cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15485/2482525
title: CROCUS Weather Data at Chicago State University Prairie Site

description: Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at Chicago State University (CSU) at a prarie site on campus in Chicago, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (CSU), data level (raw, a1), date (year, month, day), and hour (0000).
DOI: 10.15485/2338027
title: CROCUS Weather Data at Northeastern Illinois University Rooftop

description: This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.
DOI: 10.15485/2482530
title: CROCUS Weather Data at University of Illinois - Chicago Tower

description: Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the University of Illinois in Chicago, Illinois, on the meteorological tower near the greenhouse on campus. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html.  File naming convention includes the project (CROCUS), location (UIC), data level (raw, a1), date (year, month, day), and hour (0000).
DOI: 10.15485/2006549
title: Data and scripts associated with the manuscript evaluating the hydrologic responses of the Pacific Northwest watersheds to wildfires (v2)

description: This data package is associated with the publication “Evaluating Post-fire Watershed Response to Varying Burn Severity and Precipitation Regimes Using Fully-distributed and Integrated Hydrologic Models” submitted to Journal of Hydrology (Li et al. 2025). In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated watershed model that couples surface flow, subsurface flow, and canopy biophysical processes, to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity.The data package contains the required input data (meteorological forcing, Leaf Area Index, wildfire burn severities, etc.) to run the model, configuration files, the Jupyter notebooks in Python to pre-process and post-process data, the figures in the manuscript, and the modeling output files. The variables include watershed-averaged evapotranspiration, watershed-averaged surface/subsurface/canopy water content, and river discharge at watershed outlet.The data package contains a file-level metadata that lists and describes all the files contained in the data package (ATS_flmd.csv), a data dictionary file that defines columns headers across all csv files contained in the data package (ATS_dd.csv), a data package level readme file (the current file), and four zipped folders.The ‘data’ folder provides data needed to run the model in .h5, .i2s, .xyz, .shp, and .exo formats. The sub-folders are for each data types. The ‘model’ folder provides input files (.xml format) and essential model outputs. Each sub-folder provides the files from each simulated watershed. The ‘notebooks’ folder provides the Jupyter notebooks (.ipynb format) for pre- and post- processing model files, and for producing the figures in the manuscript. The ‘figures’ folder provides the figures associated with manuscript in .pdf and .png formats.The ‘model’ folder and the ‘data’ folder have been split into 5GB-large pieces using the Linux command ‘split -b 5120m model.zip model.zip.’ and ‘split -b 5120m data.zip data.zip.’, respectively. They can be merged back using the Linux command ‘cat model.zip.* > model.zip’  and ‘cat data.zip.* > data.zip’, respectively.
DOI: 10.15485/2571202
title: CROCUS Sodar Measurements of Lower Atmospheric Wind Profiles at Argonne Testbed for Multiscale Observational Science (ATMOS) Site

description: The Scintec MFAS Sodar (Multiple-Frequency Acoustic Sounder) is an autonomous, ground-based acoustic remote sensing system designed to measure vertical profiles of horizontal wind speed, wind direction, and vertical velocity in the lower atmosphere. The instrument transmits sequences of acoustic pulses and detects the Doppler-shifted sound waves backscattered by small-scale temperature and velocity fluctuations caused by atmospheric turbulence. From these Doppler shifts, the system derives three-dimensional wind vectors by combining radial velocities from multiple beam orientations.The MFAS Sodar operates with a first usable range gate beginning at approximately 30 m above ground level and a configurable vertical resolution of 10 m. Under favorable acoustic conditions, the system provides wind profiles extending up to 600 m above ground level. Measurements are processed into 15-minute averaged profiles containing wind speed, direction, vertical velocity, and diagnostic quantities such as signal-to-noise ratio and echo strength.This dataset was collected at the Argonne Testbed for Multiscale Observational Science (ATMOS) facility in Lemont, Illinois, as part of DOE's CROCUS Urban Integrated Field Laboratory (UIFL) initiative. The purpose of these observations is to characterize the vertical wind structure and boundary-layer evolution across the urban–suburban gradient of the greater Chicago region. In particular, these data are intended to improve understanding of how local meteorology, such as lake-breeze penetration, nocturnal jets, and daytime mixing, varies between the densely built urban core and the suburban periphery. The MFAS observations provide critical context for evaluating high-resolution model simulations and for integrating with complementary lidar, radar, and in-situ meteorological measurements within the broader CROCUS UIFL network.All data are archived in NetCDF (Network Common Data Form) format and include wind and diagnostic parameters. The files can be accessed and analyzed using standard software that supports NetCDF, such as Python (e.g., xarray, netCDF4), MATLAB, R (e.g., ncdf4, raster), or Panoply (NASA’s NetCDF visualization application).
DOI: 10.15485/2482528
title: CROCUS Weather Data at Argonne National Laboratory Prairie Site

description: Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).
DOI: 10.15468/dl.yzmtgj
title: Occurrence Download

description: A dataset containing 117807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stelgidopteryx ruficollis",
 "Country is Colombia"
 ]
}

 The dataset includes 117807 records from 317 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045494-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5522/04/30136174
title: Data repository for paper: <b>Locomotion selectively enhances visual speed encoding in mouse medial higher visual areas</b>

description: Data for the paper, "Locomotion selectively enhances visual speed encoding in mouse medial higher visual areas". These data include electrophysiology recordings from different mouse visual areas collected using multiple NeuroPixel high-density electrodes. Data were collected in awake head-restrained mice viewing visual stimuli and able to run over a treadmill. Data uploaded here is processed data from the Allen Institute dataset, and the orginal dataset was published here: Siegle, J. H. <i>et al.</i> Survey of spiking in the mouse visual system reveals functional hierarchy. <i>Nature </i><b>592</b>, 86–92 (2021).Allen Institute for Brain Science. The Allen Brain Observatory Visual Coding Neuropixels Dataset. (2019). Available from: https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels<br>
DOI: 10.5522/04/30136174.v1
title: Data repository for paper: <b>Locomotion selectively enhances visual speed encoding in mouse medial higher visual areas</b>

description: Data for the paper, "Locomotion selectively enhances visual speed encoding in mouse medial higher visual areas". These data include electrophysiology recordings from different mouse visual areas collected using multiple NeuroPixel high-density electrodes. Data were collected in awake head-restrained mice viewing visual stimuli and able to run over a treadmill. Data uploaded here is processed data from the Allen Institute dataset, and the orginal dataset was published here: Siegle, J. H. <i>et al.</i> Survey of spiking in the mouse visual system reveals functional hierarchy. <i>Nature </i><b>592</b>, 86–92 (2021).Allen Institute for Brain Science. The Allen Brain Observatory Visual Coding Neuropixels Dataset. (2019). Available from: https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels<br>
DOI: 10.15485/2589885
title: Remote sensing images, DEM, and point clouds associated with “Accuracy Evaluation of Cost-Effective 3D Reconstruction Approaches for Non-Perennial Stream Riverbeds”

description: This data package is associated with the publication “Accuracy Evaluation of Cost-Effective 3D Reconstruction Approaches for Non-Perennial Stream Riverbeds” submitted to GIScience & Remote Sensing. The data package includes the drone photos for a section of Umtanum Creek in Washington, Unted States. The photos were used to reconstruct the 3-dimensional (3D) digital elevation model (DEM) of the riverbed for the investigated stream section. The reconstruction results by four approaches are provided in this data package: (1) UAV imagery-based Structure-from-Motion (SfM), (2) a machine learning-based 3D reconstruction model, Visual Geometry Grounded Deep Structure from Motion (VGGSfM), (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The ground truth measurements by tripod-mounted optical level kit and ground control points GPS locations for evaluating the accuracy of the four reconstruction approaches are also provided in this data package. This dataset is comprised of 8 folders, the detailed flight configuration html files, and field metadata. The folders “2024_10_18_d01” and “2024_10_18_d02” contain the original drone photos for the two drone flights (d01 and d02) on October 18, 2024.  The reconstruction results from each of the approaches are in the folders called “ODM_SfM”, “VGGSfM”, “VGGTLong”, and  “LiDAR”. The ground truth measurements are in folder the folder called “optical_level_kit”. Lastly, results comparing the different approaches  are in the folder called “comparisons”. All files are .csv, .html, .jpg, .obj, .txt, and .npy.  See the readme files for information on using the .obj and .npy files. NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata.
DOI: 10.15485/2341583
title: Modeling the Effects of Artificial Drainage on Agriculture-dominated Watersheds using a Fully Distributed Integrated Hydrology Model: Datasets, scripts, model files

description: This model-data archive supports the research paper that demonstrates the integration of agricultural drainage features—specifically, narrow engineered ditches and tile drains—into a fully distributed, basin-scale integrated surface-subsurface hydrology model (ISSHM), Amanzi-ATS. The model employs innovative computational meshes aligned with agricultural ditches and incorporates the physically based Hooghoudt's drainage equation to simulate tile drainage, offering a novel strategy that enhances the accuracy of hydrological simulations.The archived dataset includes input parameters, model configurations, and select simulation outputs for the Amanzi-ATS model that successfully captured the streamflow patterns in the Portage River Watershed as validated by USGS gauge readings. Jupyter notebook for the preparation of model inputs and post-processing of outputs are also included. The model's predictive performance achieved a normalized Kling-Gupta Efficiency (KGE) of 0.81, surpassing SWAT without the necessity for site-specific calibration.The Amanzi-ATS model presented in this modeL-data archive allows for numerical experiments to explore the shifts in the flow structure under different drainage scenarios. As a tool for advancing the understanding of distributed hydrological responses and nutrient cycling, this archived model provides valuable insights for researchers, modelers, and decision-makers involved in watershed management and environmental modeling.The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model, are in XML format and can be edited in any commonly used text editors.
DOI: 10.21950/lrtzph
title: Data on the impact of student choice on their attitudes and perceptions of bilingual education.

description: This data set contains three files and a descriptive readme.txt. 
For this study data was gathered from high school students in various regions of Spain on the optional/compulsory nature of bilingual programmes in secondary education. The aim of this work was to investigate Spanish bilingual secondary school students’ attitudes, motivation, perceived learning, anxiety, and satisfaction with bilingual education, establishing a comparison between schools where students can only join the bilingual stream and schools where students can choose whether to join the bilingual stream or the non-bilingual one.
One Excel file contains the data gathered. The other one contains the variables.
The PDF contains the survey that was distributed to students online.
DOI: 10.7910/dvn/nhz7co
title: Integrated Assessment of Flood Risk and Human-Flood Interactions in Assam Using the Coupled Coordination Degree Model

description: This dataset compiles indicators related to flood hazard, exposure, vulnerability, restorability, and socioeconomic development in Assam, and is used to construct the Flood Risk Index (FRI) and the Socioeconomic Development Index (SEDI). It supports the evaluation of the coupled coordination between flood risk and development using the Coupled Coordination Degree Model (CCDM). The dataset provides an integrated basis for analyzing how environmental pressures and socioeconomic conditions interact across the state, offering essential inputs for understanding regional resilience and guiding sustainable flood management and development planning.
DOI: 10.15468/dl.s9vyeh
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 1000.0-1000000.0",
 "Geometry POLYGON((-130.62691 12.01167,-46.54521 12.01167,-46.54521 54.57476,-130.62691 54.57476,-130.62691 12.01167))",
 "TaxonKey is Humulus scandens"
 ]
}

 The dataset includes 347 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045570-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f8deku
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-79.88363 -3.07964,-79.89707 -3.09808,-79.90546 -3.13832,-79.9105 -3.16347,-79.95415 -3.18861,-79.9978 -3.22549,-80.02802 -3.20873,-80.06496 -3.27578,-80.10358 -3.32103,-80.19088 -3.33779,-80.23957 -3.33947,-80.28322 -3.34282,-80.30337 -3.39646,-80.28826 -3.42998,-80.24964 -3.435,-80.21774 -3.43835,-80.24461 -3.45847,-80.23285 -3.49031,-80.21103 -3.52047,-80.21271 -3.56739,-80.18752 -3.61096,-80.19592 -3.67463,-80.18584 -3.75002,-80.15898 -3.86896,-80.1254 -3.88572,-80.00003 -4.20767,-79.80282 -4.21831,-79.64292 -4.17046,-79.4617 -4.01097,-79.39241 -4.01629,-79.37495 -3.81959,-79.30787 -3.42276,-79.30787 -3.37237,-79.35835 -3.34148,-79.48374 -3.33173,-79.67589 -3.30897,-79.66775 -3.24231,-79.63355 -3.14964,-79.88363 -3.07964))",
 "HasGeospatialIssue is false",
 "TaxonKey is Blattodea"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045571-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15485/2482523
title: CROCUS Air Quality Data at Argonne National Laboratory Prairie Site

description: The AQT (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (NO, NO2, O3, CO), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html.  Each file contains one day's worth of data (24 hours, starting at 0000 UTC). The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).
DOI: 10.15485/2482533
title: CROCUS Air Quality Data at University of Illinois - Chicago Tower

description: The AQT (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (NO, NO2, O3, CO), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments. These measurements are collected at the University of Illinois in Chicago, Illinois, on the meteorological tower near the greenhouse on campus. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (UIC), data level (raw, a1), date (year, month, day), and hour (0000).