Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,410
datasets available to search
ShareScore release 0.9.0
Dataset results
6,410 results for “results”
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Finland
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_FI - Finnish Food Authority (Evira)</li> <li>TSE_2022_FI - Finnish Food Authority (Evira)</li> <li>TSE_2021_FI - Finnish Food Authority (Evira)</li> <li>TSE_2020_FI - Finnish Food Authority (Evira)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - United Kingdom in respect of Northern Ireland
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_XI - Department of Agriculture, Environment and Rural Affairs</li> <li>TSE_2022_XI - Department of Agriculture, Environment and Rural Affairs</li> <li>TSE_2021_XI - Department of Agriculture, Environment and Rural Affairs</li> </ul> <p> </p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - North Macedonia
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2022_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2021_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2020_MK: Food and Veterinary Agency (FVA)</li> <li>TSE_2019_MK: Food and Veterinary Agency (FVA)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Cyprus
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2022_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2021_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2020_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> <li>TSE_2019_CY: Ministry of Agriculture, Rural Development and Environment (VS-MOA)</li> </ul>
Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.
<p>This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.</p> <p> </p> <p>===============================================================================<br>===============================================================================<br>Overview<br>===============================================================================<br>===============================================================================</p> <p>Data and model products associated with the manuscript "Mammal niches are not <br>conserved over continental scales" by Goldstein et al. </p> <p>Files are organized into two subdirectories. The first, "model_inputs/", <br>contains 8 data files intended to be used as part of the reproducible code <br>repository at https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible. <br>The second subdirectory, "model_outputs/", contains modeled products giving<br>estimated spatially varying niche relationships and predictions of relative<br>abundance.</p> <p>Below, we describe the contents of each file type. See the main manuscript <br>for full methodology, data sources, and discussions of spatial scales.</p> <p>NOTE: Version 1 of this dataset contained some errors that have been corrected<br>in Version 2. Version 2 was used as the input dataset for the analyses in the<br>associated manuscript. Version 1 should not be used.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 1: "model_inputs/"<br>===============================================================================<br>===============================================================================</p> <p>Two versions of each of four files are provided, corresponding to analyses <br>that do or do not consider ancient genetic lineages as potential sources of <br>spatial nonstationarity in mammal niches. Each file type is formatted the same,<br>and the versions are differentiated by either the suffix "nolineage" or <br>"lineage" in the filename. </p> <p>===============================================================================<br>File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv<br>===============================================================================<br>These files are .csvs giving spatial covariate data for each scale 2 cell<br>in North America, summarized to 5000 m. All percentage values are given in <br>10ths of a percent (scale of 0-1000). The following columns are provided:</p> <p>- grid_cell: Scale 2 cell ID<br>- Arable: pct arable land (Jung et al. 2020)<br>- EVI_mean: mean enhanced vegetation index (Didan 2021)<br>- EVI_Q95: 95th quantile of EVI (Didan 2021)<br>- Forest: Pct forest cover (Jung et al. 2020)<br>- Grassland: pct grassland (Jung et al. 2020)<br>- Pastureland: pct pastureland (Jung et al. 2020)<br>- Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018)<br>- Precipitation: avg annual precip. (Vega et al. 2017)<br>- Shrubland: pct shrubland (Jung et al. 2020)<br>- Temp_max: Average maximum daily temperature (Vega et al. 2017)<br>- Terrain_roughness (Amatulli et al. 2018)<br>- Wetlands: pct wetlands (Jung et al. 2020)<br>- is_land: Whether or not the cell is on land vs. ocean, used for filtering<br>- Agriculture: Pct. agricultural land (Jung et al. 2020)<br>- Pop_den_sqrt: Square root of human population density (CIESIN 2018)<br>- EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)</p> <p> </p> <p>===============================================================================<br>File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv<br>===============================================================================</p> <p>These files give summaries of iNaturalist sampling effort and detections<br>for target species. The following columns are provided:</p> <p>- grid_cell: Scale 3 cell ID<br>- n: Total iNaturalist effort in the cell (number of obs. of all mammals)<br>- The remaining columns are named for species. Each column gives the count<br> of observations of the species in the cell.</p> <p>===============================================================================<br>File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS<br>===============================================================================</p> <p>The ct_datlist files contain R objects that are lists of lists. These objects ultimately<br>contain all of the camera detection histories and camera-level covariate data used<br>in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package<br>to organize these detection data.</p> <p>Each outer list is of length equal to the number of species. The ith element of each<br>list contains the following named slots:</p> <p>- species: a string giving the name of the ith species<br>- umf: an unmarkedFrameOccu object. This object has three important slots:<br> - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating<br> whether the target species was observed in that 10-day window;<br> - siteCovs: a (# deployments) x 2 data frame with the following columns:<br> - site_ID: A unique ID of the exact location, shared by deployments with the same<br> coordinates<br> - subproject_ID: A unique ID indicating which camera array is associated <br> with this deployment<br> - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:<br> - year: the year of survey, relative to 2020 (zero-year is 2020)<br> - yday_scaled: the (scaled) Julian date of the beginning of the window<br> - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect<br> - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)<br> - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)<br> - obs_len_scaled: Scaled duration of window, to account for some windows <br> being cut off at < 10 days<br>- coords: a data frame. Originally, this file gave the exact position for each camera,<br> but these exact locations have been scrubbed for privacy. See the original sources<br> cited in the manuscript for full details. This data frame contains the following column:<br> - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera</p> <p>===============================================================================<br>File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv<br>===============================================================================</p> <p>These files are used for bookkeeping to track the relationships between the <br>three spatial scales in this study. Each row corresponds to a single "scale 2"<br>cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each<br>species indicating whether and where it is in the species' range. </p> <p>Note that the scale names in the code don't match the manuscript. In the code,<br>"scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity<br>grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km<br>SVC grid.</p> <p>The following columns are provided:<br>- scale2_grid_ID: unique ID for each cell in the 5 km intensity grid<br>- scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation<br>- scale4_grid_ID: unique ID for each cell in the 100 km SVC grid<br>- GRID_ID_[species]: for each species, a column is provided on the S4 scale<br> counting each cell in the species' modeled range. NAs<br> indicate that the S2 cell defined in the row is not<br> included in the species' modeled range.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 2: "model_outputs/"<br>===============================================================================<br>===============================================================================</p> <p>===============================================================================<br>File 1: svc_estimates.csv<br>===============================================================================</p> <p>This file gives an estimate of the effect of each covariate on each species'<br>intensity, and the uncertainty in that estimate, for each species/covariate<br>pair. Results correspond to lineage models for species with phylogeographies<br>and non-lineage species otherwise. Each row represents the effect of one <br>covariate on one species' relative intensity process within one 100 km cell g. <br>Note that many estimates of beta_g are uncertain even for strong spatial <br>effects---the model is often confident that a spatial process is supported <br>while estimates of the realized process are uncertain.</p> <p>The following columns are provided:<br>- x: the x-coordinate of the 100 km cell<br>- y: the y-coordinate of the 100 km cell<br>- species<br>- parname: the name of the covariate<br>- mean: the mean of the posterior samples of beta_g<br>- 2.5%: the 2.5th quantile of the posterior samples of beta_g<br>- 50%: the 50th quantile of the posterior samples of beta_g<br>- 97.5%: the 97.5th quantile of the posterior samples of beta_g</p> <p>The following spatial projection is used to define X/Y coordinates:<br>"+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83"</p> <p>===============================================================================<br>File 2: predicted_intensity.tif<br>===============================================================================</p> <p>This file contains a raster "brick" giving the predicted intensity surface <br>and uncertainty in this surface for each species. All predictions are generated<br>using models that do *not* account for lineage information---this means that <br>predictions for species with lineages are not from the models reported in the<br>main manuscript. The reason for this is that we found that lineages were <br>overall unsupported, so better predictions can be arrived at by excluding this<br>source of uncertainty in the underlying intensity process.</p> <p>The raster brick has 66 layers. Each layer provides either the mean predicted<br>log intensity in each grid cell across the species range or else provides<br>the standard error of that predicted log intensity. Layer names indicate output<br>type and species associated with each layer.</p> <p><br>===============================================================================<br>===============================================================================<br>References<br>===============================================================================<br>===============================================================================</p> <p>Camera data are obtained from the following sources, which can be consulted to<br>obtain the original raw camera data</p> <p>- Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353.<br>- Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775.<br>- Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318.<br>- Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024).<br>- Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252.<br>- Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023).<br>- Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372.<br>- Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29.<br>- McMurry, S. et al. In review (2024).<br>- Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287. <br>- Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295.<br>- McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29.<br>- McMurry, S., Parsons, A., Lasky, M., Luongo, K., Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K., Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29.</p> <p>Covariate data are obtained from the following sources:<br>- Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).<br>- Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).<br>- Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).<br>- Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F.<br>- Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021).<br>- Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).<br>- Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).<br>- Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024).</p> <p>iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).</p>
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
Data and Results of eELib Simulations for the User-Based Multi-Use of Battery Storage Systems
<p>The dataset contains the configuration for the eElib models (model_data.json) and computed simulation results (.hdf5-files). The following scenarios were computed:</p> <ul> <li>ave_A_static-eq</li> <li>ave_A_static</li> <li>ave_A_dynamic_charging</li> <li>ave_A_fully_dynamic</li> <li>ave_B_static-eq_bss</li> <li>ave_B_static_bss</li> <li>ave_B_dynamic_charging_bss</li> <li>ave_B_fully_dynamic_bss</li> <li>MELANI_static</li> <li>MELANI_static_equal</li> <li>MELANI_dynamic_charging</li> <li>MELANI_fully_dynamic</li> </ul> <p><strong>Description of syntax of simulation results:</strong></p> <ul> <li>average (ave_B is half the size of the BSS of ave_A) and MELANI describe the considered multi-family house</li> <li>static/ static / dynamic_charging / fully_dynamic are the three developed operating strategies for the user-based multi-use</li> <li>static-equal: the allocation keys are equally, i.e., the PVS and BSS capabilities are equally distributed among the households of the multi-family house</li> </ul> <div> <div><strong>As part of the publication:</strong></div> <div>Henrik Wagner, Constantin von Lützow, Marcel Lüdecke, Michel Meinert, Bernd Engel "Empowering Collective Self-Consumption in Multi-Family Houses: User-Based Multi-Use of Residential Battery Storage Systems", 23rd Wind & Solar Integration Workshop 2024, Helsinki, Finland, doi: 10.1049/icp.2024.3901</div> <div> </div> <div><strong>Changelog:</strong></div> <div>v2: Added doi for WIW 2024 conference paper to improve citation possibilities</div> </div>
Experimental Results of the MANET4E experiment (Fed4FIRE+)
<p>This dataset contains measured values from the MANET4E experiment, which was funded as part of the Fed4FIRE+ F4Fp-09 call. Aim of the experiment was to research a new communication approach using mobile peer-to-peer network technologies in combination with a new blockchain solution and services. The combination of both technological parts should enable the development of future decentralized and self-organizing energy management systems. In this Fed4FIRE+ Stage-2 experiment, we analysed the blockchain behaviour in the peer-to-peer network and identified the optimal blockchain configuration (block size, distribution rates, distribution of the blocks, packet loss, etc.) in the imec w-iLab.1 testbed. The measurements were carried out in the imec w-iLab.1 testbed in Ghent, Belgium. More details can be found in the deliverables and final report of the MANET4E project.</p>
The 2020 Comparison of Tools for the Analysis of Quantitative Formal Models: Results and Reproduction
<p>This archive contains detailed results from QComp 2020 as well as the necessary scripts and data to reproduce them.</p> <p>Visit http://qcomp.org for more information for QComp.</p> <p>Overview of Contents</p> <p>- `qcomp.org/` contains the state of our website from the timepoint of the competition. This includes:<br> - All benchmark files, browsable at `qcomp.org/benchmarks/index.html`<br> - Detailed competition results in a human-readable format, browsable at `https://qcomp.org/competition/2020/`<br> - `logs/` contains the raw logfiles and data gathered by our scripts<br> - `scripts/` contains scripts to replicate the whole competition<br> - `toolpackages/` contains a package for each participating tool which includes<br> - Instructions for obtaining and installing the tool<br> - a file `invocations.json` listing the commandlines used in QComp 2020<br> - a file `tool.py` providing functionalities to obtain the result from the tool output.</p>
End-user's survey results on needs and expectations for next- generation Energy Performance Certificates (H2020 X-tendo project)
<p>The SPSS data file consists of survey data from the X-tendo project on the end-user needs and expectations from next-generation energy performance certificates.</p>
ACTIVAGE results lessons learnt workshops
<p>ACTIVAGE results lessons learnt workshops:</p> <ul> <li>SoA sobre guidelines de UX/UI for elderly users</li> <li>ACTIVAGE Lessons learnt UX/UI</li> <li>IND. DS Lessons learnt: DS individual contributions in the different lessons.</li> <li>ACTIVAGE Lessons learnt: final refined version</li> </ul>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
Operating conditions and steam gasification results
<p>Dataset of the Article "Development of a Chemical Quasi-Equilibrium Model of Biomass Waste Gasification in a Fluidized-Bed Reactor by Using Aspen Plus".</p> <p>In the delicate context of climate change, biomass gasification has been demonstrated to be a very useful technology to produce power and hydrogen. Nevertheless, in literature, there is a lack of a flexible and fast but accurate model of biomass gasification that can be used with all the combinations of oxidizing agents, taking into account both organic and inorganic contaminants, and able to give results that are more realistic. In order to do that, a model of biomass gasification has been developed using the chemical engineering software Aspen Plus. The developed model is based on the Gibbs free energy minimization applying the restricted quasi-equilibrium approach via Data-Fit regression from experimental data. The simulation results obtained, considering different mixes of gasifying agents, were compared and validated against experimental data reported in literature for the most advanced fluidized bed technology. The maximum discrepancy value obtained for hydrogen, with respect to experimental data, is of 8%, and all the other values reached by the developed simulations, considering both organic and inorganic compounds, are in good agreement with literature data. The gas yield reached by the developed simulation is in the range of 1.1–1.3 Nm<sup>3</sup>/kg. </p>
Retrieval results for optically thin clouds in the trades
<p>ASTER satellite observations at 15 m pixel resolution are used to extract the signal of optically thin clouds during the EUREC4A field campaign (https://doi.org/10.5194/essd-2021-18). The signal of optically thin clouds is derived as a residual from the all-sky minus the simulated clear-sky (https://doi.org/10.5281/zenodo.4842675) and minus the known cloudy signal according to following a common cloud masking scheme. The paper describing the method, dataset, and results is intended for publication in the journal of Atmospheric Chemistry and Physics (ACP) under Mieslinger et al., 2021.</p> <p>The dataset includes basic information of the relevant input variables to clear-sky radiative transfer simulations as well as the resulting probability density function over reflectance values and for discrete flag values (clear-sky, optically thin clouds, clouds) given an ASTER observation. This data builds the basis for any derived quantities such as the area fraction or the expected reflectance corresponding to a certain flag value.</p>
3D-Scere static files and result table
<p>Static files, result table and graphical representations associated with the 3D-Scere project (dashboard and publication).</p> <p> </p> <p>3D_distances.parquet.gzip: three-column parquet file with 3D distances between <em>Saccharomyces cerevisiae </em>chromosomal features.</p> <p>SCERE.db: SQLite database of <em>Saccharomyces cerevisiae </em>features build from the <a href="https://www.yeastgenome.org/">SGD</a>.</p> <p>Table1.tsv: tab-separated values file with Kolmogorov Smirnov test results for <em>Saccharomyces cerevisiae </em>transcription factors.</p> <p>supplementary-data-file-S4.zip: ZIP file with graphical representations associated to each transcriptional module.</p>
PolarMorphism results
<p>polarmorphism_results.tar.gz is a directory containing PolarMorphism results with significantly shared SNPs for all pairwise combinations of the traits mentioned at the end of this description. Each row contains one SNP with the respective rsid (snpid), distance (r), angle (angle), whitened z-scores for trait 1 and trait 2 (z.whitened.*), p-value and q-value for r (r.pval and r.qval) and p-value and q-value for the angle (theta.pval and theta.qval).</p> <p>polarmorphism_results_clumped.tar.gz is a directory containing the significant loci obtained after clumping the above results. See the accompanying preprint/paper for details).</p> <p>The following traits were used (see the file "GWAS.csv" on this page for references to the original papers and the accompanying preprint / paper for preprocessing details):</p> <p>Alzheimer's Disease, Atrial Fibrilation, Amyotrophic Lateral Sclerosis, Any stroke, Autism Spectrum Disorder, Asthma, Breast Cancer, Bipolar Disorder, Body Mass Index, Coronary Calcification, Coronary Artery Disease, Cardio-Embolic stroke, Carotid Intima-Media Thickness, Cigarettes per Day, Diastolic Blood Pressure, Depressive symptoms, Educational Attainment, Ever smoked, Forearm Bone Mass Density, Femoral Neck Bone Mass Density, Former Smoker, High-Density Lipoprotein, Height, Heart Failure, Inflammatory Bowel Disease, Insomnia, Intelligence Quotient, Any Ischemic stroke, Large Artery Stroke, Low-Density Lipoprotein, Onset Smoking, Lumbar Spine Bone Mass Density, Major Depression Disorder, Neuroticism, Nonischemic Cardiomyopathy, Parkinson's Disease, Plaque Presence, Pulse Pressure, Prostate Cancer, Systolic Blood Pressure, Small Vessel Disease, Subjective Well-Being, Type 2 Diabetes, Type 2 Diabetes adjusted for BMI, Total Cholesterol, Triglycerides</p>
DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region
<p>This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - <em>Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers</em>, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a></p> <p>Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.</p>
CROSSBOW TC_01.05.01 final demonstration results
<p>This data set represents final demonstration results for the Test Case 5.1 from the High Level Use case 1 of the CROSSBOW project. It contains lists of Critical Network Element & Contingency (CNEC) pairs for 17 SEE borders (AL-GR, AL-RS, BA-HR, BA-ME, BA-RS, BG-GR, BG-MK, BG-RO, BG-RS, BG-TR, GR-ALMKBGTR, ME-AL, ME-RS, RS-BAHR, RS-BGRO, RS-HU, RS-MEMKAL) and 4 seasons (spring, summer, autumn and winter), which are used as input data for Net Transmission Capacity (NTC) calculation. For each CNEC pair in the list, Outage Transmission Distribution Factor (OTDF) is presented. Selection of CNEC pairs is determined based on predefined criteria (OTDF>20%) , using algorithm presented in CROSSBOW deliverable: D4.2 CROSSBOW Regional Operation Centre Balancing Cockpit (ROC-BC).</p>
docking result of androgen
<p> </p> <div>The docking result of androgen and androgenic blocker by using autodock tools and autodock vena </div> <p> </p>
Model results and observation data in Zhang et al. modeling of wave interference at Ocean Beach, CA
<p>The dataset contains modeling results and observation data supporting the manuscript of Phase-resolved modeling of wave interference and its effects on nearshore circulation in a large ebb shoal-beach system by Yu Zhang, Fengyan Shi, Jim Kirby, Xi Feng.</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.