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41,236 results for “Reviews”
Data from "PathOS - D1.2 Scoping Review of Open Science Impact"
<p>This dataset contains the data from the Scoping Review of Open Science Impact. Included are all record for which we assessed the full-text.</p> <p>The columns are as follows:</p> <ul> <li>id: Internal identifier</li> <li>Several metadata columns from Scopus/Web of Science: authors, year, title, abstract, type, DOI</li> <li>OS type: type of Open Science (e.g., Open Access, Citizen Science)</li> <li>inclusion_status: included, duplicate, out of scope, non-english</li> <li>justification: reasons for decision on inclusion_status</li> <li>Several columns with data extracted by the authors: Study details and design, Types of data sources, Study aims, Relevance to which aspect of impact, Key findings, Coverage/Context, Confidence assessment</li> </ul> <p>A complete description of the methods and detailed instructions for coders for extracting data from reports is contained in section 2 of the deliverable report which is available at <a href="https://doi.org/10.5281/zenodo.7883699">https://doi.org/10.5281/zenodo.7883699</a>.</p>
Reviewed literature on the state of conservation planning in Europe
<p><strong>Literature review of European conservation planning studies</strong></p> <p>The data table uploaded here contains reviewed literature as part of the manuscript "An assessment of the state of conservation planning in Europe” by Jung et al. that is currently in Review. In this work we reviewed all available scientific literature broadly dealing with conservation planning in various facets and across realms (terrestrial, freshwater, marine). The database provided here thus provides a comprehensive starting point of all scientific conservation planning studies conducted in Europe up until mid 2023.</p> <p>The dataset is derived from a Scopus literature query conducted on the 23th of September 2022, which resulted in an initial 1459 studies which were further refined and supplemented by evidence known to the authors.</p> <p>---</p> <p># <strong>Description of table columns</strong>:</p> <ul> <li>"ID" = Numeric Identifier of the study</li> <li>"Extent" = Scale the study was conducted, from local, regional, national to European wide</li> <li>"Region" = The broad region with regards to European country</li> <li>"Locality" = Additional detail on the locality of the study if easily available</li> <li>"Realm" = Which realm does the study cover (e.g. Terrestrial, Marine, ...)</li> <li>"Ecosystem.specificity" = Was the study conducted only for specific ecosystems (e.g. Forests)?</li> <li>"Period" = Over which period was the study conducted (Present only, future conditions, both)</li> <li>"Planning.purpose" = What was the purpose of the study?</li> <li>"Policy.relevance" = Specific policy directives or legal documents referred to in the study introduction.</li> <li>"Method" = Which method was used for the planning purpose (i.e., Zonation, Marxan, ...)</li> <li>"Biodiversity.type; = What type of Biodiversity data was included in the study?</li> <li>"Number.of.features" = How many number of features?</li> <li>"Multiple.objectives.or.constraints" = Did the study somehow account for multiple objectives or constraints?</li> <li>"Connectivity" = Was connectivity somehow considered and if so, how?</li> <li>"Costs" = Were socio-economic costs somehow considered?</li> <li>"Stakeholder.involvement" = Were stakeholders involved in the planning exercise at any point?</li> <li>"Authors" = Authors of the study</li> <li>"Title" = The title of the study</li> <li>"Year" = The year it was published</li> <li>"Journal" = The scientific journal were it was published</li> <li>"DOI" = A Digital Object identifier link (can sometimes be missing)</li> <li>"Link" = A link to the journal website (can be missing)</li> <li>"Author.Keywords" = Keywords by the authors given to the study (can be missing)</li> <li>"Index.Keywords" = Keywords captured by SCOPUS for the study (can be missing)</li> <li>"Document.Type" = Type of article</li> <li>"Source" = Derived from SCOPUS or manually added through a snowballing approach?</li> <li>"cite_scientific_May2023" = How often cited in the scientific literature by May 2023?</li> <li>"cite_policy_May2023" = How often cited in policy literatue by May 2023?</li> </ul> <p>---</p> <p>The analysis code supporting the manuscript and analysing the dataset presented here can be found <a href="https://github.com/Martin-Jung/Review_EuropeanConservationPlanning">here</a>. A preprint of the manuscript can be found <a href="http://dx.doi.org/10.31219/osf.io/8x2ug">here</a>.</p>
Big Bee indexed biotic interactions and review summary
<p><strong>Extending Anthophila research through image and trait digitization (Big-Bee) indexed biotic interactions and review summary.</strong></p> <p>Declining populations of bees impact plant-pollinator interactions in both natural and agricultural systems. While bees and other insects pollinate most wild plants and are critical to sustaining a large proportion of global food production, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited because we lack sufficient data on the distribution of bee species, and on the behavioral and anatomical traits that may make them either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, wild bees have been collected by researchers and deposited in natural history collections for over 100 years, retaining a wealth of associated attributes that can be extracted from specimen images. This project will digitally capture data and images from these historic specimens, develop tools to measure bee traits from these images and generate a comprehensive bee trait and image dataset to measure changes through time. This will increase our understanding of specific traits that put bee species at risk of decline - a critical need for both sustaining our agricultural economy and the conservation of our natural resources. In addition, the large image datasets created by this project can be used for new artificial intelligence identification tools that will help improve our future pollinator observation and monitoring efforts.</p> <p>The Big-Bee project began in 2021 and is funded by the National Science Foundation to mobilize data about worldwide bee species to data aggregators (e.g., iDigBio, GBIF). The Big-Bee Thematic Collection Network (Big-Bee) will create over one million high-resolution 2D and 3D images of bee specimens, representing over 5,000 worldwide bee species, including all of the major pollinating species of the United States. The Big-Bee network includes 13 institutions and partnerships with US government agencies. Novel mechanisms for sharing image datasets will be developed and datasets of bee traits will be available through an open data portal, the Bee Library, for research and education. The Big-Bee project will engage the general public in research through community science via crowdsourcing trait measurements and data transcription from images. In addition, training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. All data resulting from this award will be shared with and publicly available through the national digitized biocollections resource, iDigBio.org.</p> <p>This is the first archive of Big-Bee data indexed by Global Biotic Interactions (GloBI). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open-source software. This version of the Big Bee dataset includes interactions that are not just bees. Also in this version, the datasets included in this publication are specifically those institutions in the Big Bee project network and do not represent all bee interaction data found at Global Biotic Interactions.</p> <p><strong>Bee Library Information - Statistics about Big Bee data providers</strong></p> <p>The specimens indexed by GloBI are also found in the <a href="https://library.big-bee.net/portal/">Bee Library</a>. To date, the number of specimens and images in the library are listed below. The Bee Library taxonomic backbone is not yet complete, so information regarding the number of species is not yet available. Further summary statistics are available in the Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf file.</p> <p><strong>From Bee Library (partner indexed records)</strong><br> 1,234,107 occurrence records<br> 993,692 (81%) georeferenced<br> 351,592 (28%) occurrences imaged<br> 986,323 (80%) identified to species<br> 9 families<br> 526 genera<br> 10,700 species<br> 11,386 total taxa (including subsp. and var.)</p> <p><strong>Statistics Per Collection</strong></p> <table> <tbody> <tr> <td>Collection</td> <td>Occurrences</td> <td>Georeferenced</td> <td>Imaged</td> <td>Interactions Indexed in GloBI (all)</td> <td>Interactions Indexed in GloBI (bees)</td> </tr> <tr> <td>ASU Hasbrouck Insect Collection - Bee<br> Records</td> <td>13223</td> <td>13221</td> <td>2352</td> <td>21300</td> <td>3834</td> </tr> <tr> <td>Bee Biology and Systematics Laboratory,<br> USDA-ARS Pollinating Insect-Biology,<br> Management, Systematics Research</td> <td>561820</td> <td>547461</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <td>California Academy of Sciences</td> <td>884</td> <td>300</td> <td>3</td> <td>16984</td> <td>117</td> </tr> <tr> <td>California Academy of Sciences - Type<br> Collection</td> <td>1838</td> <td>59</td> <td>83</td> <td>0</td> <td>0</td> </tr> <tr> <td>Essig Museum of Entomology, University<br> of California Berkeley</td> <td>58551</td> <td>55028</td> <td>0</td> <td> </td> <td>0</td> </tr> <tr> <td>Florida State Collection of Arthropods</td> <td>17134</td> <td>12349</td> <td>7816</td> <td>559</td> <td> </td> </tr> <tr> <td>Museum of Comparative Zoology, Harvard<br> University</td> <td>22020</td> <td>21099</td> <td>11595</td> <td>6777</td> <td>1535</td> </tr> <tr> <td>Natural History Museum of Los Angeles<br> County</td> <td>24685</td> <td>7421</td> <td>3480</td> <td>0</td> <td>0</td> </tr> <tr> <td>San Diego Natural History Museum<br> Entomology Department</td> <td>4065</td> <td>1690</td> <td>1982</td> <td>8688</td> <td>90</td> </tr> <tr> <td>University of California Santa Barbara<br> Invertebrate Zoology Collection</td> <td>8674</td> <td>8410</td> <td>2751</td> <td>1940</td> <td>660</td> </tr> <tr> <td>University of Colorado Museum of Natural<br> History, Entomology Collection</td> <td>18043</td> <td>18043</td> <td>0</td> <td>9589</td> <td>4723</td> </tr> <tr> <td>University of Kansas Natural History<br> Museum Entomology Division</td> <td>464927</td> <td>275200</td> <td>304415</td> <td>119963</td> <td>112677</td> </tr> <tr> <td>University of Michigan Museum of Zoology<br> Division of Insects</td> <td>17764</td> <td>15305</td> <td>15269</td> <td>53755</td> <td>4134</td> </tr> <tr> <td>University of New Hampshire, Donald S.<br> Chandler Entomological Collection</td> <td>17685</td> <td>17393</td> <td>0</td> <td>3137</td> <td>3137</td> </tr> <tr> <td>USGS Native Bee Inventory and Monitoring<br> Lab</td> <td>101</td> <td>101</td> <td>0</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <p><strong>GloBI Data Review Report - Datasets in Review from Global Biotic Interactions</strong></p> <p>Datasets under review:<br> - UUniversity of Michigan Museum of Zoology, Division of Insects accessed via https://github.com/globalbioticinteractions/ummz-ummzi/archive/d9282e51f29f3157af2e5869a09ea8a111ddea34.zip on 2023-07-24T22:06:08.671Z<br> - Arizona State University Hasbrouck Insect Collection accessed via https://github.com/globalbioticinteractions/asu-asuhic/archive/4ed77cb9ca8e526269d4678692e2844c950022f8.zip on 2023-07-24T22:07:09.630Z<br> - California Academy of Sciences Entomology and Entomology Type Collection accessed via https://github.com/globalbioticinteractions/cas-ent/archive/47d385b73a63aa379cd5e6d3615005ba78b0ffc1.zip on 2023-07-24T22:08:13.753Z<br> - University of California Berkeley, Essig Museum of Entomology accessed via https://github.com/globalbioticinteractions/emec/archive/93b17a3db566baa001ce9190e6fbdb60fa99dda4.zip on 2023-07-24T22:08:24.495Z<br> - Florida State Collection of Arthropods accessed via https://github.com/globalbioticinteractions/fsca/archive/2cdcf9475b7e0ef2a728a96535608bc0ce2ac5ca.zip on 2023-07-24T22:08:49.972Z<br> - University of Kansas Natural History Museum accessed via https://github.com/globalbioticinteractions/ku-semc/archive/a9c7cb81050eef68b4428667206a219da458f517.zip on 2023-07-24T22:09:17.016Z<br> - Natural History Museum of Los Angeles County accessed via https://github.com/globalbioticinteractions/lacm-lacmec/archive/dafbf532c53fbadba126c81186c26d52677aa781.zip on 2023-07-24T22:11:11.442Z<br> - Harvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University. accessed via https://github.com/globalbioticinteractions/mcz/archive/b33635a9fc75fd7931ad968cbc11180e6467bfd7.zip on 2023-07-24T22:21:32.961Z<br> - San Diego Natural History Museum accessed via https://github.com/globalbioticinteractions/sdnhm-sdmc/archive/7238d8b804f543250eb487b43144e1125fb3688a.zip on 2023-07-24T22:26:25.503Z<br> - University of Colorado Museum of Natural History Entomology Collection accessed via https://github.com/globalbioticinteractions/ucm-ucmc/archive/60530dcc82d33c9675a4026ad60dc40bea8f2a91.zip on 2023-07-24T22:26:50.178Z<br> - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/66a4e39589d1dfa299d07985546c4be522ff60d8.zip on 2023-07-24T22:27:13.801Z<br> - University of New Hampshire Donald S. Chandler Entomological Collection accessed via https://github.com/globalbioticinteractions/unhc-unhc/archive/d7668a6bb4545dc4da0645ecc383169ba547b0f5.zip on 2023-07-24T22:27:28.670Z</p> <p>Generated on:<br> 2023-07-24</p> <p>by:<br> GloBI's Elton 0.12.6 <br> (see https://github.com/globalbioticinteractions/elton).</p> <p>Note that all files ending with .tsv are files formatted <br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv: <br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar: <br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p>indexed_interactions_bees.tsv:<br> All indexed bee interactions <br> </p> <p>datasets.zip:<br> All datasets reviewed for this publication</p> <p> Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf:<br> Summary statistics from the Bee Library and GloBI about data partners</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/Big-Bee-Network/issues-observations-and-questions/discussions or contact the authors by email.</p> <p><strong>Funding:</strong><br> The creation of this archive was made possible by the National Science Foundation award Collaborative Research: Digitization TCN: Extending Anthophila research through image and trait digitization (Big-Bee). Award numbers: <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102006">DBI:2102006</a>, DBI:2101929, DBI:2101908, DBI:2101876, DBI:2101875, DBI:2101851, DBI:2101345, DBI:2101913, DBI:2101891 and DBI:2101850.</p> <p>References:<br> Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Jorrit Poelen, Tobias Kuhn, & Katrin Leinweber. (2022). globalbioticinteractions/elton: 0.12.5 (0.12.5). Zenodo. https://doi.org/10.5281/zenodo.7267926</p>
Bibliographic Data from the SoTL in Civil and Structural Engineering Systematic Review
<p>This database contains all the bibliographic information found after applying the Search Strategy used for the SoTL in Civil and Structural Engineering Systematic Review. The following electronic databases were searched:</p> <ul> <li>Scopus.</li> <li>Web of Science.</li> <li>OsloMet Library.</li> <li>Google Scholar (no bibliographic information is presented since this database does not allow to download such data).</li> </ul> <p>A total of 84 records were found in Scopus, 43 in Web of Science, and 55 in OsloMet Library. The search was conducted on September 1, 2023.</p> <p>The information is presented in .ris, .bib, and .csv format.</p>
State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022
This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata.
Datasets for: A global review of pyrosomes: Shedding light on the ocean’s elusive gelatinous ‘fire-bodies’
These are the datasets used to create all figures included in: "Lilly, L.E., Suthers, I.M., Everett, J.D., Richardson, A.J. (2023). A Global Review of Pyrosomes: Shedding light on the ocean’s elusive gelatinous ‘fire-bodies’. Limnology & Oceanography Letters." The review presents a comprehensive global description of the body of current knowledge on pyrosomes, a zooplanktonic tunicate taxon closely related to salps, doliolids, and appendicularians. For review analyses, we used pyrosome observations and associated information from literature-published studies and four databases: NOAA COPEPOD Urochordates database (NOAA, 2022; https://www.st.nmfs.noaa.gov/copepod/atlas/html/taxatlas_4350000.html), BCO-DMO Jellyfish Database Initiative (JeDI; Condon et al., 2014; https://www.bco-dmo.org/dataset/526852), Global Biodiversity Information Facility (GBIF; https://doi.org/10.15468/dl.a8phvp), and Ocean Biodiversity Information System (OBIS; https://obis.org/taxon/137216). We matched pyrosome observations to corresponding satellite-measured sea surface temperature (NOAA Optimum Interpolation Sea Surface Temperature, V2, high-resolution, https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html) and chlorophyll-a (MODIS-AQUA, 4 km^2 resolution, Melin, 2013; http://data.europa.eu/89h/10161412-a76c-42b0-b4e1-5fcccdc412b2). The files included in this metadata record have been subsetted from all original file sources. Our subsetted files are designed to run with the associated MATLAB scripts to recreate all manuscript files. We include seven MATLAB scripts: 1) A four-part script to clean up all pyrosome observations, divide to species level, and remove duplicate records from multiple databases and within each database, and 2) Three standalone scripts to plot Figs. 1, 2, and 3.
MCR LTER: Data from Duvall, Rosman and Hench, in review. Representation of coral reef roughness using obstacle and surface-based approaches, submitted to JGR: Oceans
This archive contains natural coral reef topography data from the northern coast of Mo’orea, French Polynesia. These data were used to compute reef roughness density using obstacle- and surface-based estimates and models, and to compare the two approaches for representing reef topography. Primary support for this product came from the National Science Foundation Physical Oceanography program (OCE-1435530 and OCE-1435133), and as well as Duke University and the University of North Carolina at Chapel Hill. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
P4KxSpotify: A Dataset of Pitchfork Music Reviews and Spotify Musical Features
<p>18,403 music reviews scraped from Pitchfork, including relevant metadata such as author, review date, record release year, score, and genre, along with those album's audio features pulled from Spotify's API.</p>
MiRoR7-P1- Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design
<p>dataset referring to </p> <p><strong>Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design</strong></p> <p> </p> <p> </p> <p>Lorenzo Bertizzolo<sup>1</sup>, Patrick M Bossuyt<sup>2</sup>, Ignacio Atal<sup>1, 5</sup>, Philippe Ravaud<sup>1, 3-6</sup>, Agnès Dechartres<sup>7</sup></p> <p> </p> <p><sup>1</sup> INSERM, U1153 Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases Team (METHODS), Paris, F-75004 France; Paris Descartes University, Sorbonne Paris Cité, France.</p> <p><sup>2</sup> Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Netherlands.</p> <p><sup>3</sup> Centre d’Épidémiologie Clinique, Hôpital Hôtel Dieu, AP-HP (Assistance Publique des Hôpitaux de Paris), Paris, France.</p> <p><sup>4</sup> Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.</p> <p><sup>5</sup> Cochrane France, Paris, France</p> <p><sup>6</sup> Columbia University, Mailman School of Public Health, Department of Epidemiology, New York, USA</p> <p><sup>7</sup> Sorbonne Université, INSERM, Institut Pierre Louis de Santé Publique, Département Biostatistique, Santé Publique et Information Médicale, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière – Charles Foix, Paris, France</p>
Data for "What have biological records ever done for us? A systematic scoping review"
<p>These files contain data used in "What have biological records ever done for us? A systematic scoping review" (Gaul et al. 2020). </p>
Review of the evidence for Oceans and Human Health relationships in Europe: A systematic map.
<p>This database details the results of a systematic mapping exercise linking marine exposures to measured human health outcomes for the Seas, Oceans and Public Health in Europe Project.</p>
SHAPE-ID Literature Review dataset: bibliography on IDR/TDR
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of 5040 records of publication metadata (author, abstract, title, keywords, tags etc.), produced for the purposes of the <a href="https://doi.org/10.5281/zenodo.3760417">systematic literature review</a> in the framework of the <a href="https://www.shapeid.eu/">SHAPE-ID</a> project. </p> <p>In the course of the review Project team queried Web of Science (WoS), Scopus and JSTOR databases for records on interdisciplinarity and transdisciplinarity (IDR/TDR). In the case of WoS and Scopus, <a href="https://doi.org/10.5281/zenodo.4034333">complex search strings</a> were created to reflect the main research questions of the Literature Review: different understandings of IDR/TDR and factors and indicators of success or failure of integration of IDR/TDR in research and research policy. JSTOR database offers less advanced data-analytical tools, but the project team decided to include items that have interdisciplinarity or transdisciplinarity in the title, to counterbalance the reported biases against Arts, Humanities and Social Sciences in Scopus and WoS. These three data sources were complemented with bibliographies prepared during the preliminary scoping analysis of IDR/TDR literature. The query results were compiled in reference managers Zotero and Endnote. During data processing the records were normalized and duplicates were removed. </p> <p>Based on systematic review, a sample of the literature had been selected for qualitative analysis. At the same time, the bibliographic metadata was analysed with computationally assisted quantitative methods.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file exported from the Zotero database, and formatted according to the <a href="https://www.zotero.org/support/kb/item_types_and_fields">Zotero metadata model</a>. It contains a collection of 5040 bibliographic records compiled for the purpose of the SHAPE-ID Literature Review.</p>
SHAPE-ID Literature Review dataset: journal occurrences with ASJC codes
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of a list of 2202 journal titles represented in the <a href="https://doi.org/10.5281/zenodo.4034507">SHAPE-ID Literature Review bibliography</a>, prepared for the purposes of quantitative analysis.</p> <p>The list of journals is based on 3955 journal articles in the bibliography dataset that had an International Standard Serial Number (ISSN). To each journal title the project team attributed:</p> <p>- a weight factor based on how many articles from the given journal featured in bibliography dataset</p> <p>- at least one <a href="https://service.elsevier.com/app/answers/detail/a_id/15181/supporthub/scopus/">All Science Journal Classification</a> (ASJC) code, representing different scientific disciplines</p> <p>- a country of publication. </p> <p>In case of 1853 of those journal titles, the attribution was automatised (we matched the ISSNs of journal titles in our sample against the Scopus Sources list from February 2019). In case of the remaining 349 titles the attribution was accomplished manually, based on the information available in SCOPUS, Web of Science, JSTOR, Information Matrix for the Analysis of Journals (MIAR) and ISSN databases.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file containing a list of 2202 journal titles represented in the SHAPE-ID Literature Review bibliography, with country of publication and ASJC codes assigned. </p> <p>The file is formatted as follows:</p> <p>Column A: ISSN of the journal</p> <p>Column B: information on how country and ASJC codes were attributed. Value “N” indicates automatic attribution based on match with Scopus list of sources. Other values indicate manual attribution. Values WOS, SCOPUS, JSTOR indicate source of information. Valu “Y” indicates that information was compiled based on multiple sources. </p> <p>Column C: numeric values correspond to the weight factor, i.e. number of time articles from each journal featured in the SHAP-ID Literature Review bibliography. </p> <p>Column D: SHAPE-ID Zotero bibliography identifier.</p> <p>Column E: Journal title</p> <p>Column F: The country of publication</p> <p>Columns G-AD: ASJC codes (numeric and word values) associated with journal entries. </p>
Minimal dataset for "Systematic review and meta-analysis of late auditory evoked potentials as a candidate biomarker in the assessment of tinnitus"
<p>This text file contains the minimal dataset necessary to reproduce the results and analyses in the paper: Cardon E et al., "Systematic review and meta-analysis of late auditory evoked potentials as a candidate biomarker in the assessment of tinnitus". Plos One;2020.</p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
Sentiment analysis in Galaxy with IMDB movie review dataset
<p>IMDB movie review sentiment classification dataset (Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/<br> <br> The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):<br> <br> The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org). </p>
Materials for "Poor nutritional condition promotes high-risk behaviours: A systematic review and meta-analysis"
<p>This contains a permanent record of dataset and analysis code for the study:</p> <p>Moran, N. P., Sánchez‐Tójar, A., Schielzeth, H., & Reinhold, K. (2021). Poor nutritional condition promotes high‐risk behaviours: a systematic review and meta‐analysis. <em>Biological Reviews</em>, <em>96</em>(1), 269-288.</p> <p>Full data analysis records are available on https://osf.io/3tphj/</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 836937. Also, this research was funded by the German Research Foundation (DFG) as part of the SFB TRR 212 (NC³) – Project numbers 316099922 and 396782608.</p>
Review of definitions of Open Peer Review in the scholarly literature 2016
<p>This data set contains:</p> <ul> <li>Full data files of a 2016 review of definitions of Open Peer Review in the scholarly literature in xls and csv formats</li> <li>Description of data collection methodology (txt)</li> <li>Readme file (txt)</li> </ul> <p>The term “open peer review” has neither a standardized definition nor an agreed schema of its features and implementations. Recognising the absence of a consensus view on what OPR is, OpenAIRE has undertaken a systematic review of definitions of “open peer review” or “open review”, to create a corpus of 122 definitions. These definitions have been systematically analysed to build a coherent typology of the many different innovations in peer review signified by the term and hence provide the precise technical definition currently lacking. This quantifiable data offers rich information on the range and extent of differing definitions over time and by broad subject area.</p> <p>Contact:</p> <p>Tony Ross-Hellauer: http://orcid.org/0000-0003-4470-7027 / ross-hellauer@sub.uni-goettingen.de</p>
OpenAIRE Open Peer Review Survey 2016
<p>This data set contains:</p> <p>- Full data files of a 2016 survey of attitudes to Open Peer Review in xls and csv formats<br> - Survey questions (pdf)<br> - Readme file (txt)</p> <p>Between 8 September and 7 October 2016, OpenAIRE held a survey designed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauging current levels of experience and reservation with different types of OPR. A supplementary aim was to collect feedback on a provisional definition of OPR as created during another strand of work. The survey aimed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauge current levels of experience and reservations with different types of OPR. The survey was conducted via an openly accessible online questionnaire (using the scientific survey platform SoSci, www.soscisurvey.de). It received a total of 3062 complete responses (a further 635 responses were discarded as incomplete). The survey was open to all wishing to take part and distributed via social media, scholarly communications mailing lists, publisher newsletters and, in one case, a publisher internal mailing list (Copernicus Publications). </p> <p>Acknowledgement: This work is funded by the European Commission H2020 project OpenAIRE2020 (Grant agreement: 643410, Call: H2020-EINFRA-2014-1)</p> <p>Contact: Dr Tony Ross-Hellauer, University of Göttingen, State and University Library, ross-hellauer@sub.uni-goettingen.de<br> </p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </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)
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DANDI Archive for NWB datasets
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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.