cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14725291
title: speechocean762 in WebDataset Format

description: This dataset is the speechocean762 dataset, formatted in the WebDataset format. WebDataset files are essentially tar archives, where each example in the dataset is represented by a pair of files: a WAV audio file and a corresponding JSON metadata file. The JSON file contains the class label and other relevant information for that particular audio sample.

$ tar tvf speechocean762_train.tar |head                   
-r--r--r-- bigdata/bigdata 607 2025-01-13 14:49 000010011.json
-r--r--r-- bigdata/bigdata 82604 2025-01-13 14:49 000010011.wav
-r--r--r-- bigdata/bigdata   646 2025-01-13 14:49 000010035.json
-r--r--r-- bigdata/bigdata 109804 2025-01-13 14:49 000010035.wav
-r--r--r-- bigdata/bigdata    630 2025-01-13 14:49 000010053.json
-r--r--r-- bigdata/bigdata 107244 2025-01-13 14:49 000010053.wav
-r--r--r-- bigdata/bigdata    561 2025-01-13 14:49 000010063.json
-r--r--r-- bigdata/bigdata 106252 2025-01-13 14:49 000010063.wav
-r--r--r-- bigdata/bigdata    671 2025-01-13 14:49 000010069.json
-r--r--r-- bigdata/bigdata  96364 2025-01-13 14:49 000010069.wav

$ cat  000010011.json
{"id": 10011, "accuracy": 8, "completeness": 10.0, "fluency": 9, "prosodic": 9, "words": [{"accuracy": 10, "stress": 10, "phones": ["W", "IY0"], "total": 10, "text": "WE", "phones-accuracy": [2.0, 2.0]}, {"accuracy": 10, "stress": 10, "phones": ["K", "AO0", "L"], "total": 10, "text": "CALL", "phones-accuracy": [2.0, 1.8, 1.8]}, {"accuracy": 10, "stress": 10, "phones": ["IH0", "T"], "total": 10, "text": "IT", "phones-accuracy": [2.0, 2.0]}, {"accuracy": 6, "stress": 10, "phones": ["B", "EH0", "R"], "total": 6, "text": "BEAR", "phones-accuracy": [2.0, 1.0, 1.0]}], "total": 8, "text": "WE CALL IT BEAR"}
DOI: 10.15468/dl.qn3b4n
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rattus exulans (Peale, 1848)",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 14 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002566-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7488/ds/7865
title: SV2A is expressed in subpopulations of mouse and human synapses

description: Synapse pathology is a feature of most brain diseases and there is a pressing need to monitor the onset and progression of this pathology using brain imaging in living patients. A major step toward this goal has been the development of small-molecule radiotracers that bind to synaptic vesicle glycoprotein 2A (SV2A) for use in positron emission tomography (PET). Changes in SV2A radiotracer binding in PET are widely interpreted to report differences in the density of all synapses throughout brain regions. Here, we analyse the expression of SV2A at single-synapse resolution across regions of adult mouse and human brain. We find that SV2A is expressed in fewer than 50% of excitatory and inhibitory synapses and that the density of SV2A-positive synapses differs between brain regions. Furthermore, individual synapses differ in their amounts of SV2A. These findings have important implications for the interpretation of PET imaging studies in a clinical setting and point to the need for a detailed understanding of SV2A synaptome architecture in both healthy brain and disease cases where PET imaging is being applied.
DOI: 10.5281/zenodo.14135592
title: Dataset of the cut-slope failure in the A-7 highway (S Spain)

description: The dataset contains various datasets and media related to the landslide analysis on the failure occurred in 11 March 2021 in the Km 354.3 of the A-7 Highway (S Spain). Each folder contains specific types of data collected and processed during different stages of the cut-slope assessment, including raw data, processed materials, and media documentation.

Folder Structure

DEMs: Digital Elevation Models (DEMs) representing different stages of the cut-slope. These DEMs serve as raw data for calculating volumes and understanding changes in the slope’s morphology over time.

FailureVolumes: Processed datasets used for detailed volume calculations of the landslide. These datasets represent the surfaces used for calculating the volume of the material displaced by the landslide.

Orthoimages: Orthoimages of the cut-slope at various stages. These images were acquired by drone, providing high-resolution, georeferenced views that facilitate visual analysis and comparison across different points in time.

PointClouds: Point cloud data in LAZ file format, representing different stages of the cut-slope. These point clouds offer a detailed spatial representation of the slope, valuable for further processing and 3D modeling.
Videos: Videos captured by drones, documenting the cut-slope’s condition on each data acquisition day during the emergency response and recovery phases. These videos provide a visual context for understanding the progression of the landslide and recovery efforts.

________________

Details on the dataset production and its analysis are provided in the following paper:

Galve, J.P., Pérez-García, J.L.,  Ruano, P., Gómez-López, J.M., Reyes-Carmona, C., Moreno-Sánchez, M., Jerez-Longres, P.S., Ghadimi, M., Barra, A., Mateos, R.M., Monserrat, O., Azañón, J.M. (2025) Applications of UAV Digital Photogrammetry in landslide emergency response and recovery activities: the case study of a slope failure in the A-7 highway (S Spain). Landslides. https://doi.org/10.1007/s10346-024-02449-9

The dataset includes materials gathered, acquired and processed during the investigation’s emergency and recovery phases. However, work at the site is ongoing. Please contact us (jpgalve@ugr.es) if you require additional information, as new data may have been generated since this dataset was published.
DOI: 10.5281/zenodo.14646215
title: BEAM-based model results of 850 sample buildings

description: This dataset contains highly detailed asset values and classifications for a subset of 850 buildings. It uses building volume to disaggregate net asset values across residential, industrial, service, and agricultural sectors.
DOI: 10.5281/zenodo.14646232
title: EHRE-based model results of 850 sample buildings

description: This dataset offers replacement cost estimates for a subset of 850 sample buildings in county "Ahrweiler" in western Germany. It includes data for residential and commercial building assets.
DOI: 10.5517/ccdc.csd.cc2jb8dk
title: CCDC 2333568: Experimental Crystal Structure Determination

description: Related Article: Shuyang Liu, Ziyu Gan, Min Jiang, Qian Liao, Yusheng Lu, Hongyao Wang, Zhiyan Xue, Ziyang Chen, Yongqiang Zhang, Xiaobo Yang, Chunying Duan, Yunhe Jin|2024|JACS Au|4|4899|doi:10.1021/jacsau.4c00880
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5285/fc0bc3d5887d441296091a8025f8f45d
title: EOCIS: Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product , version 4.00

description: This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.

Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.

The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.

Dataset coverage starts on 17th November 2018 and continues until 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.

The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.

The dataset was produced as part of the UK Earth Observation Climate Information Service (EOCIS) and is based on development funded under ESA CCI with additional funding from NCEO. The EOCIS dataset includes and continues the CCI v4 CDR (currently under development).
DOI: 10.15468/dl.6adybt
title: Occurrence Download

description: A dataset containing 1244375 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Zimbabwe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1244375 records from 402 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002565-250121130708018/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/10558938
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.15468/dl.anhhrt
title: Occurrence Download

description: A dataset listing the 3012 species recorded in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((18.3306 -34.08436,18.39852 -34.08436,18.39852 -34.02461,18.3306 -34.02461,18.3306 -34.08436))"
 ]
}

 The dataset's 3012 records were derived from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002564-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/a784eeb9287b43bcb63ccae59e6af82e
title: EOCIS: Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product, version 4.00

description: This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.

Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.

The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.

Dataset coverage starts on 1st May 2016 and continues until 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.

The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.

The dataset was produced as part of the UK Earth Observation Climate Information Service (EOCIS) and is based on development funded under ESA CCI with additional funding from NCEO. The EOCIS dataset includes and continues the CCI v4 CDR (currently under development).
DOI: 10.6084/m9.figshare.24086166
title: code

description: code used to generate the city-level population projection
DOI: 10.6084/m9.figshare.24229933.v2
title: Population Projection Dataset

description: National, provincial and city-level population projection
DOI: 10.6084/m9.figshare.24229933
title: Population Projection Dataset

description: National, provincial and city-level population projection
DOI: 10.6084/m9.figshare.24229933.v1
title: Population Projection Dataset

description: National, provincial and city-level population projection
DOI: 10.17028/rd.lboro.28263452
title: Supplementary information files for "Exploring sleepiness and stress among London bus drivers: An on-road observational study"

description: Supplementary files for article "Exploring sleepiness and stress among London bus drivers: An on-road observational study"<br><br>Bus driver sleepiness is commonplace but often goes unreported within the industry. Whilst past research has begun to shed a light on the prevalence, potential causes, and consequences of bus driver sleepiness, this is often done using self-report methods. This is the first study to investigate sleepiness amongst city bus drivers on-road using a live bus route with drivers’ regular schedules. A total of 16 participants completed two drives of their regular bus route once during an early morning shift and once during a daytime shift whilst physiological and self-report measures of sleep and stress were taken. Prior to these drives, drivers recorded their sleep in a diary and wore an actigraph to obtain objective sleep measures. Results showed that most drivers did not obtain sufficient sleep prior to early morning shifts, and often did not obtain as much sleep as they would need in order to feel rested before work. Sleepiness and stress were observed in both shifts. During early morning shifts sleepiness was likely a result of working during circadian lows and not obtaining enough sleep prior to the shift. In contrast, sleepiness during the daytime shift was likely a result of completing a highly demanding task in complex traffic which not only contributed to fatigue, but also led to increased levels of stress. As well as demonstrating the prevalence of sleepiness amongst bus drivers, these findings show that the causes of sleepiness can be multifaceted and often come about due to a combination of work and personal factors. In addition, the experience of sleepiness is not the same for all drivers, with individual differences in the experience of sleepiness playing a large role. These differences highlight the need for individualised interventions which should be considered by policymakers alongside the combination of causal factors within a larger systems approach.<br><br>© The Authors, CC BY-NC-ND 4.0
DOI: 10.17028/rd.lboro.28263452.v1
title: Supplementary information files for "Exploring sleepiness and stress among London bus drivers: An on-road observational study"

description: Supplementary files for article "Exploring sleepiness and stress among London bus drivers: An on-road observational study"<br><br>Bus driver sleepiness is commonplace but often goes unreported within the industry. Whilst past research has begun to shed a light on the prevalence, potential causes, and consequences of bus driver sleepiness, this is often done using self-report methods. This is the first study to investigate sleepiness amongst city bus drivers on-road using a live bus route with drivers’ regular schedules. A total of 16 participants completed two drives of their regular bus route once during an early morning shift and once during a daytime shift whilst physiological and self-report measures of sleep and stress were taken. Prior to these drives, drivers recorded their sleep in a diary and wore an actigraph to obtain objective sleep measures. Results showed that most drivers did not obtain sufficient sleep prior to early morning shifts, and often did not obtain as much sleep as they would need in order to feel rested before work. Sleepiness and stress were observed in both shifts. During early morning shifts sleepiness was likely a result of working during circadian lows and not obtaining enough sleep prior to the shift. In contrast, sleepiness during the daytime shift was likely a result of completing a highly demanding task in complex traffic which not only contributed to fatigue, but also led to increased levels of stress. As well as demonstrating the prevalence of sleepiness amongst bus drivers, these findings show that the causes of sleepiness can be multifaceted and often come about due to a combination of work and personal factors. In addition, the experience of sleepiness is not the same for all drivers, with individual differences in the experience of sleepiness playing a large role. These differences highlight the need for individualised interventions which should be considered by policymakers alongside the combination of causal factors within a larger systems approach.<br><br>© The Authors, CC BY-NC-ND 4.0
DOI: 10.15468/dl.h6w8ph
title: Occurrence Download

description: A dataset containing 870 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-70.68299013994117 -26.78512384012996,-70.17957225598902 -26.78512384012996,-70.17957225598902 -26.281705956177813,-70.68299013994117 -26.281705956177813,-70.68299013994117 -26.78512384012996))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Eriosyce rodentiophila F.Ritter"
 ]
}

 The dataset includes 870 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002571-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14725267
title: Pre-built EU Calliope power and hydrogen system model
DOI: 10.5281/zenodo.14725268
title: Pre-built EU Calliope power and hydrogen system model
DOI: 10.5281/zenodo.14722737
title: voxceleb1 in WebDataset Format

description: This dataset is the voxceleb1 dataset, formatted in the WebDataset format. WebDataset files are essentially tar archives, where each example in the dataset is represented by a pair of files: a WAV audio file and a corresponding JSON metadata file. The JSON file contains the class label and other relevant information for that particular audio sample.

$ tar tvf voxceleb1_train_0000000.tar|head                       
-r--r--r-- bigdata/bigdata  24 2025-01-10 12:16 ig9vI1du458_00005.json
-r--r--r-- bigdata/bigdata 143406 2025-01-10 12:16 ig9vI1du458_00005.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 y0UFutwJ-ow_00034.json
-r--r--r-- bigdata/bigdata 147246 2025-01-10 12:16 y0UFutwJ-ow_00034.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 FdYLFLTY-5Q_00050.json
-r--r--r-- bigdata/bigdata 138286 2025-01-10 12:16 FdYLFLTY-5Q_00050.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 zELwAz2W6hM_00010.json
-r--r--r-- bigdata/bigdata 165166 2025-01-10 12:16 zELwAz2W6hM_00010.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 Qz-GecPwRmI_00008.json
-r--r--r-- bigdata/bigdata 140846 2025-01-10 12:16 Qz-GecPwRmI_00008.wav

$ cat ig9vI1du458_00005.json     ✔ 
{"speakerid": "id10047"}
DOI: 10.5281/zenodo.14722736
title: voxceleb1 in WebDataset Format

description: This dataset is the voxceleb1 dataset, formatted in the WebDataset format. WebDataset files are essentially tar archives, where each example in the dataset is represented by a pair of files: a WAV audio file and a corresponding JSON metadata file. The JSON file contains the class label and other relevant information for that particular audio sample.

$ tar tvf voxceleb1_train_0000000.tar|head                       
-r--r--r-- bigdata/bigdata  24 2025-01-10 12:16 ig9vI1du458_00005.json
-r--r--r-- bigdata/bigdata 143406 2025-01-10 12:16 ig9vI1du458_00005.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 y0UFutwJ-ow_00034.json
-r--r--r-- bigdata/bigdata 147246 2025-01-10 12:16 y0UFutwJ-ow_00034.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 FdYLFLTY-5Q_00050.json
-r--r--r-- bigdata/bigdata 138286 2025-01-10 12:16 FdYLFLTY-5Q_00050.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 zELwAz2W6hM_00010.json
-r--r--r-- bigdata/bigdata 165166 2025-01-10 12:16 zELwAz2W6hM_00010.wav
-r--r--r-- bigdata/bigdata     24 2025-01-10 12:16 Qz-GecPwRmI_00008.json
-r--r--r-- bigdata/bigdata 140846 2025-01-10 12:16 Qz-GecPwRmI_00008.wav

$ cat ig9vI1du458_00005.json     ✔ 
{"speakerid": "id10047"}
DOI: 10.5281/zenodo.14725336
title: UV-induced fluorescence in the balance: Mate-choice and predation risk in the female-ornamented jumping spiders
DOI: 10.5281/zenodo.14512796
title: UV-induced fluorescence in the balance: Mate-choice and predation risk in the female-ornamented jumping spiders
DOI: 10.5281/zenodo.14512797
title: UV-induced fluorescence in the balance: Mate-choice and predation risk in the female-ornamented jumping spiders
DOI: 10.4121/3bee7025-46ad-4038-b4f9-8a406b223bfd
title: Port of Pantoloan Resilience Evaluation - Case Study Dataset

description: This dataset is part of Arry Destyanto’s dissertation “A Method for Evaluating Port Resilience in an Archipelago”, funded by Lembaga Pengelola Dana Pendidikan (LPDP). The study developed a method consisting of 4 analysis phases to evaluate a port’s resilience.<br>This dataset is used in the first case study, which applies the port resilience evaluation method in the Port of Pantoloan. The method was tested in the context of a commercial container port in Indonesia as an archipelago.<br>This dataset is in .zip format which when extracted consists of:Folder 1 - Dataset used for phase 1 (Island Connectivity Analysis)Folder 2 - Dataset used for phase 2 (Resilience Curve Analysis)Folder 3 - Dataset used for phase 3 (Disaster Impact Analysis)Dataset “Indonesia Vessels Capacity List 2018” from Ministry of Transportation.xlsx<br>The implementation of phase 4 used the analysis of official documents and mass articles listed in the dissertation.