cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15477380
title: Final UC1 end-to-end  measurements of power consumption, resource usage, latency and performance tests performed for the XGain European project

description: This dataset contains resource usage, power consumption, latency and network performance metrics from experiments conducted on a 5G private network deployed in CoEbreLab 5G in Mora d'Ebre, Spain on the context of the XGain european project. The tests involved streaming and processing H264 video with "Motcam", a component that performs real-time object recognition of people and vehicles. On this deployment, the video was transmitted from the edge computing node; a raspberry-pi 4 model b, streamed through 5G using an amarisoft-based indoor cell Cablefree; model emerald 5G and a coms365 5G CPE, to finally be processed on the Motcam component which was hosted on the compute, hosted on an Nvidia Orin.
DOI: 10.5281/zenodo.15477381
title: Final UC1 end-to-end  measurements of power consumption, resource usage, latency and performance tests performed for the XGain European project

description: This dataset contains resource usage, power consumption, latency and network performance metrics from experiments conducted on a 5G private network deployed in CoEbreLab 5G in Mora d'Ebre, Spain on the context of the XGain european project. The tests involved streaming and processing H264 video with "Motcam", a component that performs real-time object recognition of people and vehicles. On this deployment, the video was transmitted from the edge computing node; a raspberry-pi 4 model b, streamed through 5G using an amarisoft-based indoor cell Cablefree; model emerald 5G and a coms365 5G CPE, to finally be processed on the Motcam component which was hosted on the compute, hosted on an Nvidia Orin.
DOI: 10.5281/zenodo.15638528
title: Paper with Phillipines
DOI: 10.5281/zenodo.15638529
title: Paper with Phillipines
DOI: 10.15468/dl.dz8bf9
title: Occurrence Download

description: A dataset containing 1384474 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Larus glaucescens J.F.Naumann, 1840"
 ]
}

 The dataset includes 1384474 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035662-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15584243
title: Input and output data thesis - part 1

description: This dataset provides input data for generation expansion planning using stochastic programming, including uncertainty in hourly demand and generation availability. It contains:





Input data for the distribution case study, designed for specific test scenarios.




Input data for the European case study, based on ENTSO-E published data.




Output files from experiments on the two-dimensional, distribution, and European case studies.
DOI: 10.5281/zenodo.15584244
title: Input and output data thesis - part 1

description: This dataset provides input data for generation expansion planning using stochastic programming, including uncertainty in hourly demand and generation availability. It contains:





Input data for the distribution case study, designed for specific test scenarios.




Input data for the European case study, based on ENTSO-E published data.




Output files from experiments on the two-dimensional, distribution, and European case studies.
DOI: 10.34810/data2322
title: CatCrops_identification: A Python Project for Early Crop Type Classification Using Remote Sensing and Ancillary Data

description: CatCrops_identification is a Python library developed for the early classification of crop types using remote sensing data (Sentinel-2) and ancillary information. It is based on a Transformer model adapted for the analysis of spectral time series with variable length, and it allows the integration of auxiliary data such as the previous year’s crop, irrigation system, cloud cover, elevation, and other geographic features.

The library provides tools to download and prepare datasets, train deep learning models, and generate vector maps with plot-level classification. CatCrops_identification includes scripts to automate the entire workflow and offers a public dataset that combines declared and inspected information on crop types in the Lleida region.

This approach improves classification accuracy in the early stages of the agricultural season, offering a robust and efficient tool for agricultural planning and water resource management.
description: Python, 3.8.18
DOI: 10.15468/dl.dvgkwd
title: Occurrence Download

description: A dataset containing 357 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Parus major bokharensis Lichtenstein, 1823"
 ]
}

 The dataset includes 357 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035669-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r92b9d
title: Occurrence Download

description: A dataset containing 12295 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Asia, Europe)",
 "TaxonKey is Parus major major"
 ]
}

 The dataset includes 12295 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035671-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14646062
title: BEAM-based model results at full extent

description: This dataset provides detailed building-level exposure data based on volumetric disaggregation of net asset values. It includes classifications by economic sector and asset types for over 60,000 buildings, offering a comprehensive view of flood exposure in county "Ahrweiler" in western Germany.
DOI: 10.5281/zenodo.14725303
title: BEAM-based model results at full extent

description: This dataset provides detailed building-level exposure data based on volumetric disaggregation of net asset values. It includes classifications by economic sector and asset types for over 60,000 buildings, offering a comprehensive view of flood exposure in county "Ahrweiler" in western Germany.
DOI: 10.5281/zenodo.14725321
title: BEAM-based model results at full extent

description: This dataset provides detailed building-level exposure data based on volumetric disaggregation of net asset values. It includes classifications by economic sector and asset types for over 60,000 buildings, offering a comprehensive view of flood exposure in county "Ahrweiler" in western Germany.
DOI: 10.5281/zenodo.15638551
title: BEAM-based model results at full extent

description: This dataset provides detailed building-level exposure data based on volumetric disaggregation of net asset values. It includes classifications by economic sector and asset types for over 60,000 buildings, offering a comprehensive view of flood exposure in county "Ahrweiler" in western Germany.
DOI: 10.15468/dl.xmecuf
title: Occurrence Download

description: A dataset containing 605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-8.90559 35.34297,51.90559 35.34297,51.90559 59.65703,-8.90559 59.65703,-8.90559 35.34297))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Neomys milleri Mottaz, 1907"
 ]
}

 The dataset includes 605 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035672-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57707/fmi-b2share.97baff28a7bd49459d6e5014e0279522
title: Welgegund multiwavelength aerosol absorption data

description: Data from aethalometer AE-31 at Welgegund, South Africa in 2018. 

Data is corrected according to Collaud Coen et al., Atmos. Meas. Tech., 2010, using Cref = 4.3 after comparison with MAAP at Welgegund. 

Data is in prevailing conditions. 
Inlet is R&P PM10 and aerosol has been dried with a nafion dryer to RH &lt; 40 %.
Data filtered based on visual inspection of the data.

Time is in UTC+2 (time stamp is center of averaging period).
1 h average values are averaged (mean) from 2 minute data. Each 1 h average is based on at 
least 40 minutes of measurements (66.7% data coverage required).

Missing or bad data: -999.

Columns

1 : year
2 : month
3 : day
4 : hour
5 : minute
6 : second
7 : absorption 370 nm [Mm^-1]
8 : absorption 470 nm [Mm^-1]
9 : absorption 520 nm [Mm^-1]
10 : absorption 590 nm [Mm^-1]
11 : absorption 660 nm [Mm^-1]
12 : absorption 880 nm [Mm^-1]
13 : absorption 950 nm [Mm^-1]
DOI: 10.34740/kaggle/dsv/12128117
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.dfk766
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dopasia harti (Boulenger, 1899)"
 ]
}

 The dataset includes 69 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035673-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14466/cefasdatahub.180
title: Baited remote underwater video still images from Bridlington Bay 2021 to 2022

description: A BRUV (Baited Remote Underwater Video) data set comprising still images from two cameras mounted within traps ("parlour type") set to catch lobster (Hommarus gammarus) within Bridlington bay from 2021 to 2022. The objective was to use still images to record presence and abundance of H. gammarus, both within and without the trap.

Still images were collected using a system of two time lapse cameras mounted within parlour pots ("creels") used by the Holderness Lobster fishery. One camera faced internally, the other was external facing. The cameras were set to collect imagery (single still every minute) for approximately 24 hours, using infrared lights to illuminate at night.

The pots (with cameras mounted) were then deployed by Holderness Fishery Industry Group (HFIG) scientists.
DOI: 10.5281/zenodo.15638521
title: Arabian hydroclimate and human population dynamics since the middle Pleistocene

description: The Arabian Peninsula, a pivotal Africa-Eurasia land bridge, is crucial for understanding the human evolution and global dispersal.   Orbital-scale increases in monsoonal precipitation periodically transformed the peninsula into widespread savannahs—termed “Green Arabian” periods (GAPs), which facilitated recurrent human occupation.   GAPs fundamentally modulated Pleistocene population connectivity and demography between continents.   Despite analysis of extensive archaeological remains from the Arabian Peninsula over the last decade, the regional environmental context of human dispersals remains poorly understood due to the absence of continuous paleoenvironmental records from hominin habitats, especially for the Pleistocene. Fortunately, an alternative to terrestrial sediments is marine dust record emanated from the Arabian Peninsula, since they are continuous and well-dated and have the potential to trace the aridity shifts.   Here we present multi-proxy and high-resolution Arabian dust records from the western equatorial Indian Ocean spanning the last 500 kyr, to assess orbital-scale hydrological variations as regulators of human populations in Arabia.
DOI: 10.5281/zenodo.15638522
title: Arabian hydroclimate and human population dynamics since the middle Pleistocene

description: The Arabian Peninsula, a pivotal Africa-Eurasia land bridge, is crucial for understanding the human evolution and global dispersal.   Orbital-scale increases in monsoonal precipitation periodically transformed the peninsula into widespread savannahs—termed “Green Arabian” periods (GAPs), which facilitated recurrent human occupation.   GAPs fundamentally modulated Pleistocene population connectivity and demography between continents.   Despite analysis of extensive archaeological remains from the Arabian Peninsula over the last decade, the regional environmental context of human dispersals remains poorly understood due to the absence of continuous paleoenvironmental records from hominin habitats, especially for the Pleistocene. Fortunately, an alternative to terrestrial sediments is marine dust record emanated from the Arabian Peninsula, since they are continuous and well-dated and have the potential to trace the aridity shifts.   Here we present multi-proxy and high-resolution Arabian dust records from the western equatorial Indian Ocean spanning the last 500 kyr, to assess orbital-scale hydrological variations as regulators of human populations in Arabia.
DOI: 10.5281/zenodo.14634962
title: Dataset for the behavioral investigation of allocentric and egocentric spatial memory in humans

description: This data is from a behavioral study with the Garden Game task.
DOI: 10.5281/zenodo.14634961
title: Dataset for the behavioral investigation of allocentric and egocentric spatial memory in humans

description: This data is from a behavioral study with the Garden Game task.
DOI: 10.5281/zenodo.15638550
title: Dataset for the behavioral investigation of allocentric and egocentric spatial memory in humans

description: This data is from a behavioral study with the Garden Game task.
DOI: 10.5281/zenodo.15511857
title: SARS-CoV-2 RBD data along with ESM embeddings + Active learning results

description: Code: https://github.com/m-huot/VIRAL.git

Download the following folders and place them in the root directory of the github repository:





data_bloom/ – contains ESM embeddings and experimental log₁₀(Kd) values from Bloom lab.




data_desai/ – contains ESM embeddings and log₁₀(Kd) values for variant libraries from Desai lab.




script_results/ – stores active-learning outputs, including the variants selected by the model at each acquisition round.



Licence: see github repository

Citation: If you use this toolkit in published work, please cite:

@article{doi:10.1073/pnas.2503742122,author = {Marian Huot  and Dianzhuo Wang  and Jiacheng Liu  and Eugene I. Shakhnovich },title = {Predicting high-fitness viral protein variants with Bayesian active learning and biophysics},journal = {Proceedings of the National Academy of Sciences},volume = {122},number = {24},pages = {e2503742122},year = {2025},doi = {10.1073/pnas.2503742122},URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2503742122},eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2503742122},}
DOI: 10.5281/zenodo.15511858
title: SARS-CoV-2 RBD data along with ESM embeddings + Active learning results

description: Code: https://github.com/m-huot/VIRAL.git

Download the following folders and place them in the root directory of the github repository:





data_bloom/ – contains ESM embeddings and experimental log₁₀(Kd) values from Bloom lab.




data_desai/ – contains ESM embeddings and log₁₀(Kd) values for variant libraries from Desai lab.




script_results/ – stores active-learning outputs, including the variants selected by the model at each acquisition round.



Licence: see github repository

Citation: If you use this toolkit in published work, please cite:

@article{doi:10.1073/pnas.2503742122,author = {Marian Huot  and Dianzhuo Wang  and Jiacheng Liu  and Eugene I. Shakhnovich },title = {Predicting high-fitness viral protein variants with Bayesian active learning and biophysics},journal = {Proceedings of the National Academy of Sciences},volume = {122},number = {24},pages = {e2503742122},year = {2025},doi = {10.1073/pnas.2503742122},URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2503742122},eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2503742122},}
DOI: 10.15468/dl.e9sthk
title: Occurrence Download

description: A dataset containing 27342 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'GH'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 27342 records were derived from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035668-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29289161.v1
title: Analisis Pengetahuan Mahasiswa Ilmu Kesehatan Masyarakat tentang Determinan Derajat Kesehatan Berdasarkan Teori Blum.docx

description: perilakupksjsbhPenelitian ini bertujuan untuk menganalisis pengaruh faktor perilaku, lingkungan, pelayanan kesehatan, dan genetik terhadap derajat kesehatan mahasiswa Ilmu Kesehatan Masyarakat berdasarkan teori H.L. Blum. Metode yang digunakan adalah penelitian kuantitatif dengan pendekatan <i>Structural Equation Modeling–Partial Least Squares</i> (SEM-PLS). Data dikumpulkan dari 79 mahasiswa melalui kuesioner daring dan dianalisis menggunakan <i>SmartPLS</i> 3.0 untuk menguji hubungan antar variabel laten. Hasil penelitian menunjukkan bahwa faktor perilaku dan genetik memiliki pengaruh signifikan terhadap derajat kesehatan mahasiswa, dengan faktor perilaku sebagai variabel dominan. Sebaliknya, faktor lingkungan dan pelayanan kesehatan tidak berkontribusi secara signifikan. Kesimpulan penelitian ini menegaskan pentingnya perubahan perilaku sehat dan peningkatan kesadaran akan faktor genetik dalam meningkatkan derajat kesehatan mahasiswa. Penelitian ini memberikan kontribusi teoritis dengan memperkuat validitas model teori H.L. Blum pada populasi mahasiswa, serta implikasi praktis dalam pengembangan strategi promosi kesehatan berbasis kampus yang fokus pada edukasi perilaku dan penyuluhan risiko genetik. Rekomendasi untuk penelitian selanjutnya adalah penggunaan sampel yang lebih representatif dan pendekatan <i>mixed methods</i> untuk mengeksplorasi pengaruh faktor psikososial, teknologi digital, dan budaya hidup sehat.
DOI: 10.15468/dl.gwtppf
title: Occurrence Download

description: A dataset containing 28764266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Accipiter gentilis (Linnaeus, 1758), Accipiter nisus (Linnaeus, 1758), Acrocephalus arundinaceus (Linnaeus, 1758), Acrocephalus melanopogon (Temminck, 1823), Acrocephalus paludicola (Vieillot, 1817), Acrocephalus schoenobaenus (Linnaeus, 1758), Acrocephalus scirpaceus (Hermann, 1804), Actitis hypoleucos (Linnaeus, 1758), Aegithalos caudatus (Linnaeus, 1758), Aegypius monachus (Linnaeus, 1766), Alauda arvensis Linnaeus, 1758, Alcedo atthis (Linnaeus, 1758), Spatula querquedula (Linnaeus, 1758), Mareca strepera (Linnaeus, 1758), Anser albifrons (Scopoli, 1769), Anser anser (Linnaeus, 1758), Anser fabalis (Latham, 1787), Anthus campestris (Linnaeus, 1758), Anthus pratensis (Linnaeus, 1758), Anthus spinoletta (Linnaeus, 1758), Anthus trivialis (Linnaeus, 1758), Apus apus (Linnaeus, 1758), Apus caffer (M.H.K.Lichtenstein, 1823), Apus pallidus (Shelley, 1870), Aquila adalberti C.L.Brehm, 1861)",
 "Geometry POLYGON ((-14.93513 24.16483, -13.20808 24.16483, -3.139742 29.82368, -2.662047 32.21215, 11.22785 32.39588, 11.81578 33.86571, 45.03395 34.30666, 45.4749 42.97866, 31.95246 42.90517, 31.80548 71.71384, -10.52564 68.25974, -14.93513 24.16483))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 28764266 records from 7861 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035663-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13124727
title: Linked collectors and determiners for: KWP Lepidoptera Collection (Arctos).

description: Natural history specimen data linked to collectors and determiners held within, "KWP Lepidoptera Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a. Formatted as a Frictionless Data package.
DOI: 10.5281/zenodo.10873781
title: Linked collectors and determiners for: KWP Lepidoptera Collection (Arctos).

description: Natural history specimen data linked to collectors and determiners held within, "KWP Lepidoptera Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a. Formatted as a Frictionless Data package.
DOI: 10.5281/zenodo.12612999
title: Linked collectors and determiners for: KWP Lepidoptera Collection (Arctos).

description: Natural history specimen data linked to collectors and determiners held within, "KWP Lepidoptera Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a. Formatted as a Frictionless Data package.
DOI: 10.5281/zenodo.13387601
title: Linked collectors and determiners for: KWP Lepidoptera Collection (Arctos).

description: Natural history specimen data linked to collectors and determiners held within, "KWP Lepidoptera Collection (Arctos)". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/8539cd0e-f762-11e1-a439-00145eb45e9a. Formatted as a Frictionless Data package.