cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5cv4c37b
title: MassIVE MSV000095849 - Serum Proteome Profiling of Diabetic Patients Treated with DPP4 and SGLT2 Inhibitors Show Improved Cognitive, and Cardiovascular Functions
title: This study employed a comprehensive proteomic analysis using LC-MS/MS to investigate the molecular impact of newer anti-diabetic drug classes, namely dipeptidyl peptidase 4 (DPP4) and sodium-glucose cotransporter 2 (SGLT2) inhibitors on T2D patients when combined with metformin used as a first-line therapy. Serum samples were collected from 76 individuals across various cohorts, including 16 healthy subjects, 32 T2D patients on metformin monotherapy, and 28 T2D patients receiving metformin combined with DPP4 or SGLT2 inhibitors.
DOI: 10.6084/m9.figshare.27004777
title: data_PONE-D-24-12069.csv

description: As grazing lands expand it is important to understand the effect cattle (<i>Bos taurus</i>) may have on native ungulates. Cattle presence in a landscape can cause both spatial and temporal partitioning in wild ungulates. We used remote cameras to investigate the impacts of seasonal rest-rotational cattle grazing on both the temporal and spatial behaviors of moose (<i>Alces alces</i>), elk (<i>Cervus canadensis</i>), mule deer (<i>Odocoileus hemonius</i>), and white-tailed deer (<i>Odocoileus virginianus</i>) in Cooking Lake-Blackfoot Provincial Park near Edmonton, AB, Canada. We found that all wild ungulates decrease their intensity of use in areas while cattle were grazing, and that this effect remains even after cattle have left, suggesting a degree of spatial partitioning. We also observed species specific changes in ungulate daily activity and nocturnality in response to cattle presence indicative of temporal partitioning. Elk increased their nocturnality while both deer species decreased their nocturnality. Understanding how cattle presence affects wild ungulates is essential for wildlife management, disease transmission, and conservation in the wake of potential increased ungulate-cattle interactions in the future.
DOI: 10.6084/m9.figshare.27004777.v1
title: data_PONE-D-24-12069.csv

description: As grazing lands expand it is important to understand the effect cattle (<i>Bos taurus</i>) may have on native ungulates. Cattle presence in a landscape can cause both spatial and temporal partitioning in wild ungulates. We used remote cameras to investigate the impacts of seasonal rest-rotational cattle grazing on both the temporal and spatial behaviors of moose (<i>Alces alces</i>), elk (<i>Cervus canadensis</i>), mule deer (<i>Odocoileus hemonius</i>), and white-tailed deer (<i>Odocoileus virginianus</i>) in Cooking Lake-Blackfoot Provincial Park near Edmonton, AB, Canada. We found that all wild ungulates decrease their intensity of use in areas while cattle were grazing, and that this effect remains even after cattle have left, suggesting a degree of spatial partitioning. We also observed species specific changes in ungulate daily activity and nocturnality in response to cattle presence indicative of temporal partitioning. Elk increased their nocturnality while both deer species decreased their nocturnality. Understanding how cattle presence affects wild ungulates is essential for wildlife management, disease transmission, and conservation in the wake of potential increased ungulate-cattle interactions in the future.
DOI: 10.34740/kaggle/dsv/5301446
title: 6 Species Mosquito in Thailand for Classification
DOI: 10.5281/zenodo.13754234
title: RIS Based Hand Gesture Recognition Dataset

description: RIS Based Hand Gesture Recognition Dataset

Overview

This dataset contains images for gesture recognition, divided into two main sets: dataset0608 and data_synthetic_variab. The data was collected using a wooden hand.   

 

dataset0608

This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized).

 

This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:

- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' 

- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' 

 

For each gestures = {close, two, open}, we have n_med values from 0 to 114 and 10 frames. Therefore, the ris_random and ris_optimized folders contain 10 frames × 115 measurements × 3 gestures = 3450 files, while the ris_random2 and ris_optimized2 folders contain 1 × 115 measurements × 3 gestures = 345 files.

 

data_synthetic_variab

This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized). 

 

This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:

- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' 

- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' 

 

For each gestures = {close, two, open},  we have n_med values from 0 to 8 and 10 frames. This dataset provides additional synthetic data with variations in hand position to increase the dataset's diversity. Each gesture is represented by 8 different ways, where the hand position was slightly modified between each sample. These real data were used as a basis for generating synthetic data. By using the functions in the files "multiply_files.txt" and "add_gaussian_noise.txt," the dataset was expanded and made more realistic by adding Gaussian noise to the images.

 

Therefore, the ris_random and ris_optimized folders contain 10 frames × 8 measurements × 3 gestures = 240 files, while the ris_random2 and ris_optimized2 folders contain 1 × 8 measurements × 3 gestures = 24 files.

 

Functions

* **add_gaussian_noise.txt:** This script adds Gaussian noise to the images to simulate real-world conditions and improve the robustness of the model.

* **compact_files_frames.txt:** This script combines multiple frames into a single image, which can be useful for certain types of analysis.
DOI: 10.5281/zenodo.13754235
title: RIS Based Hand Gesture Recognition Dataset

description: RIS Based Hand Gesture Recognition Dataset

Overview

This dataset contains images for gesture recognition, divided into two main sets: dataset0608 and data_synthetic_variab. The data was collected using a wooden hand.   

 

dataset0608

This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized).

 

This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:

- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' 

- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' 

 

For each gestures = {close, two, open}, we have n_med values from 0 to 114 and 10 frames. Therefore, the ris_random and ris_optimized folders contain 10 frames × 115 measurements × 3 gestures = 3450 files, while the ris_random2 and ris_optimized2 folders contain 1 × 115 measurements × 3 gestures = 345 files.

 

data_synthetic_variab

This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized). 

 

This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:

- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' 

- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' 

 

For each gestures = {close, two, open},  we have n_med values from 0 to 8 and 10 frames. This dataset provides additional synthetic data with variations in hand position to increase the dataset's diversity. Each gesture is represented by 8 different ways, where the hand position was slightly modified between each sample. These real data were used as a basis for generating synthetic data. By using the functions in the files "multiply_files.txt" and "add_gaussian_noise.txt," the dataset was expanded and made more realistic by adding Gaussian noise to the images.

 

Therefore, the ris_random and ris_optimized folders contain 10 frames × 8 measurements × 3 gestures = 240 files, while the ris_random2 and ris_optimized2 folders contain 1 × 8 measurements × 3 gestures = 24 files.

 

Functions

* **add_gaussian_noise.txt:** This script adds Gaussian noise to the images to simulate real-world conditions and improve the robustness of the model.

* **compact_files_frames.txt:** This script combines multiple frames into a single image, which can be useful for certain types of analysis.
DOI: 10.15468/dl.k6ts45
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-125.73938714478575 37.667129732731716,-121.73938714478575 37.667129732731716,-121.73938714478575 41.667129732731716,-125.73938714478575 41.667129732731716,-125.73938714478575 37.667129732731716))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sequoiadendron giganteum (Lindl.) J.T.Buchholz"
 ]
}

 The dataset includes 33 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013477-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10525252
title: CONUS-wide Balancing Authority Scale Hydropower Projections derived from 9505 Third Assessment

description: This dataset provides historical and climate projection monthly hydropower generation timeseries for balancing authorities within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in Broman et al., 2024 and in the article's metarepository. 

The dataset is provided in three separate formats to facilitate ease of use:

1) Machine-readable csv in 'tidy' data format:




Short Name
Unit
Description


class
N/A
simulation type; control: historical, cc: climate scenario


forcing
N/A
meteorological forcing used to drive hydrology model


model
N/A
hydrology model


hp
N/A
hydropower model


gcm*
N/A
global climate model name


ds*
N/A
downscaling method; DBCCA (statistical), RegCM (dynamical)


balancing_authority
N/A
balancing authority code


year
N/A
year


month
N/A
month


modeled_generation_MWh
MWh per month
simulated generation




* only present in the climate projection (cc) files

2) xlsx with balancing authority data by tab

3) csv by balancing authority:

for historical data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp and with the units MWh per month.

for climate projection (cc) data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp_gcm_ds and with the units MWh per month.
DOI: 10.5281/zenodo.10525253
title: CONUS-wide Balancing Authority Scale Hydropower Projections derived from 9505 Third Assessment

description: This dataset provides historical and climate projection monthly hydropower generation timeseries for balancing authorities within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in Broman et al., 2024 and in the article's metarepository. 

The dataset is provided in three separate formats to facilitate ease of use:

1) Machine-readable csv in 'tidy' data format:




Short Name
Unit
Description


class
N/A
simulation type; control: historical, cc: climate scenario


forcing
N/A
meteorological forcing used to drive hydrology model


model
N/A
hydrology model


hp
N/A
hydropower model


gcm*
N/A
global climate model name


ds*
N/A
downscaling method; DBCCA (statistical), RegCM (dynamical)


balancing_authority
N/A
balancing authority code


year
N/A
year


month
N/A
month


modeled_generation_MWh
MWh per month
simulated generation




* only present in the climate projection (cc) files

2) xlsx with balancing authority data by tab

3) csv by balancing authority:

for historical data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp and with the units MWh per month.

for climate projection (cc) data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp_gcm_ds and with the units MWh per month.
DOI: 10.7266/n7rr1wkk
title: R/V Endeavor: EN586 Hydrographic (CTD​) Data, Gulf of Mexico, July 26 to August 14, 2016
DOI: 10.5281/zenodo.10535564
title: CONUS-wide HUC4 Watershed Scale Hydropower Projections derived from 9505 Third Assessment

description: This dataset provides historical and climate projection monthly hydropower generation timeseries for HUC4 watersheds within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in Broman et al., 2024 and in the article's metarepository. Note that in some instances multiple HUC4 watersheds have been merged together into a HUC4 group, and that HUC4 watersheds without hydropower facilities are excluded. A hydropower facility's physical location, rather than the source of water, was used in assigning facilities to HUC4 watersheds.

The dataset is provided in three separate formats to facilitate ease of use:

1) Machine-readable csv in 'tidy' data format:




Short Name
Unit
Description


class
N/A
simulation type; control: historical, cc: climate scenario


forcing
N/A
meteorological forcing used to drive hydrology model


model
N/A
hydrology model


hp
N/A
hydropower model


gcm*
N/A
global climate model name


ds*
N/A
downscaling method; DBCCA (statistical), RegCM (dynamical)


HUC4_group
N/A
HUC4 group; HUC4 numeric ID or multiple IDs


year
N/A
year


month
N/A
month


modeled_generation_MWh
MWh per month
simulated generation




* only present in the climate projection (cc) files

2) xlsx with HUC4 group data by tab

3) csv by HUC4 group:

for historical data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp and with the units MWh per month.

for climate projection (cc) data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp_gcm_ds and with the units MWh per month.
DOI: 10.5281/zenodo.10535565
title: CONUS-wide HUC4 Watershed Scale Hydropower Projections derived from 9505 Third Assessment

description: This dataset provides historical and climate projection monthly hydropower generation timeseries for HUC4 watersheds within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in Broman et al., 2024 and in the article's metarepository. Note that in some instances multiple HUC4 watersheds have been merged together into a HUC4 group, and that HUC4 watersheds without hydropower facilities are excluded. A hydropower facility's physical location, rather than the source of water, was used in assigning facilities to HUC4 watersheds.

The dataset is provided in three separate formats to facilitate ease of use:

1) Machine-readable csv in 'tidy' data format:




Short Name
Unit
Description


class
N/A
simulation type; control: historical, cc: climate scenario


forcing
N/A
meteorological forcing used to drive hydrology model


model
N/A
hydrology model


hp
N/A
hydropower model


gcm*
N/A
global climate model name


ds*
N/A
downscaling method; DBCCA (statistical), RegCM (dynamical)


HUC4_group
N/A
HUC4 group; HUC4 numeric ID or multiple IDs


year
N/A
year


month
N/A
month


modeled_generation_MWh
MWh per month
simulated generation




* only present in the climate projection (cc) files

2) xlsx with HUC4 group data by tab

3) csv by HUC4 group:

for historical data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp and with the units MWh per month.

for climate projection (cc) data: year, month, and HUC4_group columns are the same as above. Data column headers are class_forcing_model_hp_gcm_ds and with the units MWh per month.
DOI: 10.15468/dl.wk3ret
title: Occurrence Download

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "LifeStage is Adult",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Mycetophilidae, Ceratopogonidae)"
 ]
}

 The dataset includes 165 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013478-240906103802322/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/9376164
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5281/zenodo.13753896
title: Dataset for: "Spectrally Resolving the Phase and Amplitude of Coherent Phonons in the Charge Density Wave State of 1T-TaSe2"
DOI: 10.5281/zenodo.13753897
title: Dataset for: "Spectrally Resolving the Phase and Amplitude of Coherent Phonons in the Charge Density Wave State of 1T-TaSe2"
DOI: 10.5281/zenodo.13693412
title: Dataset on Academics' Work and Well-being During the COVID-19 Pandemic

description: The "Dataset on Academics’ Work and Well-being During the COVID-19 Pandemic" was collected through an online survey conducted between June 10 and 20, 2020, using a cloud-based survey platform.

Acknowledgement: I acknowledge the valuable insights of Hande Eslen-Ziya and T. Murat Yildirim in the development of the survey and express my gratitude for their input.
DOI: 10.5281/zenodo.13754360
title: Chemical trajectory data file

description: Set of chemical trajectories for primordial gas generated with SGChem (CHEMISTRYNETWORK == 1), for use to train chemistry emulators. See CODES-Benchmark github repository for format details etc.

Note: test data, use at your own risk.
DOI: 10.5281/zenodo.13754361
title: Chemical trajectory data file

description: Set of chemical trajectories for primordial gas generated with SGChem (CHEMISTRYNETWORK == 1), for use to train chemistry emulators. See CODES-Benchmark github repository for format details etc.

Note: test data, use at your own risk.
DOI: 10.15468/dl.j4n3wu
title: Occurrence Download

description: A dataset containing 265293 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Material sample, Specimen, Living Specimen, Human Observation, Occurrence evidence)",
 "Geometry POLYGON((-74.8493 41.63146,-72.71859 41.62563,-72.73596 42.95159,-74.81972 42.95673,-74.8493 41.63146))",
 "HasGeospatialIssue is false",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 265293 records from 178 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013468-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atwjth
title: Occurrence Download

description: A dataset containing 57652 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Halictus tripartitus Cockerell, 1895"
 ]
}

 The dataset includes 57652 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013479-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12734980
title: AD_bulk_RNAseq_review

description: Supplementary files for the Systematic review and meta-analysis for bulk RNAseq studies comparing brain tissue from Alzheimer's disease patients and controls.
DOI: 10.5281/zenodo.12734981
title: AD_bulk_RNAseq_review

description: Supplementary files for the Systematic review and meta-analysis for bulk RNAseq studies comparing brain tissue from Alzheimer's disease patients and controls.
DOI: 10.5281/zenodo.12768775
title: AD_bulk_RNAseq_review

description: Supplementary files for the Systematic review and meta-analysis for bulk RNAseq studies comparing brain tissue from Alzheimer's disease patients and controls. Updated version now including heterogeneity statistics for meta-analysis results.
DOI: 10.5281/zenodo.13356006
title: AD_bulk_RNAseq_review

description: Supplementary files for the Systematic review and meta-analysis for bulk RNAseq studies comparing brain tissue from Alzheimer's disease patients and controls.
DOI: 10.5281/zenodo.13754371
title: AD_bulk_RNAseq_review

description: Supplementary files for the Systematic review and meta-analysis for bulk RNAseq studies comparing brain tissue from Alzheimer's disease patients and controls.
DOI: 10.5281/zenodo.13754362
title: AD_bulk_RNAseq_review

description: Supplementary files for the Systematic review and meta-analysis for bulk RNAseq studies comparing brain tissue from Alzheimer's disease patients and controls.
DOI: 10.15468/dl.hc6q4x
title: Occurrence Download

description: A dataset listing the 191 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nothofagus Blume"
 ]
}

 The dataset's 191 records were derived from 197 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013472-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.