><code> PREFIX sqo: <https://w3id.org/sqo#></code></em><br><br></p> <div> <div><code>SELECT DISTINCT ?hypothesis ?relation_Operator ?leftGroup ?rightGroup</code></div> <div> </div> <div><code>WHERE {</code></div> <div> </div> <div> <code>?TriggeredLineOFInquiry disk:hasLineOfInquiry ?LineOfInquiryURI .</code></div> <div><code> ?LineOfInquiryURI disk:hasHypothesisQuery ?hypothesis .</code></div> <div><code> ?LineOfInquiryURI ca:hasGroup ?groupPair .</code></div> <div><code> ?groupPair ca:hasLeftGroup ?left .</code></div> <div><code> ?left ca:hasGroupName ?leftGroup .</code></div> <div><code> ?groupPair ca:hasRightGroup ?right .</code></div> <div><code> ?right ca:hasGroupName ?rightGroup .</code></div> <div><code> ?LineOfInquiryURI ca:hasOperator ?Operator .</code></div> <div><code> ?Operator rdfs:label ?relation_Operator .</code></div> <br> <div><code> FILTER(CONTAINS(LCASE(?leftGroup), "game") || CONTAINS(LCASE(?rightGroup), "treatment"))</code></div> <div><code> FILTER(CONTAINS(LCASE(?relation_Operator), "similar") || CONTAINS(LCASE(?relation_Operator), "same"))</code></div> <div><code>}</code></div> <div><code>GROUP BY ?hypothesis ?relation_Operator ?leftGroup ?rightGroup</code></div> </div> <p> </p> <p><strong>Note:</strong> You can find more example queries <a href="https://gitlab.ifi.uzh.ch/DDIS-Public/scihyp/-/blob/main/data/example_queries.md" target="_blank" rel="noopener"><em>here</em></a>.<br> </p>
MORFITT : A multi-label corpus of French scientific articles in the biomedical domain
<p>This article presents MORFITT, the first multi-label corpus in French annotated in specialties in the medical field. MORFITT is composed of 3~624 abstracts of scientific articles from PubMed, annotated in 12 specialties for a total of 5,116 annotations. We detail the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT. These preliminary results demonstrate the difficulty of the task, with a weighted average F1-score of 61.78%.</p>
DATASET WITH CLASSIFICATION OF ACCOUNTS THAT DISCLOSE SCIENTIFIC ARTICLES ON TWITTER BETWEEN BOT, HUMAN AND CYBORG THROUGH BOTOMETER
<p>This dataset is the result of research that analyzed accounts that tweeted scientific articles in order to classify them as bots, humans or cyborgs through Botometer. Three articles were selected among those with the highest scores on the Altmetric platform and a sample of 18,316 Twitter user accounts were classified.</p>
Global flow of earth science scientific articles based on several databases
<p>Global flow of earth science scientific articles based on several databases contains the summary of our findings from our search of earth science articles in ten databases:</p> <ol> <li>Google Scholar</li> <li>Lens</li> <li>Dimensions</li> <li>Korean Citation Index</li> <li>Russian Scientific Citation Index</li> <li>Garuda Ristekbrin</li> <li>HAL</li> <li>Scielo</li> <li>Scopus</li> <li>Web of Science</li> </ol> <p>The data are visualized using Datawrapper in the following links. All graphs contain links to the data sources (click "Get the data" under each graph):</p> <ol> <li><a href="https://www.datawrapper.de/_/YxTc9/">https://www.datawrapper.de/_/YxTc9/ (Scielo)</a></li> <li><a href="https://www.datawrapper.de/_/blOXM/">https://www.datawrapper.de/_/blOXM/ (Dimensions)</a></li> <li><a href="https://www.datawrapper.de/_/0fyDw/">https://www.datawrapper.de/_/0fyDw/ (Lens)</a></li> <li><a href="https://www.datawrapper.de/_/ad6c8/">https://www.datawrapper.de/_/ad6c8/ (Scopus)</a></li> <li><a href="https://www.datawrapper.de/_/NyypS/">https://www.datawrapper.de/_/NyypS/ (Maximum score for documents in rank promotion regulation of Indonesia)</a></li> <li><a href="https://www.datawrapper.de/_/s1KMD/">https://www.datawrapper.de/_/s1KMD/ (Percentage of OA earth sciences documents in several databases)</a></li> <li><a href="https://www.datawrapper.de/_/2nBnP/">https://www.datawrapper.de/_/2nBnP/ (Sum of earth sciences documents in several databases)</a></li> <li><a href="https://www.datawrapper.de/_/CgpLO/">https://www.datawrapper.de/_/CgpLO/ (Distribution of earth sciences documents by year in log scale)</a></li> </ol>
Dataset for the scientific article titled "Is it worth paying attention to Actinedid mites in agricultural fields?"
<p>The dataset is about the soil-dwelling mite abundance from two experiments. The abundance data refer to 400 cm3 soil samples for six mite groups. The data are from three sites (Martonvásár, Őrbottyán, Nagyhörcsök), from three years (2018, 2019, 2021), from three plant types (wheat, maize and mixed grass) and from two soil types (Phaeozem, Luvisol) and from two seasons (summer and autumn).</p>
Raw Bibliobmetric Data for the article "The Scientific Landscape of Phytoremediation of Tailings: A Bibliometric and Scientometric Analysis"
<p>Bibliometric data for CiteSpace related to the scientific article "The Scientific Landscape of Phytoremediation of Tailings: A Bibliometric and Scientometric Analysis".</p>
Manually annotated RNA-focused scientific articles -- Experimental dataset
<p>In this repository, we store 100 <a href="../records/11393776/files/annotations.json?download=1">paragraphs sourced from RNA-focused scientific articles that have been manually annotated</a> (both for entities and relations) according to a <a href="../records/11393776/files/RNA-KG.yaml?download=1">LinkML template</a> that represent associations among genes, proteins, RNAs, chemicals, variants (SNPs), GO terms, and diseases. The template reflects a <a href="../records/11393776/files/meta-graph.pdf?download=1">subportion of RNA-KG's meta-graph</a>. <a href="https://doi.org/10.48550/arXiv.2312.00183">RNA-KG</a> data are available at: <a href="../records/10078876">https://zenodo.org/records/10078876</a>.</p>
Data from the article "Short-interval wildfire and drought overwhelm boreal forest resilience" Whitman et al., Scientific Reports, 2019
<p>Field data collected in the Northwest Territories and Wood Buffalo National Park, as well as plot locations, for the study "Short-interval wildfire and drought overwhelm boreal forest resilience" by Whitman et al. in Scientific Reports, 2019.</p> <p>For metadata or assistance please contact the authors. If you intend to publish research using these data, please contact and inform the authors, and cite the source article.</p>
Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".
<p>This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".<br>The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.</p>
A GRAPH BASED TAG RECOMMENDATION FOR JUST ABSTRACTED SCIENTIFIC ARTICLES TAGGING
<p>A graph of terms</p>
Publication data for article "Scientific and technological knowledge grows linearly over time"
<p>This dataset includes metadata for academic publications used in the article <em>Scientific and technological knowledge grows linearly over time</em>. The dataset contains citation relationships, publication dates, and academic fields for 213,715,816 publications from 1800 to 2020. These publications cover 292 secondary subjects in 19 major disciplines, including Economics, Biology, Computer Science, Physics, and more. The data are requested from Acemap (https://www.acemap.info, Shanghai Jiao Tong University) and sourced from the last snapshot of Microsoft Academic Graph (MAG) as of December 31, 2021.</p> <p>The dataset includes two gzip-compressed files, which contain all data in CSV format after decompression. Sample data is presented below:</p> <ol> <li>paper_date_refs.csv (paper_date_refs.tar.gz) <ol> <li>paper_id</li> <li>date</li> <li>reference_ids (separated by comma)</li> </ol> </li> <li>field_paper (field_paper.tar.gz) <ol> <li>field_paper/Computer science.csv <ol> <li>paper_id</li> </ol> </li> <li>field_paper/Biology.csv <ol> <li>paper_id</li> </ol> </li> <li>...</li> </ol> </li> </ol>
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OpenNeuro
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