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Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
Ionospheric Vertical Correlation Lengths Derived From IRI-2016 Model Errors
<p>Ionospheric vertical correlation lengths based on IRI-2016 model and Incoherent Scatter Radar (ISR) data.</p> <p><strong>Important! The analysis was performed in log space. </strong></p> <p>ISR used for this analysis:</p> <p>Jicamarca, Arecibo, Millstone Hill, Poker Flat ISR, and ResoluteBay North ISR.</p> <p>This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation.</p> <p>Inside of the .nc file:</p> <p>lat=array of geomagnetic latitudes (degrees)<br> alt=arrays of altitudes (km)<br> vert_corr1=array of size (nalt, nlat), contains vertical correlation length above the reference point for different latitudes<br> vert_corr2=array of size (nalt, nlat), contains vertical correlation length below the reference point for different latitudes<br> </p>
Diffraction images of crystals of the spectrin repeats 7 and 8 (SR7-SR8) of the plakin domain of human plectin (PDB code 5J1G): native and Hg-derivative datasets for phasing by SIRAS
<p>Diffraction images of crystals of a fragment of the plakin domain of human plectin that includes the spectrin repeats 7 to 8 (SR7-SR8).</p> <p>Images correspond to the dataset used to solve and refine the pdb entry <strong>5J1G</strong> (http://www.rcsb.org/pdb/explore/explore.do?structureId=5J1G).</p> <p> </p> <p>The structure was phase by single isomorphous replacement with anomalous scattering (SIRAS) using two datasets: one from a native crystal and another one from a crystal derivatized with the mercurial compound ethylmercurithiosalicylate (EMTS).</p> <p> </p> <p>The <strong>Native dataset</strong> was collected on a single crystal at the beamline XALOC of the ALBA Synchrotron (Barcelona, Spain) using radiation of 0.9792 Å wavelength and a PILATUS 6M detector. The dataset consists of 4 wedges of 450 images each (0.2º oscillation per image). Each wedge was collected at a different position of the same crystal. The crystals belong to the space group P2<sub>1</sub> with approximate cell dimensions <em>a</em>=45.7 Å, <em>b</em>=115.9 Å, <em>c</em>=64.8 Å, beta=97.6 º.</p> <p> </p> <p>The data from a <strong>mercurial derivative</strong> (EMTS) was collected in house using a rotating anode X-ray generator (wavelength 1.54179 Å) and a mar345 image plate detector. The dataset consists of 360 images (1º oscillation per image). The crystal was isomorphic to the native crystal.</p> <p> </p> <p>In addition to the diffraction images the following files are included:</p> <p>a) Files for indexing with the program XDS and the HKL files containing the integrated intensities.</p> <p>b) Files for scaling using the program xscale (directory XSCALE_5J1G_Native_EMTS).<br> c) The directory “phasing_shelx” contains hkl files of the intensities of the native and EMTS datasets in a format suitable for analysis with Shelx. This directory also contains the files of the phasing by SIRAS using Shelx C/D/E.</p>
Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands
<p><span lang="EN-GB">This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders:<strong> 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4</strong>). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: <strong>5_Maps</strong>). All 25 LiDAR metrics were calculated using Laserfarm workflow (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: <strong>6_Masks</strong>). To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: <strong>7_Auxiliary_data</strong>). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: <strong>8_Sensitivity_analysis</strong>). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: <strong>9_Use_case</strong>). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: <strong>10_Ground_classification</strong>). </span></p> <p><span lang="EN-GB">An overview of all the folders in the repository:</span></p> <p><strong><span lang="EN-GB">1. </span></strong><strong><span lang="EN-GB">AHN1</span></strong></p> <p><strong><span lang="EN-GB">2. </span></strong><strong><span lang="EN-GB">AHN2</span></strong></p> <p><strong><span lang="EN-GB">3. </span></strong><strong><span lang="EN-GB">AHN3</span></strong></p> <p><strong><span lang="EN-GB">4. </span></strong><strong><span lang="EN-GB">AHN4</span></strong></p> <p><strong><span lang="EN-GB">5. </span></strong><strong><span lang="EN-GB">Maps</span></strong></p> <p><span lang="EN-GB">Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset.</span></p> <p><strong><span lang="EN-GB">6. Masks</span></strong></p> <ul> <li><span lang="EN-GB">ahn3_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_powerline.tif</span></li> <li><span lang="NL">ahn1_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn2_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn3_10m_NA_mask.tif</span></li> <li><span lang="NL">a</span><span lang="NL">hn4_10m_NA_mask.tif</span></li> </ul> <p><span lang="NL"> </span></p> <p><span lang="EN-GB">It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0.</span></p> <p><strong><span lang="EN-GB">7. Auxiliary data</span></strong></p> <p><span lang="EN-GB">(1) Point_density</span></p> <ul> <li><span lang="EN-GB">ahn1_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn2_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn3_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_point_density.tif</span></li> </ul> <p><span lang="EN-GB">(2) Pulse_density</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_pulse_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_pulse_density.tif</span></li> </ul> <p><span lang="EN-GB">(3) Flighttime</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_flighttime.tif</span></li> <li><span lang="EN-GB">ahn4_10m_flighttime.tif</span></li> </ul> <p><span lang="EN-GB">(4) DTM_DSM</span></p> <ul> <li><span lang="EN-GB">ahn2_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn2_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dsm.tif</span></li> </ul> <p><span lang="EN-GB">It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2</span><span lang="EN-GB">–</span><span lang="EN-GB">AHN4. All raster layers are provide at 10 m resolution.</span></p> <p><strong><span lang="EN-GB">8. Sensitivity analysis</span></strong></p> <ul> <li><span lang="EN-GB">Dunes</span></li> <li><span lang="EN-GB">Marsh</span></li> <li><span lang="EN-GB">Grassland</span></li> <li><span lang="EN-GB">Shrubland</span></li> <li><span lang="EN-GB">Woodland</span></li> <li><span lang="EN-GB">Code</span></li> <li><span lang="EN-GB">Figure</span></li> </ul> <p><span lang="EN-GB">It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis.</span></p> <p><strong><span lang="EN-GB">9. Use_case</span></strong></p> <p><span lang="EN-GB">(1) Multi-temporal_AHN</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Usecase_multi-temporal_AHN.R</span></li> </ul> <p><span lang="EN-GB">It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). </span></p> <p><span lang="EN-GB">(2) Natura2000</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Natura2000_end2021_HABITATCLASS.csv</span></li> <li><span lang="EN-GB">Natura2000_NL_habitat_grouped.csv</span></li> <li><span lang="EN-GB">Usecase_Natura2000.R</span></li> </ul> <p><span lang="EN-GB">It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. </span></p> <p><strong><span lang="EN-GB">10. Ground classification</span></strong></p> <ul> <li><span lang="EN-GB">Raw_point_cloud</span></li> <li><span lang="EN-GB">Computed_metrics </span></li> <li><span lang="EN-GB">Plottings_and_code</span></li> <li><span lang="EN-GB">ArcGIS_project</span></li> </ul> <p><span lang="EN-GB">It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project).</span></p> <p><strong><span lang="EN-GB">Code availability</span></strong></p> <p><span lang="EN-GB">Jupyter Notebooks for processing AHN datasets: </span></p> <p><span lang="EN-GB"><a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a></span></p> <p><span lang="EN-GB">Laserfarm workflow repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a></span></p> <p><span lang="EN-GB">Laserchicken software repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a></span></p> <p><span lang="EN-GB">Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a></span></p> <p><span lang="EN-GB">Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a></span></p> <p><span lang="EN-GB">Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a></span></p> <p> </p>
CLDF dataset derived from Mamta's "South Asian Numerals Database" from 2024
<p>Cite the source of the dataset as:</p> <blockquote> <p>Mamta, K. (2024): South Asian Numerals Database (SAND). Leipzig: Max Planck Institute for Evolutionary Anthropology.</p> </blockquote>
CLDF dataset derived from Chan's "Numeral Systems of the World's Languages." from 2019
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chan, Eugene (eds.) 2019. Numeral Systems of the World's Languages. Accessed: 2019-09-30</p> </blockquote>
CLDF dataset derived from Lee's "Phonological Features of Caijia" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee, Man Hei (2023): Phonological features of Caijia that are notable from a diachronic perspective. Journal of Historical Linguistics. DOI: https://doi.org/10.1075/jhl.21025.lee</p> </blockquote>
CLDF dataset derived from Wichmann et al.'s "ASJP Database" v21 from 2025
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wichmann, Søren, Eric W. Holman, Cecil H. Brown, Matthew S. Dryer, and Qibin Ran (eds.). 2025. The ASJP Database (version 21).</p> </blockquote>
Dataset of publication "Derivation and validation of a reference data-based real gas model for hydrogen"
<p>In this repository, a new real gas model for hydrogen based on the Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) v10.0 is provided for the use in the simulation software OpenFOAM v2012. The model is valid in a temperature and pressure range of 150-400 K and 0.1-1000 bar, respectively. Usage beyond this range is not recommended as it may lead to unrealistic results.</p>
Phlorest phylogeny derived from Walker & Ribeiro 2011 'Bayesian phylogeography of the Arawak expansion in lowland South America'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Walker, R. S., & Ribeiro, L. A. (2011). Bayesian phylogeography of the Arawak expansion in lowland South America. Proceedings of the Royal Society B: Biological Sciences, 278(1718), 2562–2567.</p> </blockquote>
Phlorest phylogeny derived from Sagart et al. 2019 'Dated language phylogenies shed light on the ancestry of Sino-Tibetan'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sagart L, Jacques G, Lai Y, Ryder RJ, Thouzeau V, Greenhill SJ, List J- M. 2019 Dated language phylogenies shed light on the ancestry of Sino-Tibetan. Proceedings of the National Academy of Sciences, 201817972.</p> </blockquote>
Phlorest phylogeny derived from Zhang et al 2019 'Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhang M, Yan S, Pan W, & Jin L. 2019. Phylogenetic evidence for Sino-Tibetan origin in northern China in the Late Neolithic. Nature, 569, 112–115.</p> </blockquote>
Phlorest phylogeny derived from Lee & Hasegawa 2013 'Evolution of the Ainu Language in Space and Time'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2013) Evolution of the Ainu Language in Space and Time. PLoS ONE 8(4): e62243. doi: 10.1371/journal.pone.0062243</p> </blockquote>
Phlorest phylogeny derived from Lee 2015 'A Sketch of Language History in the Korean Peninsula'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S (2015) A Sketch of Language History in the Korean Peninsula. PLoS ONE 10(5): e0128448. doi:10.1371/journal.pone.0128448</p> </blockquote>
Phlorest phylogeny derived from Kolipakam et al. 2018 'A Bayesian phylogenetic study of the Dravidian language family'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kolipakam V, Jordan FM, Dunn M, Greenhill SJ, Bouckaert R, Gray RD & Verkerk A. 2018. A Bayesian phylogenetic study of the Dravidian language family. R. Soc. Open Sci. 5: 171504.</p> </blockquote>
Phlorest phylogeny derived from Gray et al. 2009 'Language phylogenies reveal expansion pulses and pauses in Pacific settlement'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Gray RD, Drummond AJ, & Greenhill SJ 2009. Language phylogenies reveal expansion pulses and pauses in Pacific settlement. Science, 323(5913), 479-483.</p> </blockquote>
Phlorest phylogeny derived from Chang et al. 2015 'Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chang W, Cathcart C, Hall D, & Garrett A. 2015. Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis. Language, 91(1):194-244.</p> </blockquote>
Phlorest phylogeny derived from Bowern & Atkinson 2012 'Computational phylogenetics and the internal structure of Pama-Nyungan'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bowern C & Atkinson QD. 2012. Computational phylogenetics and the internal structure of Pama-Nyungan. Language, 88(4), 817-845.</p> </blockquote>
Phlorest phylogeny derived from Bouckaert et al. 2012 'Mapping the Origins and Expansion of the Indo-European Language Family'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bouckaert RR, Lemey P, Dunn M, Greenhill SJ, Alekseyenko AV, Drummond AJ, Gray RD, Suchard MA & Atkinson QD. 2012. Mapping the Origins and Expansion of the Indo-European Language Family. Science, 337(6097), 957-960.</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.