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Dataset for CoordiNet D6.2 - Chapter 4 - Evaluation of Combinations of Coordination Schemes and Products for Grid Services
<p>This is a supporting material for chapter 4 of CoordiNet D6.2. The deliverable is available at:</p> <p><a href="https://coordinet-project.eu/publications/deliverables">CoordiNet deliverables (coordinet-project.eu)</a></p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>In addition, table check_system contains the forecasted flows over lines before flexibility activation. For the transmission system, the SFTN is used and only active power is calculated. For the distribution systems, a linearized model of the power flow is used, thus active and reactive power are calculated:</p> <ul> <li>System: the system (TN, DN_18, DN_69, DN_141) where the line is located;</li> <li>From/to: bus numbers of the connection;</li> <li>Flow: forecasted active flow over transmission system lines;</li> <li>Flow P/Flow Q: forecasted active/reactive flow over distribution system lines (also for interface flows TN-DN);</li> <li>Congestion: if congestion is forecasted over the line.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Human papillomavirus (HPV) detection in vaginal self-samples: evaluation of eNat® as an alternative suspension medium to ThinPrep®PreservCyt® for vaginal swabs
<p>Dataset 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays</p> <p>Dataset 2: hrHPV viral load in cervical and vaginal self-samples</p> <p>Datakey 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays</p> <p>Datakey 2: hrHPV viral load in cervical and vaginal self-samples</p>
Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem
<p>Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem</p>
Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region
<p>This data repository provides the data used in the following research project supported by an NCST (UTC) grant: Evaluating the Sustainability Impacts of Intelligent Carpooling Systems for SOV Commuters in the Atlanta Region.</p> <p>Please refer to Data Format and Content in the research report for meta-data information</p> <p>1. ABM Trip Table (Data Set 1):<br> CarpoolSim_input.csv<br> 2. Traffic Analysis Zones (TAZ) Shapefiles (Data Set 2):<br> TAZ+ECs_TAZ.zip<br> 3. Employment Center Shapefiles (Data Set 3):<br> TAZ+ECs_TAZ.zip<br> 4. Shortest Paths (Data Set 4):<br> path_retention.zip<br> 5. Analytical Outputs (Data Set 5):<br> summ_stats_ind.csv<br> summ_stats.csv</p>
Analysis scripts for the evaluation of a low-cost high-throughput plant phenotyping system
<p>Data analyses to complement "Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system" (DOI: 10.5281/zenodo.5725224). "README_SetupAndAnalyses.pdf" contains instructions for setting up the high-throughput phenotyping (HTP) system and analyzing the resulting image datasets. The analyses are split into two parts. First, the automatically acquired HTP and manually acquired (DSLR) images are processed using the Python script labeled "finalGreennessAnalyses.py". The csv file labeled "labelTable.csv" is used to rename the DSLR images in terms of the date acquired and experimental conditions and must be included for the Python script to process the DSLR images. The output of the Python script includes "greennessGoProTable.txt" containing tab-delimited data regarding foliar size and greenness for each HTP image and "greennessDSLRTable.txt" containing tab-delimited data regarding foliar size and greenness for each DSLR image. The second step of the analyses includes inferential statistics (e.g., correlations and linear mixed effects modeling) and is based on the R script labeled "ghGoProAndDSLR_toPublish2.R". The csv file labeled "parAllBenches.csv" includes average solar daily light integral (solar DLI) data that were used as part of the linear mixed effects models in R.</p>
AMASS European Testbed Artistic Experiment Evaluations
<p>PURPOSE</p> <p>The data set of 36 AMASS European Testbed case studies fulfils a specific purpose:<br> • Build a research database for good practices in the field of arts-based social interventions in AMASS partner countries.<br> • Construct a solid foundation for the development of new interventions with similar objectives.<br> • To present methods of evaluation for arts-based social interventions, avoiding obstacles that made many previous efforts in this field unsustainable and unadaptable.<br> • To offer a valid and authentic knowledge repository for policy makers, developers of future projects and researchers to identify motivations, philosophies, modes of engagement and impact of arts-based social interventions.</p>
Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data - Datasets
<p>Includes raw and processed copies of the scRNA-seq datasets used for the paper: '<strong>How does data structure impact cell-cell similarity? Evaluating the influence of structural properties on proximity metric performance in single cell RNA-seq data.'</strong></p> <p><strong>Real scRNA-seq.zip </strong>contains the Abundant (subset1) and Rare (subset 2) subsets generated to represent discretely structured datasets (sourced from<strong> </strong> Wegmann et al. 2019) and the continuously structured data (sourced from Popescu et al. 2019).</p> <p><strong>Simulated scRNA-seq.zip</strong> contains the Abundant, Moderately-Rare and Ultra-Rare subsets for discretely and continuously structured datasets. All data was simulated using the PROSSTT package in Python 3.8, as well as the dataset containing the labels to re-produce Figure 3 of the manuscript.</p> <p><strong>Results.zip </strong>contains the results for all datasets from the full analysis, in a pickled python dictionary. Code to read in and visualise results is available on the projects github</p> <p>The scripts for the dataset generation, processing and visualisation of results are available at <a href="https://github.com/Ebony-Watson/scProximitE">our github for the scProcimitE package</a>, and documentation is available <a href="https://ebony-watson.github.io/scProximitE/">here</a>.</p>
Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.
<p>This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490.</p> <p>Instructions:</p> <p>1. Unpack all files into one folder.<br> 2. Open R version 4.1.2 and set the working directory to the folder with all the files.<br> 3. Run Script.R.</p> <p>In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.</p>
Evaluation of Machine learning algortihms for classification
<p>The purpose of this report is to compare three different classifiers through supervised machine learning on two diverse datasets. The whole machine learning process was applied and conducted in different experiments. The exploration of the datasets as well as the preprocessing strategies are outlined in the following. Furthermore, the modelling processes and the performance measures on which their results are evaluated will be explained. Finally, different parameter adjustments and settings are compared and discussed which leads to a conclusion.</p>
SciKGTeX Scientific Contribution Metadata LaTeX Package User Evaluation Results & Analysis
<p>The responses and measured variables from 26 participants of the first user test of the SciKGTeX package.</p> <p><a href="https://github.com/Christof93/SciKGTeX">https://github.com/Christof93/SciKGTeX</a></p> <p>Also the raw text source for the evaluation tasks and the result analysis notebook with the results saved as tsv file.</p>
Evaluation of surface properties that influence the self-cleaning action of hydrophobic plant leaves
<p>It is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Twelve different leaf types were selected for analysis due to their water repellency and self-cleaning properties.</p>
Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance
<p>This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes:<br> - A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model).<br> - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).</p>
Evaluation Set - Contributions Similarity in the Open Research Knowledge Graph
<p>This evaluation set has been created for evaluating a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts structured ORKG contribution as input and recommends existing contributions in the ORKG semantically relevant to the given one.</p> <p> </p> <p>The evaluation set is manually annotated based on the <a href="https://www.orkg.org/orkg/featured-comparisons">featured comparisons</a> in the ORKG. In the course of this, it has been distinguished between homogeneous (those who are dissimilar in 2-3 properties) and heterogeneous (otherwise) instances. Multiple annotations have been obtained for the former and exactly one for the latter.</p> <p> </p> <p>It has been also distinguished between "with_response" and "without_response" instances (50 instances for each). The former are those contributions for them the initial version of the contributions similarity service has found similarities and the latter are the opposite case.</p> <p> </p> <p>This evaluation set has been created and applied on a modified version of the contributions similarity service in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>. The modified version of the service has simplified the document representation of contributions that are stored in an ElasticSearch index by omitting redundant terms.</p> <p>The evaluation set has the following schema:</p> <pre><code class="language-json">{ "with_response": [ { "contribution_id": "some_id", "comparison_id": "some_id", "comparison_label": "some_label", "contribution_label": "some_label", "paper": "some_id", "research_field": "some_id", "research_problems": [ "some_id" ], "annotations": [ "some_id of a similar contribution", ... ] }, ... ], "without_response": [ ... ] }</code></pre> <p> </p>
Supplemental Material for a Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches
<p>Bug benchmarks are used in development and evaluation of debugging approaches. Quantitative performance comparison of different debugging approaches is only possible when they have been evaluated on the same dataset or benchmark. However, benchmarks are often specialized towards usage for certain debugging approaches in their contained data, metrics, and artifacts. Such benchmarks can not be easily used on debugging approaches outside their scope as such approach may rely on specific data such as bug reports or code metrics not included in the dataset. Furthermore, benchmarks vary in their size w.r.t. the number of subject programs and the size of the individual subject programs. For these reasons, we have performed a systematic literature review where we have identified 73 benchmarks that can be used to evaluate debugging approaches.</p> <p>We compare the different benchmarks with respect to their size and the provided information such as bug reports, contained test cases, and other code metrics. Furthermore, we have investigated how well the benchmarks realize the <a href="https://www.go-fair.org/fair-principles/">FAIR guiding principles</a>. This comparison is intended to help researchers to quickly identify all suitable benchmarks for evaluating their specific debugging approaches. More information can be found in the publication:</p> <blockquote> <p>Thomas Hirsch and Birgit Hofer: "A Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches", Journal of Systems and Software, in press, 2022.</p> </blockquote>
D4.1. YouCount open data sample from the evaluation – current stand
<p>The H2020 YouCount project runs from February 2021 to January 2024. The overarching objectives are to generate new knowledge and innovations to increase the social inclusion of youth through co-creative youth citizen social science (Y-CSS) and to provide evidence of the actual outcomes of Y-CSS. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship.</p> <p>The YouCount evaluation design for process and outcome evaluation of Y-CSS is a multi-method approach that spans across the whole duration of the project and is carried out by the WP4 of UNIVIE. It therefore is to be classified as current work in progress, as some methods only just have been implemented and will be analyzed in the future, to estimated cross-case comparisons. The deliverable aims at making the research design, as well as the current stand of the evaluative studies, transparent and publicly available. This happens in the spirit of open science, with the goal of doing “Science for and with Society”. Hereby outlined is the theoretical design, the way of carrying it out, and the current stand of each study implementation in the overall project.</p> <p>Moreover, the D4.1 includes open data regarding the outcome methodology (pre-survey questionnaire) and a sav.file with a sample of open data collected from the current pre-survey data. See more details in the report. The attached sav.-file can provide knowledge about the used variables, to estimate occurring answering patterns very roughly, ad to familiarize with the implementation of such a pre-post-survey. However, it is to be noted that this data set is exemplary, anonymized and potentially also not complete yet and must be handled and used accordingly. Due to the relatively low number of participants (yet), this research is to be characterized as early stage research and only depicts a moment in time. This being said, the YouCount project is designed to gather a huge quantity of data that promises a variety of concrete research outputs, so future data samples will be richer for in-depth analyses. At this point, more quantitative as well as qualitative data is needed to estimate real impacts of Y-CSS in all its facettes.</p>
Test Stimuli for Context Based Evaluation of the OPUS Audio Codec
<p>A dataset that includes the 360° video .mp4 files and Ambisonic audio .wav files used in the listening tests for "Context Based Evaluation of the OPUS Audio Codec for Spatial Audio Content in Virtual Reality".</p>
UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits
<p>Summary-level GWAS data for 53 traits generated by <a href="https://www.genomicsplc.com/">Genomics plc</a> as presented in:</p> <p>Thompson D. et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits (<a href="https://doi.org/10.1101/2022.06.16.22276246">https://doi.org/10.1101/2022.06.16.22276246</a>)</p> <p>If you have any questions or comments regarding these files, please contact Genomics plc at <a href="mailto:research@genomicsplc.com">research@genomicsplc.com</a></p> <p><strong>NOTES</strong></p> <p>These analyses were carried out using the full UK Biobank (UKB) imputation data release (v3b). After removal of exclusions and withdrawals, a subset of 337,151 UKB individuals, the White British Unrelated (WBU) subgroup, was defined as the intersection of two sample groups created by Bycroft et al 2018 (Nature 562, 203-209): the ‘White British ancestry’ group (UKB Data Field 22006) and the ‘used in genetic principal components’ group (UKB Data Field 22020), the latter being high quality samples that were filtered to avoid closely related individuals. All GWAS analyses were performed on the WBU subgroup.</p> <p>Phenotypes were defined as described in Supplementary Table 1 ‘Phenotype definitions’ using a combination of Hospital Episode Statistics, Cancer Registry reports (where applicable) and self-report responses, with the exception of coronary artery disease (CAD). GWAS data was generated for both a “narrow” and a “broad” definition of CAD. The former was used as part of the training data for the Enhanced CAD PRS, the latter was used as part of the training data for the Enhanced CVD PRS. The phenotype definitions for “narrow” and a “broad” CAD are as follows:</p> <table> <tbody> <tr> <td>Narrow CAD<br> (includes angina)</td> <td>ICD10 codes (where .X indicates all subcodes) from both hospital and death records: I21, I22, I23, I24.1, I25.2, I20.X. ICD9 codes: 410-412, 42979, 413.X. OPCS-4 codes (K40.1–40.4, K41.1–41.4, K45.1–45.5,K49.1–49.2, K49.8–49.9, K50.2, K75.1–75.4, K75.8–75.9), self-reported heart attack (UKB codes 1075 in field 20002; code 1 in field 6150), self-reported coronary angioplasty (ptca) or coronary artery bypass graft (UKB codes 1070 and 1095 in field 20004), self-reported angina.</td> </tr> <tr> <td>Broad CAD<br> (includes angina and all ischaemic heart disease)</td> <td>As for Narrow CAD, plus ICD10 codes I24.X, I25X, and ICD9 codes 414.X (where .X indicates all subcodes).</td> </tr> </tbody> </table> <p>Note that there is no GWAS for cardiovascular disease (CVD) per se. This is because the UKB training data for the Enhanced CVD PRS consisted of separate GWASs for “narrow” CAD and ischaemic stroke.</p> <p>All analyses included Age at assessment, sex (for non-sex specific traits), genotyping chip, and 10 principal components as covariates.</p> <p>GWAS summary statistics for each trait were generated by applying PLINK 2.0 to the WBU subgroup, using a logistic regression for disease traits, and a linear regression model for quantitative traits. For chromosome X variants males were treated as having 0 or 2 alternative alleles.</p> <p>The results are not adjusted for genomic control.</p> <p><strong>DATA FILE CONTENT DESCRIPTION (DISEASE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size (log odds ratio)</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ncase</td> <td>Number of cases</td> </tr> <tr> <td>ncontrol</td> <td>Number of controls</td> </tr> </tbody> </table> <p><strong>DATA FILE CONTENT DESCRIPTION (QUANTITATIVE TRAITS)</strong></p> <table> <tbody> <tr> <td>cpra</td> <td>Variant ID in ‘CPRA’ format. Position reflects position in b37</td> </tr> <tr> <td>chrom</td> <td>Chromosome</td> </tr> <tr> <td>pos</td> <td>Position in base pairs (b37, 1-based)</td> </tr> <tr> <td>alt</td> <td>Alternative allele (effect allele)</td> </tr> <tr> <td>beta</td> <td>Effect size</td> </tr> <tr> <td>standard_error</td> <td>Standard error of beta</td> </tr> <tr> <td>minus_log10_p</td> <td>Minus log(base 10) of P-value</td> </tr> <tr> <td>ref</td> <td>Reference allele (non-effect allele)</td> </tr> <tr> <td>ntotal</td> <td>Total sample size</td> </tr> </tbody> </table> <p><strong>FILE NAMES</strong></p> <p>The following is a list of traits and their corresponding file names.</p> <p><em><strong>DISEASE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age-related macular degeneration</td> <td>amd_strict_UKB_WBU.csv.gz</td> </tr> <tr> <td>Alzheimer's disease</td> <td>alzheimers_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Asthma</td> <td>asthma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Atrial fibrillation</td> <td>atrial_fibrillation_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bipolar disorder</td> <td>bipolar_disorder_UKB_WBU.csv.gz</td> </tr> <tr> <td>Bowel cancer</td> <td>CRC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Breast cancer</td> <td>BC_UKB_WBU_women.csv.gz</td> </tr> <tr> <td>Coeliac disease</td> <td>celiac_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Narrow coronary artery disease</td> <td>NARROW_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Broad coronary artery disease</td> <td>BROAD_CAD_UKB_WBU.csv.gz</td> </tr> <tr> <td>Crohn's disease</td> <td>crohns_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Epithelial ovarian cancer</td> <td>OC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Hypertension</td> <td>HT_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ischaemic stroke</td> <td>IS_stroke_UKB_WBU.csv.gz</td> </tr> <tr> <td>Melanoma</td> <td>melanoma_UKB_WBU.csv.gz</td> </tr> <tr> <td>Multiple sclerosis</td> <td>multiple_sclerosis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Osteoporosis</td> <td>OP_WBU_training.csv.gz</td> </tr> <tr> <td>Prostate cancer</td> <td>PC_UKB_WBU.csv.gz</td> </tr> <tr> <td>Parkinson's disease</td> <td>parkinsons_disease_UKB_WBU.csv.gz</td> </tr> <tr> <td>Primary open angle glaucoma</td> <td>POAG_WBU_training.csv.gz</td> </tr> <tr> <td>Psoriasis</td> <td>psoriasis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Rheumatoid arthritis</td> <td>rheumatoid_arthritis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Schizophrenia</td> <td>schizophrenia_UKB_WBU.csv.gz</td> </tr> <tr> <td>Systemic lupus erythematosus</td> <td>lupus_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 1 diabetes</td> <td>t1d_UKB_WBU.csv.gz</td> </tr> <tr> <td>Type 2 diabetes</td> <td>T2D_UKB_WBU.csv.gz</td> </tr> <tr> <td>Ulcerative colitis</td> <td>ulcerative_colitis_UKB_WBU.csv.gz</td> </tr> <tr> <td>Venous thromboembolic disease</td> <td>VTE_UKB_WBU.csv.gz</td> </tr> </tbody> </table> <p><em><strong>QUANTITATIVE TRAITS</strong></em></p> <table> <tbody> <tr> <td>Age at menopause</td> <td>age_at_menopause_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein A1</td> <td>apolipoprotein_a1_UKB_WBU.csv.gz</td> </tr> <tr> <td>Apolipoprotein B</td> <td>apolipoprotein_b_UKB_WBU.csv.gz</td> </tr> <tr> <td>Body mass index</td> <td>bmi_UKB_WBU.csv.gz</td> </tr> <tr> <td>Calcium</td> <td>calcium_UKB_WBU.csv.gz</td> </tr> <tr> <td>Docosahexaenoic acid</td> <td>docosahexaenoic_acid_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated bone mineral density T-score</td> <td>BMD_WBU_training.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (creatinine based)</td> <td>egfr_UKB_WBU.csv.gz</td> </tr> <tr> <td>Estimated glomerular filtration rate (cystatin based)</td> <td>egfr_cys_UKB_WBU.csv.gz</td> </tr> <tr> <td>Glycated haemoglobin</td> <td>hba1c_UKB_WBU_nodiabetes.csv.gz</td> </tr> <tr> <td>High density lipoprotein cholesterol</td> <td>hdl_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Height</td> <td>height_UKB_WBU.csv.gz</td> </tr> <tr> <td>Intraocular pressure</td> <td>iop_WBU_training.csv.gz</td> </tr> <tr> <td>Low density lipoprotein cholesterol</td> <td>ldl_UKB_WBU_nostatins.csv.gz</td> </tr> <tr> <td>Omega-6 fatty acids</td> <td>omega_6_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Omega-3 fatty acids</td> <td>omega_3_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphatidylcholines</td> <td>phosphatidylcholines_UKB_WBU.csv.gz</td> </tr> <tr> <td>Phosphoglycerides</td> <td>phosphoglycerides_UKB_WBU.csv.gz</td> </tr> <tr> <td>Polyunsaturated fatty acids</td> <td>polyunsaturated_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Resting heart rate</td> <td>resting_heart_rate_UKB_WBU.csv.gz</td> </tr> <tr> <td>Remnant cholesterol (Non-HDL, Non-LDL cholesterol)</td> <td>remnant_cholesterol__UKB_WBU.csv.gz</td> </tr> <tr> <td>Sphingomyelins</td> <td>sphingomyelins_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total cholesterol</td> <td>total_cholesterol_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total fatty acids</td> <td>total_fatty_acids_UKB_WBU.csv.gz</td> </tr> <tr> <td>Total triglycerides</td> <td>total_triglycerides_UKB_WBU.csv.gz</td> </tr> </tbody> </table>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Monitoring feedback to authors on the quality of trials evaluating interventions aimed at preventing and treating COVID-19
<p>We aimed to assess transparency of reporting and risk of bias of randomized trials evaluating interventions aimed at preventing and treating COVID-19.</p> <p>This review is part of a larger project: the COVID-NMA project (Boutron 2020a). The COVID-NMA project aims to provide decision-makers with a complete, high-quality and up-to-date synthesis of evidence on interventions for the prevention and treatment of COVID 19. For this purpose, we perform a living mapping of all registered randomized controlled trials and a living evidence synthesis of data from RCTs. We developed a master protocol on the effect of all interventions for the prevention and treatment of COVID-19 (first published on April 8, 2020; an update on May 11, 2020, June 17, 2020, and September 8, 2020) (Boutron 2020b). We set-up a platform (<a href="https://covid-nma.com/">https://covid-nma.com</a>) where all our results are made available and updated weekly.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.