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23 results for “impact metrics”
Citation data of arXiv eprints and the associated quantitatively-and-temporally normalised impact metrics
<p><strong>Data collection</strong></p> <p>This dataset contains information on the eprints posted on arXiv from its launch in 1991 until the end of 2019 (1,589,006 unique eprints), plus the data on their citations and the associated impact metrics. Here, eprints include preprints, conference proceedings, book chapters, data sets and commentary, i.e. every electronic material that has been posted on arXiv. </p> <p>The content and metadata of the arXiv eprints were retrieved from the arXiv API (https://arxiv.org/help/api/) as of 21st January 2020, where the metadata included data of the eprint’s title, author, abstract, subject category and the arXiv ID (the arXiv’s original eprint identifier). In addition, the associated citation data were derived from the Semantic Scholar API (https://api.semanticscholar.org/) from 24th January 2020 to 7th February 2020, containing the citation information in and out of the arXiv eprints and their published versions (if applicable). Here, whether an eprint has been published in a journal or other means is assumed to be inferrable, albeit indirectly, from the status of the digital object identifier (DOI) assignment. It is also assumed that if an arXiv eprint received <em>c</em><sub>pre</sub> and <em>c</em><sub>pub</sub> citations until the data retrieval date (7th February 2020) before and after it is assigned a DOI, respectively, then the citation count of this eprint is recorded in the Semantic Scholar dataset as <em>c</em><sub>pre</sub> + <em>c</em><sub>pub</sub>. Both the arXiv API and the Semantic Scholar datasets contained the arXiv ID as metadata, which served as a key variable to merge the two datasets.</p> <p>The classification of research disciplines is based on that described in the arXiv.org website (https://arxiv.org/help/stats/2020_by_area/). There, the arXiv subject categories are aggregated into several disciplines, of which we restrict our attention to the following six disciplines: Astrophysics (‘astro-ph’), Computer Science (‘comp-sci’), Condensed Matter Physics (‘cond-mat’), High Energy Physics (‘hep’), Mathematics (‘math’) and Other Physics (‘oth-phys’), which collectively accounted for 98% of all the eprints. Those eprints tagged to multiple arXiv disciplines were counted independently for each discipline. Due to this overlapping feature, the current dataset contains a cumulative total of 2,011,216 eprints. </p> <p>Some general statistics and visualisations per research discipline are provided in the original article (Okamura, 2022), where the validity and limitations associated with the dataset are also discussed.</p> <p> </p> <p><strong>Description of columns (variables)</strong></p> <ul> <li><strong>arxiv_id</strong> : arXiv ID</li> <li><strong>category</strong> : Research discipline</li> <li><strong>pre_year</strong> : Year of posting v1 on arXiv</li> <li><strong>pub_year</strong> : Year of DOI acquisition</li> <li><strong>c_tot</strong> : No. of citations acquired during 1991–2019</li> <li><strong>c_pre</strong> : No. of citations acquired before and including the year of DOI acquisition</li> <li><strong>c_pub</strong> : No. of citations acquired after the year of DOI acquisition</li> <li><strong>c_<em>yyyy</em></strong> (<em>yyyy</em> = 1991, …, 2019) : No. of citations acquired in the year <em>yyyy</em> (with ‘<em>yyyy</em>’ running from 1991 to 2019)</li> <li><strong>gamma</strong> : The quantitatively-and-temporally normalised citation index</li> <li><strong>gamma_star</strong> : The quantitatively-and-temporally standardised citation index</li> </ul> <p><em>Note:</em> The definition of the quantitatively-and-temporally normalised citation index (γ; ‘gamma’) and that of the standardised citation index (γ*; ‘gamma_star’) are provided in the original article (Okamura, 2022). Both indices can be used to compare the citational impact of papers/eprints published in different research disciplines at different times. </p> <p> </p> <p><strong>Data files</strong></p> <p>A comma-separated values file (‘<strong>arXiv_impact.csv</strong>’) and a Stata file (‘<strong>arXiv_impact.dta</strong>’) are provided, both containing the same information.</p> <p> </p>
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Text-fig. 16. Impact marks on the ventral edge of mandible 98-583-C-Př5-90-92, for the metrics of this bone see Table 12. in Consumption Of Canid Meat At The Gravettian Předmostí Site, The Czech Republic
Text-fig. 16. Impact marks on the ventral edge of mandible 98-583-C-Př5-90-92, for the metrics of this bone see Table 12.
Towards Understanding the Impact of Code Modifications on Software Quality Metrics
<p>The provided dataset contains the data used by "Towards Understanding the Impact of Code Modifications on Software Quality Metrics", in order to examine the impact of code changes in software quality metrics and identify types of code changes with similar impact and the results obtained.</p>
Alternative metrics and social impact of research about Social Sciences in Cuba
<p>The evaluation of social impact of research is a subject demanded by the scientific and social community. The present research is developed with the objective of describing the social impact of the results of scientific research in the field of Social Sciences in Cuba. 5 dimensions of analysis and 16 alternative indicators were used, through the use of altmetric tools and data sources. The sample collection for the study was carried out through the Scopus database and the altmetric data provider PlumX Metrics. For the analysis, statistical techniques of trend and correlation between indicators, data visualization and scientific information were used. The results show that the indicators with the greatest presence were citations in Scopus and CrossRef, Views count, Full Text Views, Abstract Views, Readers in Mendeley captures and the social network metrics Facebook and Twitter. The research results with the greatest social impact are related to climate change and environmental policy, scientific production about COVID-19, higher education, sustainable development, gender studies, legislation, and tourism.</p>
Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings - dataset
<p>This dataset accompanies the conference submission 'Journal metrics as predictors of Research Excellence Framework 2021 results: Comparison of impact factor quartiles and Finnish expert-ratings'. It contains the data in CSV format, one file per unit of analysis (Units of Assessment, Higher Education Institutions, and Subject Areas).</p> <p>The format of the UoA file is as follows (the other two files are analogous):</p> <ul> <li> <p>institution_name: name of higher education institution (e.g. university)</p> </li> <li> <p>unit_of_assessment_name: UoA name in REF (https://www.ref.ac.uk/panels/units-of-assessment/)</p> </li> <li> <p>main_panel: main panel in REF</p> </li> <li> <p>multiple_submission_letter: blank unless submitted to multiple panels.Exceptionally HEIs may have requested permission to make <a href="https://ref.ac.uk/publications-and-reports/invitation-to-make-requests-for-multiple-submissions-exception-from-submission-for-small-units-and-for-impact-case-studies-requiring-security-clearance/">two submissions from the same UoA to different panels</a>.</p> </li> <li> <p>multiple_submission_name: blank unless submitted to multiple panels</p> </li> <li> <p>non_english: number of articles in language other than English</p> </li> <li> <p>jufo_score_uoa: JUFO score (see paper for calculation details) of UoA</p> </li> <li> <p>jif_score_uoa: JIF score (see paper for calculation details) of UoA</p> </li> <li> <p>ref_score_uoa: REF score (see paper for calculation details) of UoA</p> </li> </ul>
Data from: Natural disturbances can produce misleading bioassessment results: identifying metrics to detect anthropogenic impacts in intermittent rivers
<p>Ecosystems experience natural disturbances and anthropogenic impacts that affect biological communities and ecological processes. When natural disturbance modifies anthropogenic impacts, current widely used bioassessment metrics can prevent accurate assessment of biological quality.</p> <p>Our aim was to assess the ability of biomonitoring metrics to detect anthropogenic impacts at both perennial and intermittent sites, and in the latter including both flowing and disconnected pool aquatic phases. Specifically, aquatic macroinvertebrates from 20 rivers were sampled along gradients of natural flow intermittence (natural disturbance) and anthropogenic impacts to investigate their combined effects on widely used river biomonitoring metrics (i.e. taxonomic richness and standard biological indices) and novel functional metrics, including functional redundancy (i.e. the number of taxa contributing similarly to an ecosystem function, here a trophic function) and response diversity (i.e. how functionally similar taxa respond to natural disturbance and anthropogenic impacts).</p> <p>Our results showed that natural flow intermittence can confound river bioassessment, and that a set of new functional metrics could be used as effective alternatives to standard metrics in naturally disturbed intermittent rivers.</p>
Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
1.Functional diversity (FD) metrics are widely used to assess invasion ecosystem impacts, but we have limited theory to predict how FD should respond to invasion. A key challenge to effectively using FD metrics is the complexity of conceptualizing alterations to multi-dimensional trait space, making it difficult to select a priori the most appropriate metric for specific ecological questions. 2.Here, we provide expectations on how invasion should change four commonly used FD metrics—functional richness (FRic), evenness (FEve), divergence (FDiv), and dispersion (FDis)—and then test these expectations in a lab decomposition experiment. We simulate invasion of a forest by understory plants by adding leaf litter from 18 natives and nonnatives to a representative canopy tree litter mixture to test changes in FD and decomposition. 3.All four metrics changed predictably with invasion. Species that were more functionally unique or when added at greater proportions had larger impacts on FD. Overall, FRic, FEve, and FDiv were poor choices for understanding impacts of nonnative species. FDis was the only metric that both changed predictably with addition of understory litter and correlated intuitively with changes in carbon mineralization. Furthermore, ranking species based upon how much they changed FDis of the litter mixture provided a fair assessment of which species had the largest impact on decomposition. As such, functional dispersion may be a key tool for predicting a priori which nonnatives will have the greatest impact on ecosystem processes. 4.Synthesis: We highlight the need to assess the suitability of each FD metric for the specific ecological question at hand. Our work reveals the pitfalls of considering multiple metrics or randomly choosing a single metric without suitability assessments. At the same time, it suggests a framework for metric assessment that should help lead to selection of a metric or metrics that provide robust a priori insights into how invasion by nonnative species can impact ecosystem processes.
Data from: Contrasting genetic metrics and patterns among naturalized rainbow trout (Oncorhynchus mykiss) in two Patagonian lakes differentially impacted by trout aquaculture
Different pathways of propagation and dispersal of non-native species into new environments may have contrasting demographic and genetic impacts on established populations. Repeated introductions of rainbow trout (Oncorhynchus mykiss) to Chile in South America, initially through stocking and later through aquaculture escapes, provide a unique setting to contrast these two pathways. Using a panel of single nucleotide polymorphisms, we found contrasting genetic metrics and patterns among naturalized trout in Lake Llanquihue, Chile's largest producer of salmonid smolts for nearly 50 years, and Lake Todos Los Santos (TLS), a reference lake where aquaculture has been prohibited by law. Trout from Lake Llanquihue showed higher genetic diversity, weaker genetic structure and larger estimates for the effective number of breeders (Nb) than trout from Lake TLS. Trout from Lake TLS were divergent from Lake Llanquihue, and showed marked genetic structure and a significant isolation-by-distance pattern consistent with secondary contact between documented and undocumented stocking events in opposite shores of the lake. Multiple factors, including differences in propagule pressure, origin of donor populations, lake geomorphology, and habitat quality or quantity, may concomitantly explain contrasting genetic metrics and patterns for trout between lakes. We also discussed violations of assumptions that may lead to overestimating Nb . We contend that high propagule pressure from aquaculture may not only increase genetic diversity and Nb via demographic effects and admixture, but also may impact the evolution of genetic structure and increase gene flow, consistent with findings from artificially propagated salmonid populations in their native and naturalized ranges.
Impact of Methodological Choices on the Analysis of Code Metrics and Maintenance
<p>The repo-data folder contains 53 <code>.json</code> files, each corresponding to one of the 53 Java open-source projects. Each file contains various metrics for methods in the project.</p> <p> </p> <pre><code> { "hawtio-3976.json": { "Age": 794, "sloc": [11,11,11], "slocAsItIs": [11,14,14], "slocNoCommentPretty": [11,11,11], "diffSizes": [0,7,0 ], "bodychanges": [0,1,0], "newAdditions": [0,5,0], "isGetter": [false,false,false], "isSetter": [false,false,false], "changeDates": [0,3,794], "isEssentialChange": [false,true,false], "isBuggy": [false,false,false], "changeTypes": ["Yintroduced","Ybodychange","Yfilerename"], "filename": "hawtio-3976.json", "authors": ["X","Y","Z"], "editDistance": [0, 68, 0], "repo": "hawtio" }, "method_id": {...}, "method_id": {...} } </code></pre> <p> </p> <p>The above method with id <code>hawtio-3976.json</code> has total 3 revisions which is why the array of values for a particular metric (e.g., sloc: <code>[11,11,11]</code>) are of length 3. <code>Index 0</code> of the array represents the introduction value of a particular metric for the above method.</p> <div> <h3>Description of the metrics</h3> </div> <ul> <li><code>Age</code>: Age of the method in days</li> <li><code>sloc</code>: Source line of code of a method without comment and blank lines</li> <li><code>slocAsItIs</code>: Source line of code of a method with comment and blank lines</li> <li><code>slocNoCommentPretty</code>: Source line of code pretty printed without comment and blank lines</li> <li><code>diffSizes</code>: Total number of lines added + removed in git <code>diff</code></li> <li><code>bodychanges</code>: Contains value 0 or 1; where 1 implies occurrence of body change</li> <li><code>newAdditions</code>: Total number of lines added in git <code>diff</code></li> <li><code>isGetter</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates it is a <code>get</code> method</li> <li><code>isSetter</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates it is a <code>set</code> method</li> <li><code>changeDates</code>: Contains the date difference in days from when the method was introduced. Index <code>0</code> is always 0 which indicates the introduction date</li> <li><code>isEssentialChange</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates it is an essential change. Essential change includes: <code>Ybodychange</code>, <code>Ymodifierchange</code>, <code>Yexceptionschange</code>, <code>Yrename</code>, <code>Yparameterchange</code>, <code>Yreturntypechange</code> and <code>Yparametermetachange</code> detected by <code>CodeShovel</code></li> <li><code>isBuggy</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates the method bug was fixed at a particular revision</li> <li><code>changeTypes</code>: All transformations applied to the method at each revision. The full list of transformation that is detected by <code>CodeShovel</code> are: <code>Ybodychange</code>, <code>Ymodifierchange</code>, <code>Yexceptionschange</code>, <code>Yrename</code>, <code>Yparameterchange</code>, <code>Yreturntypechange</code>, <code>Yparametermetachange</code>, <code>Yannotationchange</code>, <code>Ydocchange</code>, <code>Yformatchange</code>, <code>Yfilerename</code> and <code>Ymovefromfile</code></li> <li><code>filename</code>: It is the method <code>id<br></code> <p><strong>bugData</strong> folder contains 53 .json files with bug information, each belonging to one of the 53 Java open-source projects. The sample JSON schema of a file is given below:</p> <pre><code>{ "hawtio-3976.json":{ "exactBug0Match": [false, false, false], "exactBug1Match": [false, false, false], "exactBug2Match": [false, false, false], "exactBug3Match": [false, false, false], "regExBug0": [false, false, false], "regExBug1": [false, false, false], "regExBug2": [false, false, false], "regExBug3": [false, false, false] }, "method_id": {...}, "method_id": {...}, }</code></pre> <p>The above method can be mapped to its metrics dataset using the method_id. For e.g., the above method with id hawtio-3976.json in bugData/hawtio.jsonthat has 3 revision can be found in the metric dataset using the same id hawtio-3976.json in the file repo-data/hawtio.json.<br>Description of bug dataset</p> <p>Each key in the above example contains value true or false indicating if a method was buggy or not at each revision. The "hawtio-3976.json method has 3 revisions (including method's introduction) which is why the array length is 3. The keys in the above json output represent bug-fix classification based on buggy keywords adopted from prior work.</p> <p>We identified bug-fix commit using two approaches:</p> <p> Exact case insensitive match of buggy keywords from the commit message (keys prefix wih exact represent this)<br> Partial case insensitive substring match (using regular expression) excluding words that ends with fix or bug. (keys prefix with regEx represent this)</p> <p> Bug0: This is the approach that we have used for classifying bug-fix commit. Buggy keyword list: <strong>["error", "bug", "fixes", "fixing", "fix", "fixed", "mistake", "incorrect", "fault", "defect", "flaw"]</strong><br> Bug1: Same keyword list as exactBug0Match with the addition of keyword issues<br> Bug2: Buggy keyword list from prior work: <strong>["bug", "fix", "error", "issue", "crash", "problem", "fail", "defect", "patch"]</strong><br> Bug3: Buggy keyword list from prior work: <strong>["error", "bug", "fix", "issue", "mistake", "incorrect", "fault", "defect", "flaw", "type"]</strong></p> </li> </ul>
Data for: Functional response metrics explain and predict high but differing ecological impacts of juvenile and adult lionfish
<p>Recent accumulation of evidence across taxa indicates that the ecological impacts of invasive alien species are predictable from their Functional Response (FR; e.g. the maximum feeding rate) and Functional Response Ratio (FRR; the FR attack rate/handling time ratio). Here, we experimentally derive these metrics to predict the ecological impacts of both juvenile and adult lionfish (<em>Pterois volitans</em>), one of the world's most damaging invaders, across representative and likely future prey types. Potentially prey-population destabilising Type II FRs were exhibited by both life stages of lionfish towards four prey species: <em>Artemia salina</em>, <em>Gammarus oceanicus</em>, <em>Palaemonetes varians</em> and <em>Nephrops norvegicus</em>. FR magnitudes revealed ontogenetic shifts in lionfish impacts, while lionfish FRR values were substantially higher than mean FRR values across known damaging invasive taxa. Thus, both life stages of lionfish are predicted to contribute to differing but high ecological impacts across prey communities, including commercially important species. With lionfish invasion ranges currently expanding across multiple regions globally, efforts to reduce lionfish numbers and population size structure, and provision of prey refugia through habitat complexity, might reduce their impacts. However, early detection and complete eradication of individuals located in new regions is advised.</p>
The Impact of Dry Eye Syndrome on Metrics of Low Contrast Vision Before and After Meibomian Gland Expression
ClinicalTrials.gov study NCT05713981. IPD Sharing: YES. Countries: 1. Publications: 5.
Hypo-METRICS: Hypoglycaemia - Measurement, ThResholds and ImpaCtS
ClinicalTrials.gov study NCT04304963. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Data from: A human impact metric for coastal ecosystems with application to seagrass beds in Atlantic Canada
Open the record for dataset details and reuse information.
Data from: An experimental comparison of three Towed Underwater Video Systems using species metrics, benthic impact and performance
Open the record for dataset details and reuse information.
Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
Open the record for dataset details and reuse information.
Data from: Contrasting genetic metrics and patterns among naturalized rainbow trout (Oncorhynchus mykiss) in two Patagonian lakes differentially impacted by trout aquaculture
Open the record for dataset details and reuse information.
Data for: Functional response metrics explain and predict high but differing ecological impacts of juvenile and adult lionfish
Open the record for dataset details and reuse information.
Data from: Natural disturbances can produce misleading bioassessment results: identifying metrics to detect anthropogenic impacts in intermittent rivers
Open the record for dataset details and reuse information.
Data from: U-Index, a dataset and an impact metric for informatics tools and databases
Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources' impact over time. Our dataset differentiates the context in which citations occur to distinguish between 'awareness' and 'usage', and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.
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.