cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14558574
title: mayavchung/enso-disease MERRA-2/SIRS model data

description: Datasets for "Intersecting memories of immunity and climate: Potential multiyear impacts of the El Niño–Southern Oscillation on infectious disease spread"

Maya V. Chung, Gabriel A. Vecchi, Wenchang Yang, Bryan Grenfell, C. Jessica Metcalf

This repository contains input and output data for the MERRA-2/SIRS model with HCoV-HKU1 parameters.

Software to run the model and process the output are available through Zenodo or Github.



MERRA-2 daily specific humidity data:



Used to create 7-day running mean data that is used to force the model in the study. 

1980-2018 climatology: climatology.MERRA2.1980-2018.QV2M.daily.nc

1981-2017 daily data: MERRA2.1981-2017.QV2M.daily.nc



SIRS model inputs:



Input specific humidity data from MERRA-2 derived from specific humidity for HCoV-HKU1 parameters. These specific humidity data are converted to R0 values and used to run the SIRS model.

Climatology for model spin-up: climatology.MERRA2.1980-2018.QV2M.weekly.nc

Timeseries 1981-2017: MERRA2.1981-2017.QV2M.weekly.nc



SIRS model outputs:



Output disease data (R0, infected fraction, susceptible fraction) from the SIRS model.

Repeated climatology version (spin-up): climatology_HKU1_MERRA2_weekly_SIRS_output.nc

1981-2017 version: HKU1_MERRA2_weekly_SIRS_output.nc




These data and additional processed data (which is generated by the software) are available in a tigress repository at Princeton University: https://tigress-web.princeton.edu/~mvchung/ENSO_disease/paper_MERRA2-SIRS-HKU1-data/

Submitted to GeoHealth, 2024

Last updated December 26, 2024 by Maya Chung
DOI: 10.34740/kaggle/dsv/10316479
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.28102862
title: Original data of PV power prediction

description: The dataset includes observed photovoltaic power generation data from relevant photovoltaic power studies, along with meteorological parameters.
DOI: 10.15468/dl.tkpp46
title: Occurrence Download

description: A dataset containing 5197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tapinoma melanocephalum (Fabricius, 1793)"
 ]
}

 The dataset includes 5197 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050157-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rb8wgk
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-118.29011 50.98087,-100.43566 50.98087,-100.43566 58.11463,-118.29011 58.11463,-118.29011 50.98087))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Potamogeton robbinsii Oakes"
 ]
}

 The dataset includes 13 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050158-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dqtayh
title: Occurrence Download

description: A dataset containing 1712 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Technomyrmex albipes (Smith, 1861)"
 ]
}

 The dataset includes 1712 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050161-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a8vskc
title: Occurrence Download

description: A dataset containing 192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is CHN.18.1_1",
 "TaxonKey is Passeriformes",
 "Year 1994-2023"
 ]
}

 The dataset includes 192 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050163-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/3912
title: anime-descriptor
DOI: 10.15468/dl.q8ks7e
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eremurus persicus (Jaub. & Spach) Boiss."
 ]
}

 The dataset includes 84 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050164-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.ok0o2ysl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4 records)
DOI: 10.15468/dl.sancwd
title: Occurrence Download

description: A dataset containing 76421 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is TUR.8_1",
 "TaxonKey is Passeriformes",
 "Year 1994-2023"
 ]
}

 The dataset includes 76421 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050165-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60686/t-fsd3248
title: FSD3248 Finnish Views on Lutheranism 2017
DOI: 10.60686/t-fsd3249
title: FSD3249 Personal Information Management of Students at the University of Tampere 2017
DOI: 10.60686/t-fsd3250
title: FSD3250 Finnish Youth Survey 2017
DOI: 10.60686/t-fsd3251
title: FSD3251 Finnish Municipal Election Study 2017
DOI: 10.15468/dl.dkfhnv
title: Occurrence Download

description: A dataset containing 1134 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Plethobasus cyphyus (Rafinesque, 1820)"
 ]
}

 The dataset includes 1134 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020584-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60686/t-fsd3252
title: FSD3252 Professional Identity and Religious Beliefs of Children's Instructor Students 2014
DOI: 10.60686/t-fsd3253
title: FSD3253 Volunteering in Finland 2018
DOI: 10.60686/t-fsd3254
title: FSD3254 Citizen Safety and Security 2015
DOI: 10.15468/dl.aspdpx
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "MediaType is Image",
 "TaxonKey is Macromia splendens (Pictet, 1843)"
 ]
}

 The dataset includes 21 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0167964-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60686/t-fsd3255
title: FSD3255 Generational Transmissions in Finland, Baby Boomers 2012
DOI: 10.60686/t-fsd3256
title: FSD3256 Generational Transmissions in Finland, Baby Boomers' Adult Children 2012
DOI: 10.60686/t-fsd3257
title: FSD3257 Gelotophobia, Burnout and Workplace Bullying 2007
DOI: 10.15468/dl.55g36g
title: Occurrence Download

description: A dataset containing 487983 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Carduus L."
 ]
}

 The dataset includes 487983 records from 2521 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048530-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9xchd
title: Occurrence Download

description: A dataset containing 270676 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.27257 -38.97331,-68.78312 -38.97331,-68.78312 -36.8406,-72.27257 -36.8406,-72.27257 -38.97331))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1918"
 ]
}

 The dataset includes 270676 records from 333 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006713-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ysvjnt
title: Occurrence Download

description: A dataset containing 2168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "Geometry POLYGON ((154.6117 -29.28293, 156.6117 -29.28293, 156.6117 -27.28293, 154.6117 -27.28293, 154.6117 -29.28293))",
 "EventDate is greater than or equal to 2010-01-01",
 "EventDate is less than or equal to 2023-12-31",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)"
 ]
}

 The dataset includes 2168 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006731-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11216341
title: Users vs bots classification
DOI: 10.5281/zenodo.12588358
title: OPSSAT-AD - anomaly detection dataset for satellite telemetry

description: This is the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT---a CubeSat mission that has been operated by the European Space Agency.

It is accompanied by the paper with baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics that should always be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible, and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.

The included files are:



segments.csv with the acquired telemetry signals from ESA OPS-SAT aircraft,

dataset.csv with the extracted, synthetic features are computed for each manually split and labeled telemetry segment.

code files for data processing and example modeliing (dataset_generator.ipynb for data processing, modeling_examples.ipynb with simple examples,  requirements.txt- with details on Python configuration, and the LICENSE file)


 

Please have a look at our two papers commenting on this dataset:



The benchmark paper with results of 30 supervised and unsupervised anomaly detection models for this collection:Ruszczak, B., Kotowski. K., Nalepa, J., Evans, D.: The OPS-SAT benchmark for detecting anomalies in satellite telemetry, 2024, preprint arxiv: 2407.04730,

the conference paper in which we presented some preliminary results for this dataset:Ruszczak, B., Kotowski. K., Andrzejewski, J., et al.: (2023). Machine Learning Detects Anomalies in OPS-SAT Telemetry. Computational Science – ICCS 2023. LNCS, vol 14073. Springer, Cham, DOI:10.1007/978-3-031-35995-8_21.
DOI: 10.5281/zenodo.12588359
title: OPSSAT-AD - anomaly detection dataset for satellite telemetry

description: This is the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT---a CubeSat mission that has been operated by the European Space Agency.

It is accompanied by the paper with baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics that should always be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible, and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.

The two included files are:



segments.csv with the acquired telemetry signals from ESA OPS-SAT aircraft,

dataset.csv with the extracted, synthetic features are computed for each manually split and labeled telemetry segment.


 

Please have a look at our two papers commenting on this dataset:



The benchmark paper with results of 30 supervised and unsupervised anomaly detection models for this collection:Ruszczak, B., Kotowski. K., Nalepa, J., Evans, D.: The OPS-SAT benchmark for detecting anomalies in satellite telemetry, 2024, preprint arxiv: 2407.04730,

the conference paper in which we presented some preliminary results for this dataset:Ruszczak, B., Kotowski. K., Andrzejewski, J., et al.: (2023). Machine Learning Detects Anomalies in OPS-SAT Telemetry. Computational Science – ICCS 2023. LNCS, vol 14073. Springer, Cham, DOI:10.1007/978-3-031-35995-8_21.
DOI: 10.5281/zenodo.15108715
title: OPSSAT-AD - anomaly detection dataset for satellite telemetry

description: This is the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT---a CubeSat mission that has been operated by the European Space Agency.

It is accompanied by the paper with baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics that should always be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible, and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.

The included files are:



segments.csv with the acquired telemetry signals from ESA OPS-SAT aircraft,

dataset.csv with the extracted, synthetic features are computed for each manually split and labeled telemetry segment.

code files for data processing and example modeliing (dataset_generator.ipynb for data processing, modeling_examples.ipynb with simple examples,  requirements.txt- with details on Python configuration, and the LICENSE file)


 

Please have a look at our two papers commenting on this dataset:



The benchmark paper with results of 30 supervised and unsupervised anomaly detection models for this collection:Ruszczak, B., Kotowski. K., Nalepa, J., Evans, D.: The OPS-SAT benchmark for detecting anomalies in satellite telemetry, 2024, preprint arxiv: 2407.04730,

the conference paper in which we presented some preliminary results for this dataset:Ruszczak, B., Kotowski. K., Andrzejewski, J., et al.: (2023). Machine Learning Detects Anomalies in OPS-SAT Telemetry. Computational Science – ICCS 2023. LNCS, vol 14073. Springer, Cham, DOI:10.1007/978-3-031-35995-8_21.
DOI: 10.15468/dl.cgjcgu
title: Occurrence Download

description: A dataset containing 627 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-32.38425 35.61375,-21.37156 35.61375,-21.37156 40.52899,-32.38425 40.52899,-32.38425 35.61375))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nyctalus azoreum (Thomas, 1901)",
 "Year 1975-2025"
 ]
}

 The dataset includes 627 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006732-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vkqhhb
title: Occurrence Download

description: A dataset containing 3210 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Flora of the ancient settlements within Lower Dnipro – natural heritage with cultural background"
 ]
}

 The dataset includes 3210 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006733-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/82sr-ew24
title: Chemistry-Physics Lab Materials Dataset

description: Laboratory experiments are fundamental to science education, yet resource constraints often limit students’ access to hands-on learning experiences. While object detection technology offers promising solutions for automated material identification and assistance, existing datasets like CABD (21 classes) and Chemical Experiment Image Dataset (7 classes) are limited in scope. We present two comprehensive datasets for laboratory material detection: a Chemistry dataset comprising 1,191 images across 60 classes and a Physics dataset containing 1,749 images across 76 classes. Both datasets feature high-quality images captured in authentic laboratory settings. We validate these datasets using three state-of-the-art object detection models: YOLOv5nu, YOLOv8, and YOLOv11. The models achieve exceptional performance on both datasets, with YOLOv8 demonstrating the highest accuracy, reaching mean Average Precision (mAP@0.5) of 0.99 or 1.0 for most chemistry classes and comparable results for physics materials. Our key contributions include: (1) the first comprehensive open-source dataset of physics laboratory materials; (2) a significantly expanded chemistry dataset that addresses the limitations of existing collections; and (3) extensive validation using multiple YOLO architectures to ensure dataset reliability. These datasets enable the development of robust computer vision applications for laboratory education, potentially improving resource accessibility and student learning outcomes in STEM fields. The datasets[1] and trained models are made publicly available to support further research and development in educational technology.[1] https://github.com/danison2/Chemistry-Physics-Lab-Materials-Dataset
DOI: 10.6084/m9.figshare.28407395
title: Additional file

description: The placenta plays a key role in fetal growth and function to transport nutrients that are vital to the fetus. Heat stress (HS) impairs placental function in cows, affecting fetal development. There is limited information on the effects of HS on the placenta of heat-tolerant and -sensitive dry cows. The objective of this study was to assess differences in placental development and placental proteomics in heat-tolerant and -sensitive dry cows.
DOI: 10.6084/m9.figshare.28407395.v2
title: Additional file

description: The placenta plays a key role in fetal growth and function to transport nutrients that are vital to the fetus. Heat stress (HS) impairs placental function in cows, affecting fetal development. There is limited information on the effects of HS on the placenta of heat-tolerant and -sensitive dry cows. The objective of this study was to assess differences in placental development and placental proteomics in heat-tolerant and -sensitive dry cows.
DOI: 10.15468/dl.vsht7w
title: Occurrence Download

description: A dataset containing 2168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "Geometry POLYGON ((154.6117 -29.28293, 156.6117 -29.28293, 156.6117 -27.28293, 154.6117 -27.28293, 154.6117 -29.28293))",
 "EventDate is greater than or equal to 2010-01-01",
 "EventDate is less than or equal to 2023-12-31",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)"
 ]
}

 The dataset includes 2168 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006734-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kqtgnn
title: Occurrence Download

description: A dataset containing 22284285 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Liliopsida"
 ]
}

 The dataset includes 22284285 records from 1874 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006730-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1184448.10fu
title: SH1184448.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1184449.10fu
title: SH1184449.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.