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12,346 results for “derivatives”
Biplane fluoroscopy derived humerus and scapula kinematics during arm elevation and rotation
<p>This dataset contains torso, scapula, and humerus kinematics from 20 healthy subjects performing coronal plane abduction, scapular plane abduction, forward elevation, internal external rotation at 90º of abduction, and internal external rotation in adduction. The humerus and scapula were imaged at 100 Hz using a biplane fluoroscopy system. 3D models of the humerus and scapula were constructed from each subject’s CT scan. Model-based markerless tracking ascertained the 3D position and orientation of each bone model by semi-automatically aligning digitally reconstructed radiographs against each frame of the biplane fluoroscopy recordings. The kinematics of the torso were measured using skin marker motion capture.</p>
CLDF dataset derived from Key and Comrie's "Intercontinental Dictionary Series" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Key, Mary Ritchie & Comrie, Bernard (eds.) 2023. The Intercontinental Dictionary Series. Leipzig: Max Planck Institute for Evolutionary Anthropology.</p> </blockquote>
Data and graphics from: "Three invariants of strange attractors derived through hypergeometric entropy"
<p>This package contains data and graphics related to the publication:</p><p>Keisuke Okamura, "Three invariants of chaotic attractors derived through hypergeometric entropy", Chaos, Solitons & Fractals 170 (2023) 113392; <a href="https://doi.org/10.1016/j.chaos.2023.113392">https://doi.org/10.1016/j.chaos.2023.113392</a></p><p> </p><p><strong>Abstract of the original article</strong></p><p>A new description of strange attractor systems through three geometrical and dynamical invariants is provided. They are the correlation dimension (\(\mathcal{D}\)) and the correlation entropy (\(\mathcal{K}\)), both having attracted attention over the past decades, and a new invariant called the correlation concentration (\(\mathcal{A}\)) introduced in the present study. The correlation concentration is defined as the normalised mean distance between the reconstruction vectors, evaluated by the underlying probability measure on the infinite-dimensional embedding space. These three invariants determine the scaling behaviour of the system's Rényi-type extended entropy, modelled by Kummer's confluent hypergeometric function, with respect to the gauge parameter (\(\rho\)) coupled to the distance between the reconstruction vectors. The entropy function reproduces the known scaling behaviours of \(\mathcal{D}\) and \(\mathcal{K}\) in the 'microscopic' limit \(\rho\to\infty\) while exhibiting a new scaling behaviour of \(\mathcal{A}\) in the other, 'macroscopic' limit \(\rho\to 0\). The three invariants are estimated simultaneously via nonlinear regression analysis without needing separate estimations for each invariant. The proposed method is verified through simulations in both discrete and continuous systems.</p><p> </p><p><strong>Data files</strong></p><p>The following files are provided (see the original article for the notation):</p><ul><li>The comma-separated values (CSV) file named 'est_sum_all.csv' summarises the results of the correlation dimension (\(\mathcal{D}\)), correlation entropy (\(\mathcal{K}\)) and correlation concentration (\(\mathcal{A}\)) estimates for two discrete (logistic and Hénon maps) and four continuous (Lorenz, Rössler, Duffing-Ueda and Langford attractors) chaotic systems in tabular form.</li><li>The six CSV files whose file names begin with 'H_' record the extended Rényi entropy values calculated for each \(\sigma\) value (in increments of 0.1) for the chaotic system whose name follows the prefix, for each embedding dimension (\(m\)). The column 'mN' shows the results for the embedding dimension \(N\).</li><li>The six PDF files whose file names begin with 'gr_' illustrate, for the named chaotic systems following their prefixes, their i) appearance (left), ii) graph of the extended Rényi entropy as a function of \(\sigma\) (centre) and iii) the chaotic invariants (\(\mathcal{D}, \mathcal{K}, \mathcal{A}\)) estimation results (right).</li></ul>
A catalog of associated, machine-learning-derived phase arrival times for ten days of seismic data in the Yellowstone region
<p>This dataset contains the associated phase picks and event information from applying a deep learning phase picker to continuous data recorded over March 25 – April 3, 2014, on 20 three-component stations and 14 vertical-component stations in the Yellowstone region. This 10-day period contains an M<sub>w</sub> 4.8 event, the largest earthquake in the Yellowstone region since 1980. The catalog and deep learning phase picker are described in Armstrong et al. (submitted).</p> <p>The arrivals were associated using the method described by Baker et al. (2021) and located using HypoInverse2000 (Klein, 2002). There are 1,053 events in this catalog, including 855 that were previously unidentified. Events that also appear in the University of Utah Seismograph Stations catalog have an event identifier (evid) beginning with “6”, while new events begin with “9”. </p> <p>Columns include:</p> <ul> <li>A simple event number</li> <li>the network, station, channel, and location code for the arrival time</li> <li>the arrival time in UTC (arrival_time) and Unix (arrival_time_epoch) format</li> <li>any static correction applied to the arrival time</li> <li>the P-pick first motion polarity as determined by a machine learning model - up (1), down (-1), or unknown (0)</li> <li>the arrival time residual </li> <li>the take off angle in degrees </li> <li>the event latitude and longitude in degrees</li> <li>the event depth in km</li> <li>the event origin time in UTC (origin_time) and Unix (origin_time_epoch) format</li> <li>the azimuthal gap of the event in degrees</li> <li>the root mean square error (RMS) of the event location</li> <li>the event identifier (evid) - begins with a “6” for events in the UUSS catalog and a “9” for new events</li> </ul> <p> </p>
CLDF dataset derived from a preprint of 'Új magyar etimológiai szótár' [New Hungarian Etymological Dictionary] by Károly Gerstner (ed.)
<p>Cite the source of the dataset as:</p> <blockquote> <p>Gerstner, Károly (ed.) (2011-2023). Új magyar Etimológiai Szótár. Hungarian Academy of Sciences, Budapest. http://uesz.nytud.hu/.</p> </blockquote>
CLDF dataset derived from 'West Old Turkic' by András Róna-Tas and Árpád Berta from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Róna-Tas, András, & Berta, Árpád. (2011) West Old Turkic. Harrassowitz Verlag, Wiesbaden</p> </blockquote>
CLDF dataset derived from Greenhill et al.'s "Austronesian Basic Vocabulary Database" from 2022 focusing on Oceanic languages
<p>Cite the source of the dataset as:</p> <blockquote> <p>Greenhill, S.J., Blust. R, & Gray, R.D. (2008). The Austronesian Basic Vocabulary Database: From Bioinformatics to Lexomics. Evolutionary Bioinformatics, 4:271-283.</p> </blockquote>
Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps
<p><strong>Title:</strong></p> <p>Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps</p> <p><strong>Citation:</strong></p> <p>Seeger, K.; Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Zenodo, <a href="https://doi.org/10.5281/zenodo.7875965">https://doi.org/10.5281/zenodo.7875965</a>.</p> <p><strong>Supplement to:</strong></p> <p>Seeger, K., Minderhoud, P. S. J., Peffeköver, A., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, and Brill, D. (2023): Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding. EGUsphere [preprint], <a href="https://doi.org/10.5194/egusphere-2022-1425">https://doi.org/10.5194/egusphere-2022-1425</a>.</p> <p><strong>Abstract:</strong></p> <p>The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast.</p> <p>The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements.</p> <p>For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material.</p> <p>File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif</p> <p>File format: GEOTIFF file</p> <p>Spatial reference: MMR2000_46N</p> <p>Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; <a href="https://doi.org/10.5194/os-17-789-2021">https://doi.org/10.5194/os-17-789-2021</a>) transposed to EGM96</p> <p>Cell size: 750 × 750 m</p> <p>File name: DataPoorAreas_MMR2000.shp</p> <p>File format: ESRI Shapefile</p> <p>Spatial reference: MMR2000_46N</p>
Data of yield in a mandarin crop derived from Diverfarming project
<p>Crop yield data, auxiliary data and methods metadata from a mandarin crop studied in Diverfarming project</p>
A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s
<p>Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).</p>
AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections
<p>Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (<a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov/</a>) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. For detailed information concerning this dataset, please refer to our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2]. </p> <p>We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [3] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [4]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [5].</p> <p>We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [6,7], and the National Lung Screening Trial (NLST collection) [8,9]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [10] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data.</p> <p>IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the <em>dcmqi</em> [12], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using <em>dcmqi </em>and <em>highdicom</em> [13]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection. </p> <p>The dataset is available in IDC, and is accompanied by our publication <a href="https://www.nature.com/articles/s41597-023-02864-y">here</a> [2]. This pre-print details how the data were generated, and how the resulting DICOM objects can be interpreted and used in tools. Additionally, for further information about how to interact with and explore the dataset, please refer to our <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/">repository</a> and accompanying <a href="https://github.com/ImagingDataCommons/nnU-Net-BPR-annotations/blob/main/usage_notebooks/scientific_data_paper_usage_notes.ipynb">Google Colaboratory notebook</a>. </p> <p>The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files. </p> <ul> <li>nsclc <ul> <li>2d-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_lowres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>3d_fullres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_gt <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files. </p> <ul> <li>nlst <ul> <li>3d_fullres-tta <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_SEG.dcm</li> <li>ReferencedSeriesInstanceUID_features_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_regions <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_regions_SR.dcm</li> </ul> </li> </ul> </li> </ul> </li> <li>sr_landmarks <ul> <li>PatientID <ul> <li>StudyInstanceUID <ul> <li>ReferencedSeriesInstanceUID_landmarks_SR.dcm </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>The query used for NSCLC-Radiomics is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NSCLC_Radiomics_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nsclc_radiomics_series_analyzed.csv">here</a>. The query used for NLST is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/NLST_query.txt">here</a>, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is <a href="https://github.com/ImagingDataCommons/ai_medima_misc/blob/main/common/queries/zenodo_nlst_series_analyzed.csv">here</a>. The two csv files that describe the series analyzed, <em>nsclc_series_analyzed.csv</em> and <em>nlst_series_analyzed.csv</em>, are also available as uploads to this repository. </p> <p><em>Version updates: </em></p> <p><em>Version 2: For the regions SR and landmarks SR, changed to use a distinct TrackingUniqueIdentifier for each MeasurementGroup. Also instead of using TargetRegion, changed to use FindingSite. Additionally for the landmarks SR, the TopographicalModifier was made a child of FindingSite instead of a sibling.</em></p> <p><em>Version 3: Added the two csv files that describe which series were analyzed </em></p> <p><em>Version 4: Modified the landmarks SR as the TopographicalModifier for the Kidney landmark (bottom) does not describe the landmark correctly. The Kidney landmark is the "first slice where both kidneys can be seen well." Instead, removed the use of the TopographicalModifier for that landmark. For the features SR, modified the units code for the Flatness and Elongation, as we incorrectly used mm units instead of no units. </em></p>
SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)
<p><strong>SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution </strong>by applying SM2RAIN algorithm (<em>Brocca et al., 2014; 2019) </em>to Soil Moisture data from SMCI Soil moisture (<a href="https://doi.org/10.5194/essd-14-5267-2022"><em>https://doi.org/10.5194/essd-14-5267-2022</em></a>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (<a href="https://doi.org/10.5194/hess-26-2481-2022">https://doi.org/10.5194/hess-26-2481-2022</a>) was adopted to obtain rainfall. Precipitation data from HRLT (<a href="https://doi.org/10.5194/essd-14-4793-2022">https://doi.org/10.5194/essd-14-4793-2022</a>) were adopted to obtain mean climatology.</p> <p>The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.</p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)
<p>This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul> <p> </p>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Phlorest phylogeny derived from Michael et al. 2015 'A Bayesian Phylogenetic Classification of Tupi-Guarani'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Michael L, Chousou-Polydouri N, Bartolomei K, Donnelly E, Wauters V, Meira S & O'Hagan Z. 2015. A Bayesian Phylogenetic Classification of Tupi-Guarani. LIAMES 15(2):1–36.</p> </blockquote>
Global Water Body Levels Derived from ICESat-2
<p>This dataset contains water level records derived from ICESat-2 for 227,386 lakes spanning Oct 14, 2018 to July 16, 2020. These records have been updated to reflect an error in the calculation of lake area in the previous version which led to an overestimation of lake area at high latitudes. For details on how this correction was performed, please see the 2023 Addenda to Cooley et al (2021).</p> <p>A complete description of the method used to derive water level from ICESat-2 can be found in Cooley et al (2021), but is briefly summarized below:</p> <ol> <li>We create a conservative water mask modified from the Global Surface Water Occurrence (GSWO) product (Pekel et al., 2016);</li> <li>We intersect ATL08 mean terrain height returns with this water mask, requiring water bodies to receive at least three ICESat-2 point observations on the same day to be included in the analysis;</li> <li>We filter observations based on the mean standard deviation of returns, among other factors;</li> <li>We aggregate observations to monthly timesteps;</li> <li>We calculate seasonal variability in water level as the maximum minus the minimum monthly water level over the 22-month period.</li> </ol> <p>This dataset contains:</p> <ol> <li><strong>ICESat2_lake_variability_v2_updated.shp</strong>: A shapefile containing lake points and summary statistics (i.e. height variability, storage variability, etc) <em>*updated to include corrected lake area and storage values</em></li> <li><strong>ICESat2_lake_height_time_series_v2_updated.csv</strong>: A csv file of the monthly water height time series used to calculate the global lake level variability <em>*updated to include corrected lake area values</em></li> <li><strong>265 water mask GeoTiffs</strong>: Water masks created from GSWO which we intersect with ICESat-2 data to produce the water level time series <em>*unchanged from previous version</em></li> <li><strong>ICESat2_mask_reference_v2_updated.csv</strong> – A csv file which lists the corresponding water mask for each water body in the dataset <em>*updated to include corrected lake area values</em></li> <li><strong>USGS_height_validation_v2_updated.csv </strong>– A csv file containing the height comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area values</em></li> <li><strong>USGS_range_validation_ v2_updated</strong>.<strong>csv </strong>– A csv file containing the range comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area values</em></li> <li><strong>California_storage_validation_v2_updated.csv </strong>– A csv file containing the storage comparison between ICESat-2 and California Department of Water Resource gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area and storage values</em></li> </ol> <p>See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Cooley et al. 2021) and contact Sarah Cooley at <a href="mailto:scooley2@uoregon.edu">scooley2@uoregon.edu</a> with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. </p> <p>Cooley, S.W., Ryan, J.C., and Smith, L.C., (2021), Human alteration of global surface water storage variability, <em>Nature, </em>https://doi.org/10.1038/s41586-021-03262-3 </p>
Tables of verbal derivatives in -š- in the Saami languages
<p>The tables present the occurrence of verbs with a suffixal <em>-š- (-šš-)</em> by tabulating the attested derivatives across the Saami languages. Base words are given in their North Saami form by default, including approximate meanings in Finnish or the language used in sources in question. The corresponding entry number of Eliel Lagercranz's Lappischer Wortschatz (1939) is also given when applicable. For more information about sources etc., see the paper to which this is an appendix:</p> <p>Kuokkala, Juha & Koponen, Eino. 2023. On the distribution and history of Saami verbal derivatives in <em>-š-</em>. <em>Journal of Estonian and Finno-Ugric Linguistics </em>14(3). 73–105. <a href="https://doi.org/10.12697/jeful.2023.14.3.03">https://doi.org/10.12697/jeful.2023.14.3.03</a></p>
CLDF dataset derived from Grierson's "Linguistic Survey of India" from 1928
<p>Cite the source of the dataset as:</p> <blockquote> <p>Grierson, George Abraham (1928): Linguistic Survey of India. Comparative Vocabulary. Calcutta: Government of India Central Publication Branch.</p> </blockquote>
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.