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2,129 results for “Score”
Supplementary material 1 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Options for the Control of Influenza VIII Conference Programme Book
Supplementary material 3 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
A sample Stata program code fragment is attached.
What can we learn from 100,000 freshwater forecasts? A synthesis from the NEON Ecological Forecasting Challenge: scores and targets
<p>This data publication contains the zipped scores parquet database and the targets used in the analysis for the NEON Challenge aquatics synthesis: scores.zip contains evaluated forecast scores, targets.zip contains in-situ water temperature and dissolved oxygen data generated for the Challenge, and Model_descriptions.csv contains the meta data for the submitted models used in the analysis. </p> <p><strong>scores</strong>: this is the primary dataset used in the manuscript. The database includes the scores (forecasts evaluated against observations).</p> <p><strong>targets</strong>: these are the data used to evaluate the forecast (observations) and were also available to forecast teams in near-real time. The R script included in this folder (aquatics_targets.R) is intended for reference only and cannot be run. </p> <p><strong>Model_descriptions.csv</strong>: table of summarised meta data on forecast model approaches submitted by forecast teams. This includes whether the forecasts include (1) or exclude (0) each source of uncertainty/initial conditions/model updating, which covariates are included, and the reported model class. </p>
MultiTune: Multiple-Environment Configuration Tuning via Multi-Task Learning and Propensity Score Matching
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The Ó Riada Scores (from Seán Ó Riada Collection/Bailiúchán Sheáin Uí Riada)
<p>Musical scores discovered during PhD research on the Seán Ó Riada Collection at the Boole Library, University College Cork.</p> <p> </p> <p>Dataset originally created 16/03/2016 UPDATE: Packaged on 03/05/2024</p> <p>I. About this Data Set</p> <p>This data set is a result of close reading conducted by Patrick Egan (Pádraig Mac Aodhgáin) at the Boole Library, University College Cork between 2014-2018. It contains a combination of metadata from searches of the Seán Ó Riada Collection finding aid (or "descriptive list") concerning musical scores or "score cards" that were created by 1960s Irish artist Seán Ó Riada. The PhD project was published in 2020, entitled, “<a href="https://cora.ucc.ie/items/2361088a-8a26-497c-a2b9-23afa26af71a">Exploring ethnography and digital visualisation: a study of musical practice through the contextualisation of music related projects from the Seán Ó Riada Collection</a>” You are invited to use and re-use this data with appropriate attribution.</p> <p>The Ó Riada Scores dataset consists of just over 1571 rows.</p> <p>II. What’s included? This data set includes:</p> <ul> <li>The Ó Riada Scores Dataset – a .CSV containing <br>Handwritten? Estimated Pages Full Score? (1 if true) ITEM # in Finding Aid Title of Piece Instruments Used (with Parts) Notes Written on the Score Personal Notes About Document Copyright Stamp?<br>This .CSV is the direct export of the Ó Riada Scores Google Spreadsheet</li> </ul> <p>III. How Was It Created? These data were created by daily visits to the Seán Ó Riada Collection over the course of four years. Through close reading, description, identifying catalogue numbers and tagging detail, access and understanding of the contents of the collection.</p> <p>"The following were documented in conjunction with the Finding Aid on the Seán Ó Riada special collection, to gather an overview on all scores that exist at UCC. An online version of the Finding Aid is located at https://booleweb.ucc.ie/documents/O%20Riada.pdf - Apart from working through headings of sections of Ó Riada's professional works, I used the following relevant search terms within the PDF to source any documents that might relate to manuscripts/score material: "score", "sheet", "suite" and "arrangement", "manuscript", "autograph". Patrick Egan, UCC"</p> <p>IV. Data Set Field Descriptions</p> <p>a) The Ó Riada Scores dataset field descriptions</p> <ul> <li>Handwritten? - Describes if the score was handwritten or printed </li> <li>Estimated Pages - Number of pages estimated within the score </li> <li>Full Score? (1 if true) - If the score is available in full </li> <li>ITEM # in Finding Aid - The corresponding number with the "descriptive list" on UCC Lib Guide <br>https://libguides.ucc.ie/SeanORiadaCollection/descriptivelist </li> <li>Title of Piece - The title given on the score </li> <li>Instruments Used (with Parts) - Listing of instrument parts that were used, or described by the composer </li> <li>Notes Written on the Score - Any notes that were written on the score for reference </li> <li>Personal Notes About Document - Personal notes used during close reading of each score </li> <li>Copyright Stamp? - If copyright stamps were included (generally this was done by RTÉ)</li> </ul> <p>V. Rights statement The text in this data set was created by the researcher and can be used in many different ways under creative commons with attribution. All contributions to this PhD project are released into the public domain as they are created. Anyone is free to use and re-use this data set in any way they want, provided reference is given to the creator of this dataset.</p> <p>VI. Creator and Contributor Information</p> <p>Creator: Patrick Egan (Pádraig Mac Aodhgáin)</p> <p>VII. Contact Information Please direct all questions and comments to Patrick Egan via his website at <a href="http://www.patrickegan.org/">www.patrickegan.org</a>. You can also get in touch with the Library via UCC website.</p>
Computed Semantic clues scores for Grafematik (Magistry & Goudin, 2022)
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. Nutritional information, mean ± SEM body weight, and mean ± SEM body condition score (BCS) over the experiment period.
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RESONANCES Database : Experiment no. 2 — Self-Report Scores
<p>This dataset is an output of the RESONANCES neurophysiological-signal-based testing protocol aimed at recognizing and measuring atmospheric emotions. It contains sociodemographics describing the analyzed sample, self-report scores, and related questionnaires.<br> <br>The dataset is made of six files:<br>no. 1 dataset summary (.pdf)<br>no. 1 database (.xlsx)<br>no. 4 documents (.pdf) embedding the used questionnaires (Appendix01_SREIT; Appendix02_BFI-10; Appendix03_EES; Appendix04_RAQ)</p>
LBPA/Cholesterol, Prestwick screen image analysis data (Z-score)
<p>LBPA-Cholesterol, Prestwick library screening image analysis data</p>
Loyalty_Scores_Prediction
<p>Exploratory data analysis for Elo Merchant Company to predict Customer Loyalty Scores.</p>
An analysis of DeepTRIAGE importance scores
<p>An analysis of DeepTRIAGE importance scores</p>
Spreadsheet showing scorings of Bernissartia in dataset of Sookias 2019
<p>Spreadsheet showing scorings of Bernissartia in dataset of Sookias 2019 (<a href="https://doi.org/10.1093/zoolinnean/zlz015">https://doi.org/10.1093/zoolinnean/zlz015</a>)</p>
Figure 3. Coral morphology class score for 13 in Current status of coral reefs in Tioman Island, Peninsular Malaysia
Figure 3. Coral morphology class score for 13 reef sites in Tioman Island. Most of the reef sites were plotted under mixed coral morphologies and received class 4 (C 4). Three reef sites (Tomok Island, Gado Bay, and Tulai Bay) dominated by stress-tolerator corals and received class 1 (C 1). One reef site (Bayan Bay) dominated by competitor corals and received class 2 (C 2).
The Role of Scientific Language in Citation Count and Altmetric Score of Academic Publications _ Appendix
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Cattle PhastCons and PhyloP conservation scores
<p>This dataset provides conservation scores for cattle (ARS-UCD1.2/bosTau9), including PhastCons and PhyloP scores in BigWig format. Please cite the following paper if you use the cattle conservation scores:</p> <p>Zhao, R., Owen, R., Marr, M., Siddharth, J., Hong, N. C., Talenti, A., Hassan, M. A., & Prendergast, J. G. D. (2024). The potential of regulatory variant prediction AI models to improve cattle traits (p. 2024.08.01.606140). bioRxiv. <a href="https://doi.org/10.1101/2024.08.01.606140" target="_new" rel="noopener">https://doi.org/10.1101/2024.08.01.606140</a></p>
Raw Dataset (organized to create ppts' single choice scores)
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Team running performance while scoring and conceding goals in the UEFA Champions League: Analysis of five-minute intervals.
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Assessing the Effectiveness of Administering Modified Ultrasound-Guided Lower Abdominal Nerve Blocks for Alleviating Pain Scores in Patients Undergoing Transinguinal Hernia Surgery
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Baseline and Future (2050s and 2090s) Climate Suitability Scores for 137 Useful Tree Species and 273 locations from the United Republic of Tanzania
<p>Climate suitability scores were calculated for 137 Useful Tree Species identified by filtering native tree species from the United Republic of Tanzania via the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database, matching species with those described in the <a href="https://apps.worldagroforestry.org/usefultrees/">RELMA-ICRAF Useful Tree and Shrub Species for Tanzania manual</a> and checking for the availability of globally observed environmental ranges from the <a href="https://doi.org/10.5281/zenodo.13132613">TreeGOER</a> database.</p> <ul> <li>Score = 3 means that in 'environmental space' the planting site occurs within the 25% - 75% species's range (as documented in the <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16914" target="_blank" rel="noopener">TreeGOER</a> ) for all variables</li> <li>Score = 2 corresponds to the 5% - 95% species's range for all variables</li> <li>Score = 1 corresponds to the 0% - 100% species's range for all variables</li> <li>Score = 0 means that the planting site occurs outside the 0% - 100% species's range for some of the variable</li> <li>Score = -1 means that the species is not documented by TreeGOER</li> </ul> <p> </p> <p>Locations corresponded to cities within the target countries sourced from the <a href="https://doi.org/10.5281/zenodo.10004594">CitiesGOER</a> database. This database provides bioclimatic conditions for the historical (baseline) and three future climate change scenarios. Bioclimatic variables for future climates correspond to the median values from 24 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 for the 2050s (2041-2060), from 21 GCMs for SSP 3-7.0 for the 2050s and from 13 GCMs for SSP 5-8.5 for the 2090s.</p> <p>Investigations were made for two different sets of bioclimatic variables, allowing for sensitivity analysis:</p> <ul> <li>One set of bioclimatic variables included BIO01, BIO12, climaticMoistureIndex, monthCountByTemp10, growingDegDays5, BIO05, BIO06, BIO16, BIO17 and MCWD. These are the same bioclimatic variables available internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> for climate filtering.</li> <li>One set only included BIO01 (= Mean Annual Temperature), which is the same bioclimatic variables available from the BGCI <a href="https://cat.bgci.org/">Climate Assessment Tool</a>.</li> </ul> <p>Calculations were made with similar scripting pipelines in the <em>R</em> statistical environment as documented here: <a href="https://rpubs.com/Roeland-KINDT/1168650">https://rpubs.com/Roeland-KINDT/1168650</a>. These scripts use similar calculations methods as those used for the global case studies of the TreeGOER manuscript (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>), and used internally in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> online database. Interested readers should especially refer to the manuscript for further details on methods used and their justification.</p> <p>The maps show the frequency distribution of tree species with climate scores 3, 2, 1 and 0, excluding 6 species not documented by the TreeGOER.</p> <p> </p> <p>The Excel database allows filtering useful tree species by some of the attributes available in the <a href="https://patspo.shinyapps.io/GlobalUsefulTrees/">GlobalUsefulNativeTrees</a> database.</p> <p>Species can be filtered for ten categories of documented human uses (see <a href="https://kew.iro.bl.uk/concern/datasets/7243d727-e28d-419d-a8f7-9ebef5b9e03e">Diazgranados et al. 2020</a> for details):</p> <ul> <li>AF: Animal Food.</li> <li>EU: Environmental Uses.</li> <li>FU: Fuel.</li> <li>GS: Gene Sources.</li> <li>HF: Human Food.</li> <li>IF: Invertebrate Food.</li> <li>MA: Materials.</li> <li>ME: Medicines.</li> <li>PO: Poisons.</li> <li>SU: Social Uses</li> </ul> <p>Species can also be filtered for the Climatic Moisture Index (CMI). See this Zenodo archive (<a href="https://zenodo.org/records/8252756">https://zenodo.org/records/8252756</a>) to see the distribution of CMI zones across the United Republic of Tanzania. Codings refer to the species reaching the upper part of the range in the zone (code: 2), the zone being included in teh middle part of the ranage (code: 9) or the species reaching the lower part of the range in this zone (code:3).</p> <ul> <li>CMI.A (CMI ≥ 0.5 ; P >= 2 * PET; ‘extremely humid’ lands)</li> <li>CMI.B (0 ≤ CMI < 0.5 ; PET <= P < 2 * PET ; ‘very humid’ lands)</li> <li>CMI.C (−0.35 ≤ CMI < 0 ; 0.65 <= P/PET < 1 ; ‘humid’ lands)</li> <li>CMI.D ( −0.5 ≤ CMI < −0.35 ; 0.50 <= P/PET < 0.65 ; dry sub-humid drylands)</li> <li>CMI.E (−0.8 ≤ CMI < −0.5 ; 0.20 <= P/PET < 0.50 ; semi-arid drylands)</li> <li>CMI.F (−0.95 ≤ CMI < −0.8 ; 0.05 <= P/PET < 0.20 ; arid drylands)</li> <li>CMI.G (CMI < −0.95 ; P/PET < 0.05 ; hyper-arid drylands)</li> </ul> <p> </p> <p><strong>References</strong></p> <ul> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13132613" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13132613</a></li> <li>Kindt, R., Graudal, L., Lillesø, JP.B. <em>et al.</em> (2023). GlobalUsefulNativeTrees, a database documenting 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in landscape restoration. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a></li> <li>Kindt, R. (2023). CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000 (2023.10) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10004594" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10004594</a></li> <li>Kindt, R. (2024). ClimateForecasts: Globally Observed Environmental Data for 15,504 Weather Station Locations (2024.07) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12679832" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12679832</a></li> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> <li>Meteostat (2024) Weather stations: Lite dump with active weather stations. <a href="https://github.com/meteostat/weather-stations">https://github.com/meteostat/weather-stations</a> (accessed 17-FEB-2024)</li> <li>Diazgranados, M., Allkin, B., Black, N., Cámara-Leret, R., Canteiro, C., Carretero, J., Eastwood, R., Hargreaves, S., Hudson, A., Milliken, W. and Nesbitt, M., 2020. World checklist of useful plant species. Royal Botanic Gardens, Kew. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34</a></li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>The data sets and maps available in this archive were created through funding by the <strong>U. S. Agency for International Development (USAID)</strong> to CIFOR-ICRAF, here specifically in the context of the <em>On-farm Land Restoration for Livelihoods and Environmental Benefits</em> project.</p> <p> </p>
Scoring of research priorities for the Philippine Liver Research Network
<p>Health research networks are viewed as an avenue through which complex health problems can be solved. Networks apply multidisciplinary expertise and methodologies to strengthen current evidence for a particular disease or condition. In 2019, the Philippine Council for Health Research Development signed a bilateral agreement with the Fondazione Italiana Fegato. Recognizing the need for liver research in the Philippines, one of their main areas of collaboration is the creation of the Philippine Liver Research Network. This exercise aimed to develop a research agenda for the network. This research priority-setting exercise followed steps based on the Philippine National Health Research System Guidelines for Health Research Prioritization and the James Lind Alliance framework: 1) identify stakeholders to participate, 2) map existing liver studies in the Philippines, 3) generation of the interim list of priorities, 4) research priority-setting consultation, and 5) seeking feedback on the research agenda from stakeholders. Seven stakeholders participated in the research priority-setting exercise. Soliciting research questions from stakeholders resulted in sixty-five initial research priorities. The final research agenda was selected through criteria selection, weight assignment, and scoring of each research priority in a consultation. The Philippine Liver Research Network aims to consolidate and capitalize on multi-sectoral and mult-disciplinary collaboration opportunities to advance liver health research in the country. With the eventual establishment of the network, the interdisciplinary research agenda presents an important opportunity for further funding. Although the network is currently in its early stages of formation, this exercise may serve to aid in further organizing the network and its stakeholders under the research agenda for 2020-2022.</p>
ScienceDex guides
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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.