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9,277 results for “ETS”
CLDF dataset derived from Birchall et al.'s "A Combined Comparative and Phylogenetic Analysis of the Chapacuran Language Family" from 2016
<p>Cite the source of the dataset as:</p> <blockquote> <p>Birchall J, Dunn M, & Greenhill SJ. 2016. A Combined Comparative and Phylogenetic Analysis of the Chapacuran Language Family. International Journal of American Linguistics 82(3). 255–284.</p> </blockquote>
CLDF dataset derived from Blum et al.'s "A phylolinguistic classification of the Quechua language family" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Blum, Frederic, Carlos Barrientos, Adriano Ingunza & Zoe Poirier. 2023. A phylolinguistic classification of the Quechua language family. INDIANA - Anthropological Studies on Latin America and the Caribbean 40(1). 29–-54. DOI: https://doi.org/10.18441/IND.V40I1.29-54.</p> </blockquote>
CLDF dataset derived from Sagart et al.'s "Sino-Tibetan Database of Lexical Cognates" from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Laurent Sagart, Jacques, Guillaume, Yunfan Lai, and Johann-Mattis List (2019): Sino-Tibetan Database of Lexical Cognates. Jena: Max Planck Institute for the Science of Human History.</p> </blockquote>
Chirila export of languages in Malaspinas et al sample
<p>Linguistic cognate dataset that underlies the language tree associated with Malaspinas, Anna-Sapfo, et al. "A genomic history of Aboriginal Australia." <em>Nature</em> 538.7624 (2016): 207.</p>
Data for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect
<p>Dataset for: Crall et al., Spatial fidelity of workers predicts collective response to disturbance in a social insect, in final revision for Nature Communications.</p> <p>Includes two files - one behavioral data from uniquely identified worker bumblebees, and the second containing metadata for the colonies from which these data were generated (including experimental treatments, locations, sizes, etc.).</p>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Data set and benchmarks from Eriksson et al., ICAPS 2018
<p>These are the benchmarks and the experiment data used in the paper "A Proof System for Unsolvable Planning Tasks" by Eriksson et al. (ICAPS 2018). The raw data contains the logs from all runs, while the eval-directories contain a json file with all parsed attributes. The benchmarks directory contains all benchmarks used in the experiments. Finally, the file eriksson-et-al-icaps2018.html provides an overview over the most interesting attributes across all runs.</p>
Haxby et al. (2001): Faces and Objects in Ventral Temporal Cortex (fMRI)
<pre><a href="http://data.pymvpa.org/datasets/haxby2001/">http://data.pymvpa.org/datasets/haxby2001/</a> This is a block-design fMRI dataset from a study on face and object representation in human ventral temporal cortex. It consists of 6 subjects with 12 runs per subject. In each run, the subjects passively viewed greyscale images of eight object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval. Full-brain fMRI data were recorded with a volume repetition time of 2.5s, thus, a stimulus block was covered by roughly 9 volumes. This dataset has been repeatedly reanalyzed. For a complete description of the experimental design, fMRI acquisition parameters, and previously obtained results see the references_ below. Terms Of Use ============ The original authors of :ref:`Haxby et al. (2001) <HGF+01>` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license. .. _Creative Commons Attribution-Share Alike 3.0: http://creativecommons.org/licenses/by-sa/3.0/</pre> <pre>References ========== :ref:`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, P. (2001) <HGF+01>`. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430. :ref:`Hanson, S., Matsuka, T., and Haxby, J. (2004) <HMH04>`. Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area? NeuroImage 23, 156–166. :ref:`O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005) <OJA+05>`. Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590. :ref:`Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S. and Pollmann, S (2009) <HHS+09b>`. PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.</pre> <p> </p>
New annotation of the Lolium perenne genome described by Bryne et al, (2015)
<p>Annotation of the Lolium perenne genome described by Bryne <em>et al,</em> (2015, DOI: 10.1111/tpj.13037).</p> <p>To identify genic regions RNA sequencing data were aligned to the genome using Tophat (Tophat version: V2.0.11; Bowtie2 version: 2.1.0). Isoforms, of genes, were identified using Cufflinks (Version: 2.2.0). Open reading frames (ORF), were found using using program ORFpredictor (version: 3.0). Frame selection was assisted by BLASTX searching the proteomes of <em>Arabidopsis thaliana</em> (TAIR, version: 10), <em>Oryza sativa</em> (Ensembl)<em>, Gycine max </em>(Ensembl)<em>, Populus trichocarpa </em>(Ensembl)<em> and Manihot esculenta </em>(cassava, v4.1). The predicted CDS was back translated to annotate the GFF file created by Cufflinks for CDS using scripts kindly provided by Palmieri<em> et al.,</em> 2012 (doi: 10.1371/journal.pone.0046415). These results are included in the file LG_V2_full.gtf.</p> <p>Functional annoatation was using three sources. First, protein sequences were search against the <em>A. thaliana</em> proteome using BLASTP. Second, the proteins were search against the Swiss-Prot non-redundant protein database (<a href="http://www.uniprot.org/downloads">http://www.uniprot.org/downloads</a> downloaded 14/03/2016, UniProt Consortium, 2014), again using BLASTP. In the third step, the protein sequences were scanned against InterPro's signatures using InterProScan (Version: 5.16-55). These data are included in the file ALLXLOC.txt.</p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Dataset and code Petry et al. 2018 Journal of Ecology
<p>This dataset contains all the data and code needed to reproduce the analyses in</p> <p>Petry, W.K., G.S. Kandlikar, N.J.B. Kraft, O. Godoy, & J.M. Levine (2018) A competition-defence trade-off both promotes and weakens coexistence in an annual plant community. Journal of Ecology. DOI: 10.1111/1365-2745.13028</p> <p>Complete metadata is provided in README.md</p>
Data and materials for Wallace et al (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: comparison of two prospective cohort studies v1.2
<p>This comprises the data and materials for the study: Wallace E, Moriarty F, McGarrigle C, Smith SM, Kenny RA, Fahey T. (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: Comparison of two prospective cohort studies. PLOS ONE 13(10): e0206201. <a href="https://doi.org/10.1371/journal.pone.0206201">https://doi.org/10.1371/journal.pone.0206201</a></p> <p>The anonymised TILDA dataset is publicly available to researchers who meet the criteria for access, at no monetary cost, from the Irish Social Science Data Archive (ISSDA) at University College Dublin (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ucd.ie%2Fissda%2Fdata%2Ftilda%2F&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=%2Fcochi1RuRtYSUa5sF9uA%2BjOOoNYIg7DPpk0mZl5D2s%3D&reserved=0">http://www.ucd.ie/issda/data/tilda/</a>) and the Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.icpsr.umich.edu%2Ficpsrweb%2FICPSR%2Fstudies%2F34315&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=7LHSSqU8xotACMsalpAjVrV5m95DlapgQViyr4P%2FsXY%3D&reserved=0">http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315</a>). For the CPCR cohort, no provision for data sharing was included in the original ethical approval and participant consent form. As a minimal data set necessary to replicate the present study could not be deidentified due to the large number of demographic variables considered, a synthetic version of the study dataset was produced using the synthpop package in R: <a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fsynthpop%2Findex.html&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128229884&sdata=j3If%2FNe%2F1eGsGAt9hyg3ICMqmLec4aOrjRVKppaRSFU%3D&reserved=0">https://cran.r-project.org/web/packages/synthpop/index.html</a>. This dataset and the analytical code for the present study are presented here. Code developed on the synthetic data can be sent to frankmoriarty@rcsi.ie or <a href="mailto:enquiries.cpcr@rcsi.ie">enquiries.cpcr@rcsi.ie</a> to be run on the original data.</p> <p>v1.2 includes a more detailed description of how the dataset was synthesised.</p>
GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci.
<p><strong>Data from article Stocker et al. (in review) *Nature Geosci.*</strong></p> <p>The datasets provided here include:</p> <ul> <li>Site-level GPP model results from the P-model (Wang et al., 2017)</li> <li>Model outputs from global simulations with the P-model (Wang et al., 2017) as implemented for the study by Stocker et al. (2019)</li> </ul> <p>This data may be used to partly reproduce results presented in Stocker et al. (2019) <em>Nature Geosci</em>. "Partly" because we used data for our analysis that was not open access but was confidentially shared with us. This includes remote sensing-based GPP estimates from the BESS and VPM models. Other open access data that was used for the analysis may not be distributed under this DOI. This includes FLUXNET 2015 data and MODIS data.</p> <p>For reproducing results of Stocker et al. (2019) regarding site-scale evaluations, run for example the scripts `plot_bias_all.R` and `plot_bias_problem.R`, available from <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="http://doi.org/10.5281/zenodo.1423328">Zenodo</a>, using CSV files provided here (see comments in scripts). For more insight, including analysis of global simulation outputs, see RMarkdown file `si_soilm_global.Rmd`. This renders the supplementary information PDF document provided along with Stocker et al. (2019), which is available also on <a href="http://rpubs.com/stineb/si_soilm_global2">RPubs</a>.</p> <p>The present datasets are prepared by script `prepare_data_openaccess.R ` on <a href="https://github.com/stineb/soilm_global">Github</a> or <a href="https://zenodo.org/record/1286966#.W6TFipMzbUI">Zenodo</a>.</p> <p><strong>Data description</strong></p> <p><em>Site-level data</em></p> <p>Data is provided as CSV files:</p> <ul> <li>`gpp_daily_fluxnet_stocker18natgeo.csv`: Daily data for full time series (not including MODIS GPP)</li> <li>`gpp_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods corresponding to MODIS dates (including MODIS GPP)</li> <li>`gpp_alg_daily_fluxnet_stocker18natgeo.csv`: Data filtered to periods with substantial soil moisture effects ("fLUE droughts" following Stocker et al. (2018a))</li> <li>`gpp_alg_8daily_fluxnet_stocker18natgeo.csv`: Data aggregated to 8-day periods and filtered to periods with substantial soil moisture effects.</li> </ul> <p>Each column is a variable with the following name and units (not all variables are available in all files):</p> <ul> <li>`site_id`: FLUXNET site ID </li> <li>`date`: Date of measurement, units: YYYY-MM-DD</li> <li>`gpp_pmodel` and `gpp_modis`: Simulated GPP from the P-model and MODIS (see Stocker et al. (2018b), Methods, RS models), units: g C m-2 d-1 (mean across 8 day periods in respective files)</li> <li>`aet_splash`: Simulated actual evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`pet_splash`: Simulated potential evapotranspiration from the SPLASH model (Davis et al., 2017), units: mm d-1</li> <li>`soilm_splash`: Soil moisture simulated by the SPLASH model (Davis et al., 2017), normalised to vary between zero and one at the maximum water holding capacity, unitless.</li> <li>`flue`: fLUE estimate from Stocker et al. (2018). Estimates soil moisture stress on light use efficiency from flux data, unitless.</li> <li>`beta_a`, `beta_b`, and `beta_c`: Empirical soil moisture stress, used as multiplier to simulated GPP as described in Stocker et al. (2018b), unitless.</li> </ul> <p><em>Global P-model simulation outputs</em></p> <p>GPP and soil moisture output is provided as NetCDF files for simulations s0, and s1b (see Stocker et al. (2018b)). All meta information is provided therein. Files for simulation s1b are names as follows (for outputs from other simulations replace s1b with other simulation name). The fraction of each gridcell covered by land (not open water or ice) is given by separate file `s1b_fapar3g_v2_global.fland.nc`.</p> <ul> <li>`s1b_fapar3g_v2_global.d.gpp.nc`: Daily GPP from simulation s1b.</li> <li>`s1b_fapar3g_v2_global.d.wcont.nc`: Daily soil moisture from simulation s1b (is identical in other simulations, therefore not provided.)</li> </ul> <p>Due to limited total file size allowed for uploads to Zenodo, only outputs from s1b are provided here. Other outputs may be obtained upon request addressed to benjamin.stocker@gmail.com. </p> <p><strong>References</strong></p> <p>Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geoscientific Model Development 10, 689–708 (2017).<br> Hufkens, K. khufkens/gee_subset: Google Earth Engine subset script & library. (2017). doi:10.5281/zenodo.833789Running, S. W. et al. A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience 54, 547–560 (2004).<br> Stocker, B. et al., Quantifying soil moisture impacts on light use efficiency across biomes, New Phytologist, doi: 10.1111/nph.15123 (2018a).<br> Stocker, B. et al., Satellite monitoring underestimates the impact of drought on terrestrial primary productivity, Nature Geoscience (2019).<br> Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat Plants 3, 734–741 (2017).<br> </p>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
Dataset for publication: Compound parabolic collector solar disinfection system for the treatment of harvested rainwater, Strauss et al. (2018). DOI:10.1039/c8ew00152a.
<p>Datasets used for the publication: Strauss A, Reyneke B, Waso M and Khan W (2018) Compound parabolic collector solar disinfection system for the treatment of harvested rainwater. Environ Sci: Water Res. Technol. DOI: 10.1039/c8ew00152a. Please cite the article when using the datasets.</p> <p>Available datasets:</p> <ul> <li>WATERSPOUTT_688928_US_Environmental Conditions_01_1.0.0: Dataset describing the environmental conditions on sampling days while assessing a SODIS-CPC reactor for the treatment of roof-harvested rainwater.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC Schematics_01_1.0.0: Schematic diagrams showing the design of the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC-Microbiology_01_1.0.0: Dataset describing the results obtained while monitoring the microbiological quality of the roof-harvested rainwater before and after treatment with the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_SODIS-CPC-Physicochemical_01_1.0.0: Dataset describing the physicochemical quality of the roof-harvested rainwater before and after treatment with the SODIS-CPC reactor.</li> <li>WATERSPOUTT_688928_US_UV-transmittance_01_1.0.0: Dataset describing the UV transmittance of polymethyl methacrylate and borosilicate glass.</li> </ul>
Derived data and analysis code accompanying Deines et al. 2019, Environmental Research Letters
<p>This codebase accompanies the paper:</p> <p>Deines, JM, AD Kendall, JJ Butler, Jr., & DW Hyndman. 2019. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters. DOI: <a href="https://doi.org/10.1088/1748-9326/aafe39">https://doi.org/10.1088/1748-9326/aafe39</a></p> <p>Data and code at time of publication.</p>
SLAFEEL: R scripts and reformatted data analyzed by Alamil et al. (2019)
<p>SLAFEEL: Statistical Learning Approach For Estimating Epidemiological Links from deep sequencing data</p> <p>This archive contains R scripts for running analyses proposed by Alamil et al. (2019; Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases), namely<br> - functions.R that contains R functions required for computations,<br> - influenza.R, ebola.R and potyvirus.R where the analyses are implemented for each case study, and<br> - influenza-format-genomic-data.R giving an example of how to format data to be used in the statistical learning approach.</p> <p>This archive also contains the reformatted data analyzed by Alamil et al. (2019). The datasets that are provided concern swine influenza virus (reformatted from Murcia et al., 2012), Ebola virus (reformatted from Gire et al., 2014) and a wild salsify potyvirus. Two rds files are provided for swine influenza, the first one for the naive chain, the second one for the vaccinated chain. Ebola rds files are compressed into the archive ebolaRDS.zip. rds files can be loaded in the R statistical software with the command "readRDS(filename)", which returns a list. The list contains a "readme" item describing the contents of the list, as well as a "host.table" item providing metadata about host units and a "set.of.sequences" item providing sequencing data formatted in numeric matrices.</p> <p>Murcia PR, Hughes J, Battista P, Lloyd L, Baillie GJ, Ramirez-Gonzalez RH, et al. Evolution of an Eurasian avian-like influenza virus in naive and vaccinated pigs. PLoS Pathogens. 2012;8(5):e1002730.</p> <p>Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345:1369–1372</p> <p> </p> <p>Funded by the ANR - Project name: SMITID (2016-2020) - Grant number: ANR-16-CE35-0006</p>
Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science
<p>Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science</p> <p> </p> <ul> <li>info.txt contains a general description of how the data was processed and the main results.</li> <li>pipeline.tar contains the data pipeline used to process the e-MERLIN observations</li> <li>imaging.py is the script used to produce the final images</li> <li>CY6213_images.tar contain the final images of the target source (not corrected by calibration factor, described in the imaging script).</li> </ul> <p> </p>
Supplementary phylogenetic data for Manzano-Marín et. al. 2020 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems"
<p>Supplementary data for Manzano-Marín et. al. 2019 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems".</p> <p>The data set consists of four folders:</p> <p>1) "Buchnera_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Buchnera</em> and resulting tree in NEWICK format.</p> <p>2) "Erwinia_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Erwinia</em> and resulting tree in NEWICK format.</p> <p>3) "Hamiltonella_phylo”: FASTA-formatted nucleotide alignment files of each gene and NEXUS-formatted files used for Bayesian phylogenetic reconstruction of <em>Hamiltonella</em> symbionts.</p> <p>4) "HGT_genes": FASTA-formatted nucleotide alignment files of each horizontally transferred gene and non-horizontally transferred genes nupC, and <em>gpmA</em>. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p> <p>5) "Tn3_pylo": FASTA-formatted amino acid alignment files of mobile elements related to the Tn3 family resolvase/invertase found in <em>Hamiltonella</em>-associated <em>Erwinia haradaeae</em> symbionts. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p>
Supplementary data and scripts for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and de novo emergence of E5 oncogenes"
<p>Supplementary data for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and <em>de novo</em> emergence of E5 oncogenes". The data set consists of three folders: “Alignments”, “Bali-Phy” and “RandomPermutationTests”. The “Alignments” folder contains the different alignments used for phylogenetic tree construction and comparison. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</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.