cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/cysj-qhsb
title: Measurement of Mercury at Virolahti Harju

description: Measurement of Mercury at Virolahti Harju
DOI: 10.48597/xkdn-2497
title: Measurement of Mercury at Virolahti Harju

description: Measurement of Mercury at Virolahti Harju
DOI: 10.15468/dl.sz5wex
title: Occurrence Download

description: A dataset containing 7223 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "Elevation 0.0-9999.0m",
 "Geometry POLYGON((114.56759 -7.96224,114.42192 -8.0913,114.42192 -8.27914,114.63404 -8.45931,115.12538 -9.0778,117.21249 -9.3297,120.58839 -11.11648,126.49074 -10.82768,129.00003 -8.51303,128.383 -6.91807,122.94615 -7.55838,119.15083 -7.73301,114.82816 -7.84992,114.56759 -7.96224))",
 "Year 2000-2025"
 ]
}

 The dataset includes 7223 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054148-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.suwfh5
title: Occurrence Download

description: A dataset containing 125494955 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2023-2024"
 ]
}

 The dataset includes 125494955 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054142-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/h294-er41
title: "Bearing dataset"

description: "This dataset is from the Rotating Machinery Fault Diagnosis Experimental Platform of Guangdong Polytechnic Normal University (GPNU). The GPNU dataset encompassed three types of individual fault modes, namely inner race fault, outer race fault, and rolling element fault, and two types of normal operating condition signals. In addition, this dataset included six types of compound faults. As detailed in Table I, the sampling frequency of the GPNU dataset was 10 kHz, and each fault category contained 400 samples."
DOI: 10.48597/yq96-x9bg
title: Inorganics in air and particle phase at Virolahti Harju

description: Measurement of Inorganics in air and particle phase at Virolahti Harju
DOI: 10.48597/53mh-9a5c
title: Measurement of Inorganics in air and particle phase at Virolahti Harju

description: Measurement of Inorganics in air and particle phase at Virolahti Harju
DOI: 10.48597/s4st-q36x
title: Measurement of Inorganics in air and particle phase at Virolahti Harju

description: Measurement of Inorganics in air and particle phase at Virolahti Harju
DOI: 10.5281/zenodo.14054542
title: The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes

description: The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.

This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).
DOI: 10.5281/zenodo.14054543
title: The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes

description: The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.

This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).
DOI: 10.15468/dl.bmt85c
title: Occurrence Download

description: A dataset containing 2679966 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "PublishingCountry is Türkiye"
 ]
}

 The dataset includes 2679966 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054146-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w3dtah
title: Occurrence Download

description: A dataset containing 175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Burkina Faso",
 "OccurrenceStatus is Present",
 "TaxonKey is Anopheles arabiensis Patton, 1905"
 ]
}

 The dataset includes 175 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054151-250525065834625/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.29353148
title: Dados dos vídeos sobre a Amazônia publicados no YouTube entre agosto e outubro de 2019.

description: Dados referentes aos vídeos sobre a Amazônia publicados no <i>YouTube </i>entre agosto e outubro de 2019. Os dados são partes do estudo "Rede de desinformação no <i>YouTube</i>: as queimadas na Amazônia em 2019" e foram coletados no dia 17 de dezembro de 2020 através da ferramenta <i>YouTube Data Tools</i>.
DOI: 10.6084/m9.figshare.29353148.v1
title: Dados dos vídeos sobre a Amazônia publicados no YouTube entre agosto e outubro de 2019.

description: Dados referentes aos vídeos sobre a Amazônia publicados no <i>YouTube </i>entre agosto e outubro de 2019. Os dados são partes do estudo "Rede de desinformação no <i>YouTube</i>: as queimadas na Amazônia em 2019" e foram coletados no dia 17 de dezembro de 2020 através da ferramenta <i>YouTube Data Tools</i>.
DOI: 10.25574/42340
title: Chandra X-ray Observatory ObsId 42340
DOI: 10.15468/dl.2azn8v
title: Occurrence Download

description: A dataset containing 32810307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "PublishingCountry is United Kingdom of Great Britain and Northern Ireland"
 ]
}

 The dataset includes 32810307 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054147-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18141/2569609
title: Electricity Baseline 2020

description: The Electricity Baseline (2020) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2020" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569605. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).
DOI: 10.15468/dl.drpfh4
title: Occurrence Download

description: A dataset containing 241349191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2022-2023"
 ]
}

 The dataset includes 241349191 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054143-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18141/2569605
title: Electricity Baseline 2020 Background Data and Log File

description: The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data.

ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store).

This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files.

To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'.

The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.
DOI: 10.5281/zenodo.15689964
title: costus_lateriflorus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.15689965
title: costus_lateriflorus_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.utw599
title: Occurrence Download

description: A dataset containing 212680 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is IND.30_1",
 "IucnRedListCategory is one of (EX, EW, CR, EN, VU, NT, LC, DD, NE)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 212680 records from 198 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054141-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3hfp9
title: Occurrence Download

description: A dataset listing the 1183 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "Elevation 0.0-9999.0m",
 "Geometry POLYGON((114.56759 -7.96224,114.42192 -8.0913,114.42192 -8.27914,114.63404 -8.45931,115.12538 -9.0778,117.21249 -9.3297,120.58839 -11.11648,126.49074 -10.82768,129.00003 -8.51303,128.383 -6.91807,122.94615 -7.55838,119.15083 -7.73301,114.82816 -7.84992,114.56759 -7.96224))",
 "Year 2000-2025"
 ]
}

 The dataset's 1183 records were derived from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054150-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15689954
title: Data and processing for "Flip-Chip Optomechanics: Cooling Mechanics and Mitigating Noise with Feedback and Nonlinearity"

description: This folder contains all the code and data necessary to produce the figures of the PhD dissertation "Flip-Chip Optomechanics: Cooling Mechanics and Mitigating Noise with Feedback and Nonlinearity" written by Jean-Paul van Soest, and supervised by Gary A. Steele.
DOI: 10.6084/m9.figshare.29352908.v1
title: Supplementary Data

description: Monitoring changes in aboveground biomass of wetland vegetation is crucial for understanding the response of wetland ecosystems to global climate change. Here we compiled a new wetland aboveground biomass dataset with 1087 sites covering all wetland regions in China. Based on this dataset, we mapped the spatial distribution of wetland aboveground biomass in China using machine learning algorithms to understand its historical and future changes. The wetland aboveground biomass density of China in 2020 was 352.23 g C m<sup>−2</sup> on average and the total wetland aboveground biomass stock was 57.51 Tg C. During the past two decades, wetland aboveground biomass has gradually increased, indicating a notable greening trend in China’s wetlands. Our results project that China’s wetlands could continue to green-up rapidly and sustainably under various future climate change scenarios, but with varying degrees of greening. This highlights the difference in wetland ecosystem response to various climate conditions.
DOI: 10.6084/m9.figshare.29314496
title: EdU-seq HMM calling zones

description: All HMM calling and further processed EdU-seq zonesHidden Markov Model State 0,1,2 correspond to the EdU enriched, transition , depleted domains
DOI: 10.6084/m9.figshare.29314496.v1
title: EdU-seq HMM calling zones

description: All HMM calling and further processed EdU-seq zonesHidden Markov Model State 0,1,2 correspond to the EdU enriched, transition , depleted domains
DOI: 10.18141/2569580
title: Electricity Baseline 2021

description: The Electricity Baseline (2021) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2021" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569576. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).
DOI: 10.6084/m9.figshare.29352908
title: Persistent vegetation greening trends across China’s wetlands

description: Monitoring changes in aboveground biomass of wetland vegetation is crucial for understanding the response of wetland ecosystems to global climate change. Here we compiled a new wetland aboveground biomass dataset with 1087 sites covering all wetland regions in China. Based on this dataset, we mapped the spatial distribution of wetland aboveground biomass in China using machine learning algorithms to understand its historical and future changes. The wetland aboveground biomass density of China in 2020 was 352.23 g C m<sup>−2</sup> on average and the total wetland aboveground biomass stock was 57.51 Tg C. During the past two decades, wetland aboveground biomass has gradually increased, indicating a notable greening trend in China’s wetlands. Our results project that China’s wetlands could continue to green-up rapidly and sustainably under various future climate change scenarios, but with varying degrees of greening. This highlights the difference in wetland ecosystem response to various climate conditions.
DOI: 10.6084/m9.figshare.29352908.v3
title: Persistent vegetation greening trends across China’s wetlands

description: Monitoring changes in aboveground biomass of wetland vegetation is crucial for understanding the response of wetland ecosystems to global climate change. Here we compiled a new wetland aboveground biomass dataset with 1087 sites covering all wetland regions in China. Based on this dataset, we mapped the spatial distribution of wetland aboveground biomass in China using machine learning algorithms to understand its historical and future changes. The wetland aboveground biomass density of China in 2020 was 352.23 g C m<sup>−2</sup> on average and the total wetland aboveground biomass stock was 57.51 Tg C. During the past two decades, wetland aboveground biomass has gradually increased, indicating a notable greening trend in China’s wetlands. Our results project that China’s wetlands could continue to green-up rapidly and sustainably under various future climate change scenarios, but with varying degrees of greening. This highlights the difference in wetland ecosystem response to various climate conditions.
DOI: 10.6084/m9.figshare.29352908.v2
title: <b>Persistent vegetation greening trends across China’s wetlands</b>

description: Monitoring changes in aboveground biomass of wetland vegetation is crucial for understanding the response of wetland ecosystems to global climate change. Here we compiled a new wetland aboveground biomass dataset with 1087 sites covering all wetland regions in China. Based on this dataset, we mapped the spatial distribution of wetland aboveground biomass in China using machine learning algorithms to understand its historical and future changes. The wetland aboveground biomass density of China in 2020 was 352.23 g C m<sup>−2</sup> on average and the total wetland aboveground biomass stock was 57.51 Tg C. During the past two decades, wetland aboveground biomass has gradually increased, indicating a notable greening trend in China’s wetlands. Our results project that China’s wetlands could continue to green-up rapidly and sustainably under various future climate change scenarios, but with varying degrees of greening. This highlights the difference in wetland ecosystem response to various climate conditions.
DOI: 10.25345/c54x54v6q
title: MassIVE MSV000098252 - Glycosylation-Dependent Cargo Sorting in Melanoma-Derived Extracellular Vesicles: Proteomic Insights via LC-MS/MS Analysis
title: Previous studies on the role of glycosylation in protein sorting into extracellular vesicles (EVs) have focused on modifying individual proteins to determine whether glycosylation patterns influence their presence in EVs. However, the broader impact of cellular glycosylation on the overall protein composition of EVs remains unexplored. In this study, we assessed how two glycosylation inhibitors, tunicamycin (TM) and deoxymannojirymycin (DMJ), affect surface glycosylation of WM115 and WM266-4 melanoma cells, and of their secreted EVs, specifically exosomes (EXOs) and microvesicles (MVs). By comparing the glycosylation profiles of both the cells (from control and inhibitor-treated conditions) and their corresponding EVs, we identified how glycosylation disruption affects EV glycan content. Furthermore, we performed qualitative and quantitative proteomic analyses of EXOs and MVs to investigate changes in EV protein cargo resulting from altered cellular glycosylation. Perturbed cellular glycosylation affected EV glycosylation patterns, but these changes were not directly mirrored between the cells and EVs they released, indicating independent glycosylation regulation at the vesicular level. EXOs and MVs also showed a distinct enrichment of proteins in specific Gene Ontology (GO) categories. Remarkably, EXOs derived from WM266-4 cells consistently carried fewer proteins than their MV counterparts. Importantly, the number of potentially N-glycosylated proteins downregulated in EVs varied significantly, suggesting that N-glycosylation may affect protein sorting into both EXOs and MVs. Interestingly, proteins associated with EV biogenesis and secretion lacked predicted N-glycosylation sites. Finally, the analysis revealed the presence of three EV proteins encoded by genes associated with favorable prognosis and eighteen encoded by genes linked to unfavorable prognosis in melanoma. These results highlight the potential of N-glycan-mediated mechanisms in EV cargo sorting. A deeper understanding of this process may provide new insights into disease progression and biomarker discovery.
DOI: 10.18141/2569576
title: Electricity Baseline 2021 Background Data and Log File

description: The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data.

ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store).

This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files.

To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'.

The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.
DOI: 10.15468/dl.f44yd8
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "GadmGid is HRV",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nepa cinerea Linnaeus, 1758"
 ]
}

 The dataset includes 20 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054152-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.