Please refer to the README for guidance on how to use this data.

See Condit (1998).

This dataset is about: A global dataset of crowdsourced land cover and land use reference data (2011-2012). Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.869682 for more information.

This data set contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain, and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea level pressure stations, and 1800 station pressure stations. Each station has at least 10 years of data, 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara Desert, Greenland, and Antarctica.

This MATLAB code is part of the study titled "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients", which has been accepted for publication in the Journal of Imaging (MDPI). The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:

Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. Journal of Imaging. 2024; 10(10):245. https://doi.org/10.3390/jimaging10100245

The datsets use in this work are:

Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1
Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1
Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1

The marsh fritillary (Euphydryas aurinia) is a critically endangered butterfly species in Denmark known to be particularly vulnerable to habitat fragmentation due to its poor dispersal capacity. We identified and genotyped 318 novel SNP loci across 273 individuals obtained from 10 small and fragmented populations in Denmark using a genotyping-by-sequencing (GBS) approach to investigate its population genetic structure. Our results showed clear genetic substructuring and highly significant population differentiation based on genetic divergence (F_ST) among the 10 populations. The populations clustered in three overall clusters and due to further substructuring among these, it was possible to clearly distinguish six clusters in total. We found highly significant deviations from Hardy-Weinberg equilibrium due to heterozygote deficiency within every population investigated which indicates substructuring and/or inbreeding (due to mating among closely related individuals). The stringent filtering procedure that we have applied to our genotype quality could have overestimated the heterozygote deficiency and the degree of substructuring of our clusters but is allowing relative comparisons of the genetic parameters among clusters. Genetic divergence increased significantly with geographic distance, suggesting limited gene flow at spatial scales comparable to the dispersal distance of individual butterflies and strong isolation by distance. Altogether, our results clearly indicate that the marsh fritillary populations are genetically isolated. Further, our results highlight that the relevant spatial scale for conservation of rare, low mobile species may be smaller than previously anticipated.

Abstract:will be uploaded later

Soil properties, processing rates, and water chemistry data. This dataset is associated with the following publication: Forshay, K., J. Weitzman, J. Wilhelm, J. Hartranft, D. Merritts, M. Rahnis, R. Walter, and P. Mayer. Unearthing a stream-wetland floodplain system: increased denitrification and nitrate retention at a legacy sediment removal restoration site, Big Spring Run, PA, USA. BIOGEOCHEMISTRY. Springer, New York, NY, USA, (161): 171-191, (2022).

Taxonomic lookup table containing clade-level mappings for 15,363 genera of Spermatophyta.Spermatophyta_Genera.csvGlobal Woodiness DatabaseGlobalWoodinessDatabase.csvPhylogenetic ResourcesThis archive contains datasets and resulting trees for maximum likelihood phylogeny reconstruction and time-scaling.PhylogeneticResources.zipGlobal Plant Species Freezing Exposure DatabaseThis collection of files documents the processing of the Global Biodiversity Information Facility (GBIF) geographic data and the WorldClim Bioclim data to produce a species freezing exposure datafile which is also included.climate.zipGlobal Leaf Phenology DatabaseGlobalLeafPhenologyDatabase.csv

Hardware: Autoanalyser "QuAAtro" (Seal Analytics) / Autoanalyser Evolution III (Alliance)

This file contains the complete catalog of datasets and publications reviewed in: Di Mauro A., Cominola A., Castelletti A., Di Nardo A.. Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water 2021.The complete catalog contains:

92 state-of-the-art water demand datasets identified at the district, household, and end use scales;

120 related peer-reviewed publications;

57 additional datasets with electricity demand data at the end use and household scales.

The following metadata are reported, for each dataset:

Authors

Year

Location

Dataset Size

Time Series Length

Time Sampling Resolution

Access Policy.

The following metadata are reported, for each publication:

Authors

Year

Journal

Title

Spatial Scale

Type of Study: Survey (S) / Dataset (D)

Domain: Water (W)/Electricity (E)

Time Sampling Resolution

Access Policy

Dataset Size

Time Series Length

Location

Authors: Anna Di Mauro - Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | anna.dimauro@unicampania.it; Andrea Cominola - Chair of Smart Water Networks | Technische Universität Berlin - Einstein Center Digital Future (Germany) | andrea.cominola@tu-berlin.de; Andrea Castelletti - Department of Electronics, Information and Bioengineering | Politecnico di Milano (Italy) | andrea.castelletti@polimi.it Armando Di Nardo -Department of Engineering | Università degli studi della Campania Luigi Vanvitelli (Italy) | armando.dinardo@unicampania.it

Citation and reference:

If you use this database, please consider citing our paper

Di Mauro, A., Cominola, A., Castelletti, A., & Di Nardo, A. (2021). Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13(1), 36, https://doi.org/10.3390/w13010036

Updates and Contributions:

The catalogue stored in this public repository can be collaboratively updated as more datasets become available. The authors will periodically update it to a new version.

New requests can be submitted to the authors, so that the dataset collection can be improved by different contributors. Contributors will be cited, step by step, in the updated versions of the dataset catalogue.

Updates history:

March 1st, 2021 - Pacheco, C.J.B., Horsburgh, J.S., Tracy, J.R. (Utah State University, Logan, UT - USA) --- The dataset associated with paper Bastidas Pacheco, C.J.; Horsburgh, J.S.; Tracy, R.J.. A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters. Sensors 2020, 20, 3655. is published in the HydroShare repository, where it is available as an OPEN dataset. Data can be found here: https://doi.org/10.4211/hs.4de42db6485f47b290bd9e17b017bb51

Data and software repository from CalTech.

This is a database of pile load test information that has been built as part of the Engineering and Physical Sciences Research Council (EPSRC) funded project EP/P020933/1: Databases to INterrogate Geotechnical Observations (DINGO) which ran between 1 July 2017 and 9 June 2019. The database is populated with data digitised from the literature as well as datasets supplied by contributors from the geotechnical engineering industry in the United Kingdom. Contributors have agreed in writing for their data to be shared via the DINGO Database and are cited as personal communication. v1.1 is a minor revision of v1.0 with some error corrections. v1.0 can be found at https://doi.org/10.5523/bris.3r14qbdhv648b2p83gjqby2fl8. N.b. these data have been superseded by The DINGO Database, v1.2 (https://doi.org/10.5523/bris.1jraem68g7ara21p2oi6hv4z22).

Dataset from the following publication:

Wagner, I., Wolf, C., & Schütz, A.C. (in press). Motor learning by selection in visual working memory. Scientific Reports.

For every experiment, there is a zip folder with the underlying data. Column descriptions can be found in pdf files. More informations can be found in the README.txt files in every folder.

For further questions, please contact:
ilja.wagner[at]uni-marburg.de or a.schuetz[at]uni.marburg.de

Collection contains open and publicly funded data sets created by Brown University faculty and student researchers. Increasingly, publishers, and funders are requiring that protocols, data sets, metadata, and code underlying published research be retained and preserved, their locations cited within publications, and shared with other researchers and the public. The deposits here endeavor to be in line with FAIR Principles (Findable, Accessible, Interoperable, Reusable). If you would like to deposit data set into this collection for the purposes of citation/linking within publication and public dissemination, then please log in, zip up and upload your file, and request digital object identifier (DOI) for your data citation.

The 5 kilometer (km) Arctic Ocean Tidal Inverse Model developed in 2004 (AOTIM5) is a barotropic tide model on a polar stereographic grid. AOTIM5 was created using the OSU Tidal Inversion Software (OTIS) package (https://www.tpxo.net/). Model development is described by Padman and Erofeeva (2004) (https://doi.org/10.1029/2003GL019003). The bathymetry grid is based on the original International Bathymetric Chart of the Arctic Ocean (IBCAO) bathymetry (Jakobsson et al., 2000; https://doi.org/10.1029/00EO00059). AOTIM5 consists of grids of sea surface height and depth-integrated currents (“volume transports”) for each of 8 tidal constituents; 4 semidiurnal (M2, S2, K2, N2) and 4 diurnal (K1, O1, P1, Q1). The first step in building AOTIM5 was development of the Arctic Ocean Dynamics-based Tide Model (AODTM5), which is also available at the Arctic Data Center (https://arcticdata.io/catalog/view/doi:10.18739/A2901ZG3N). That model was forced at open ocean boundaries by the TOPEX/Poseidon global barotropic tidal solution version 6.2 (TPXO.6.2), and by local astronomical forcing (“potential tides”). Each constituent in AODTM5 was tuned, separately, to Arctic tide height data by optimizing the linear drag coefficient. AOTIM5 used AODTM5 as a “prior” model, then assimilated coastal and benthic tide gauges, and TOPEX/Poseidon and ERS satellite radar altimetry, to improve the 4 largest-amplitude constituents, M2, S2, K1 and O1. An updated version of this model created in 2018, Arc5km2018, is also available at the Arctic Data Center. The newer model uses updated open boundary conditions, and a wider range of assimilated data including much longer satellite altimetry records. We recommend that users compare results from AOTIM5 and Arc5km2018 before deciding which to use for a specific application. Please also check the ESR Polar Tide Model webpage (https://www.esr.org/research/polar-tide-models/) for more recent Arctic barotropic tide models. Padman, L., and S. Erofeeva (2004), A barotropic inverse tidal model for the Arctic Ocean, Geophysical Research Letters, 31(2), L02303, doi:10.1029/2003GL019003.

This is the demonstration code for the "Compact Morphology-based Nodule Delineation" (CoMoNoD) algorithm. CoMoNoD is a rapid method to delineate poly-metallic (or manganese) nodules from vertical benthic images. The paper describing the algorithm is currently under review. This algorithm makes extensive use of the OpenCV library for image processing and uses NVIDIA CUDA for computational speedup.

This dataset, collected with the Unmanned Aircraft Systems (UAS) Chromatograph for Atmospheric Trace Species (UCATS), provides atmospheric concentrations of nitrous oxide (N2O), sulfur hexafluoride (SF6), methane (CH4), hydrogen (H2), carbon monoxide (CO), water vapor (H2O), and ozone (O3). The UCATS system is three different instruments in one enclosure: a two-channel chromatograph with electron capture detectors (one measures N2O and SF6, the other measures CH4, H2 and CO), a tunable diode laser instrument for H2O, and a dual-beam O3 photometer.

The Indian Precipitation Ensemble Dataset (IPED) is the first observation-based ensemble gridded precipitation dataset for India. It includes the mean and standard deviation of 30 ensembles daily from 1991 to 2023 at a resolution of 0.1°.

The dataset contains two folders:

1. IPED Ensemble's Mean
2. IPED Ensemble's Standard Deviation

For detailed information about this dataset and its development, please refer to the original research article published in the Scientific Data:

Peringiyil, A., Saharia, M., O. P., S. et al. A station-based 0.1-degree daily gridded ensemble precipitation dataset for India. Sci Data 12, 333 (2025). https://doi.org/10.1038/s41597-025-04474-2

Disclaimer

When using the IPED dataset, users must cite it along with the associated research article published in "Scientific Data".

To Be Cited:

1. Peringiyil, A., Saharia, M., O. P., S. et al. A station-based 0.1-degree daily gridded ensemble precipitation dataset for India. Sci Data 12, 333 (2025). https://doi.org/10.1038/s41597-025-04474-2
2. Anagha P, & Manabendra Saharia. (2025). Indian Precipitation Ensemble Dataset (IPED) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8199138

P-V-T data of the four major mantle minerals, olivine, wadselyite, ringwoodite, and bridgmanite

The original data are as follows:

Olivine: https://doi.org/10.1016/j.pepi.2008.08.002

Wadsleyite: https://doi.org/10.1029/2009GL038107

Ringwoodite: https://doi.org/10.1029/2004JB003094

Bridgmanite; https://doi.org/10.1029/2009GL039318 https://doi.org/10.1029/2011JB008988

The temperatures were recalculated using https://doi.org/10.1016/j.pepi.2019.106348

The pressures were recalculated using https://doi.org/10.1029/2011JB008988