cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e244393v1
title: Daily Census Tract-Level PM2.5 Concentrations, 2011-2014

description: This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2011-2014. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements:
1. Use the data for statistical reporting and analysis only.
2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals.
3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov.
4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating.
5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC.
6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
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description: (Downloaded 1/16/2026)
DOI: 10.15468/dl.xnvwwn
title: Occurrence Download

description: A dataset containing 383333 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Vespa velutina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 383333 records from 869 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000442-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e244425
title: Daily Census Tract-Level PM2.5 Concentrations, 2016 - 2020

description: This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2016-2020. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
description: collection 1/18/2026
DOI: 10.3886/e244425v1
title: Daily Census Tract-Level PM2.5 Concentrations, 2016 - 2020

description: This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2016-2020. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.
description: collection 1/18/2026
DOI: 10.15468/dl.yzbqnm
title: Occurrence Download

description: A dataset containing 25669501 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "Country is one of (Portugal, Spain)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25669501 records from 699 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000447-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fm2sj3k7rs
title: Supplementary Data for Substantial Accumulation of Reactive Organic Carbon in Continental Margin Sediments

description: This profile contains organic carbon datasets in the East China Continental Margin (ECCMs) sediments. Estimated organic carbon (OC) accumulation rates (OC-ARs) and burial fluxes on the East China Continental Margin (ECCM), calculated from OC content, dry bulk density (DBD), sedimentation rates (SR), and area of the corresponding sea regions of ECCMs.
DOI: 10.17632/fm2sj3k7rs.1
title: Supplementary Data for Substantial Accumulation of Reactive Organic Carbon in Continental Margin Sediments

description: This profile contains organic carbon datasets in the East China Continental Margin (ECCMs) sediments. Estimated organic carbon (OC) accumulation rates (OC-ARs) and burial fluxes on the East China Continental Margin (ECCM), calculated from OC content, dry bulk density (DBD), sedimentation rates (SR), and area of the corresponding sea regions of ECCMs.
DOI: 10.17632/mpmmtmxrg8
title: Sediment MS and microbial data

description: We characterize the biogeochemistry of the sediments in two typical regions of Taihu Lake.The raw data of P fractions, fluorescent signal, MS analysis, and microbial sequencing in sediments was collected. The precise Po molecular structures were identified. The Po mineralization processes were assessed at molecular scale.
DOI: 10.17632/mpmmtmxrg8.1
title: Sediment MS and microbial data

description: We characterize the biogeochemistry of the sediments in two typical regions of Taihu Lake.The raw data of P fractions, fluorescent signal, MS analysis, and microbial sequencing in sediments was collected. The precise Po molecular structures were identified. The Po mineralization processes were assessed at molecular scale.
DOI: 10.15468/dl.een6kj
title: Occurrence Download

description: A dataset containing 401 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Festuca albensis, Festuca alpina, Festuca alpina, Festuca csikhegyensis, Festuca duvalii, Festuca duvalii, Festuca duvalii, Festuca filiformis, Festuca filiformis, Festuca filiformis, Festuca filiformis, Festuca filiformis, Festuca guestfalica, Festuca guestfalica, Festuca laevigata, Festuca laevigata, Festuca ovina, Festuca ovina, Festuca ovina, Festuca pallens, Festuca pulveridolomiana, Festuca rupicaprina, Festuca rupicaprina, Festuca rupicaprina, Festuca rupicola, Festuca supina, Festuca supina, Festuca trachyphylla, Festuca trachyphylla, Festuca trachyphylla, Festuca valesiaca, Festuca valesiaca, Festuca valesiaca)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 401 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000467-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/c7yfxmtkyc
title: Reducing agricultural phosphorus pollution by regulating the hydrological processes of small water bodies(Research Data)

description: This dataset excerpt contains data from CNKI and Web of Science on phosphorus removal from small water bodies such as ditches and ponds, used to explore the processes affecting phosphorus removal in ditches and ponds.
DOI: 10.17632/c7yfxmtkyc.1
title: Reducing agricultural phosphorus pollution by regulating the hydrological processes of small water bodies(Research Data)

description: This dataset excerpt contains data from CNKI and Web of Science on phosphorus removal from small water bodies such as ditches and ponds, used to explore the processes affecting phosphorus removal in ditches and ponds.
DOI: 10.17632/9j447cynb9.1
title: Bangladesh Air Quality Index (AQI) Dataset (2000–2025): Historical Hourly Air Pollution Data Across 103 Cities

description: The Bangladesh Air Quality Index (AQI) Dataset (2000–2025) is a large-scale, high-resolution dataset providing historical air pollution data across 103 cities in Bangladesh, covering urban, divisional, district, sub-district, coastal, and border regions. Spanning 25 years, it contains over 3.19 million hourly observations, making it one of the most extensive publicly available air quality datasets for Bangladesh. Each record includes measurements of eight pollutants: PM10, PM2.5, CO, CO₂, NO₂, SO₂, O₃, and a composite AQI. Records also include geographic coordinates, city identifiers, city names, and timestamps in ISO 8601 format, ensuring precise spatial and temporal alignment.

The dataset is organized in a standardized CSV format with 13 columns, allowing easy integration with data analysis and machine learning pipelines. Temporal coverage varies by city, with older data primarily from major metropolitan areas such as Dhaka, Chittagong, and Sylhet, and more recent years providing extensive coverage across smaller cities and upazilas. This granular structure supports a wide range of applications, including time-series analysis, AQI forecasting, anomaly detection, spatial mapping, seasonal trend analysis, and feature engineering for predictive modeling.

From a technical perspective, the dataset ensures hourly resolution, enabling detailed short- and long-term trend analyses. Geographic coverage spans all major regions of Bangladesh, including the Ganges-Brahmaputra-Meghna delta, coastal areas like Cox’s Bāzār and Teknāf, and border locations such as Panchagarh and Thākurgaon. Although extensive, some missing values exist due to sensor maintenance, calibration, or transmission issues, and monitoring equipment differences may cause variability across locations and years.

The AQI values follow the US EPA standard, categorized into six levels: Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, and Hazardous. The dataset supports environmental research, public health studies, climate analysis, urban planning, and policy evaluation, as well as machine learning applications including pollutant prediction, city clustering, and AQI forecasting using LSTM, ARIMA, or Prophet.

Released under a CC BY 4.0 license, the dataset allows free sharing, adaptation, and commercial use with attribution. It provides a robust foundation for researchers, policymakers, and developers to study air quality trends, evaluate interventions, and implement data-driven public health and urban planning strategies.
DOI: 10.17632/6f72xv3h6w
title: Influence of sand fence porosity on aeolian sand transport and deposition

description: Wind tunnel and numerical simulation dataset for aeolian sand transport around perforated sand fences under fixed porosity
DOI: 10.17632/6f72xv3h6w.1
title: Influence of sand fence porosity on aeolian sand transport and deposition

description: Wind tunnel and numerical simulation dataset for aeolian sand transport around perforated sand fences under fixed porosity
DOI: 10.17632/npby5xngwt
title: Self-mineralization of sandstone-hosted uranium deposit revealed by iron isotopes

description: Supplementary Materials: Iron isotope fractionation model in sandstones; Thermodynamic modeling of U-δ56Fe coevolution.
Table S1 Whole-rock elements, S contents, and bulk Fe3+/ΣFe ratios of the Nalinggou uranium deposit. 
Table S2 Whole-rock Fe isotopic compositions of the Nalinggou uranium deposit.
DOI: 10.17632/npby5xngwt.1
title: Self-mineralization of sandstone-hosted uranium deposit revealed by iron isotopes

description: Supplementary Materials: Iron isotope fractionation model in sandstones; Thermodynamic modeling of U-δ56Fe coevolution.
Table S1 Whole-rock elements, S contents, and bulk Fe3+/ΣFe ratios of the Nalinggou uranium deposit. 
Table S2 Whole-rock Fe isotopic compositions of the Nalinggou uranium deposit.
DOI: 10.17632/tg2hm7n2bs
title: Image-to-Text Bilingual Dataset from Medical Prescriptions

description: The 1000 handwritten images of medical prescriptions in the dataset are annotated in both Bangla and English, making it suitable for OCR, NLP, machine translation, and machine learning-based healthcare research. `annotations.csv` is a CSV file containing individual rows for each prescription and their respective Bangla and English texts. Those prescriptions that were clear and full were selected and the name and other identifying features of the patients blacked out. The images were obtained from hospitals, clinics and pharmacies in Gazipur, Bangladesh. The intention is research and education. Please see README.pdf within the dataset for more information on using it.
DOI: 10.17632/tg2hm7n2bs.2
title: Image-to-Text Bilingual Dataset from Medical Prescriptions

description: The 1000 handwritten images of medical prescriptions in the dataset are annotated in both Bangla and English, making it suitable for OCR, NLP, machine translation, and machine learning-based healthcare research. `annotations.csv` is a CSV file containing individual rows for each prescription and their respective Bangla and English texts. Those prescriptions that were clear and full were selected and the name and other identifying features of the patients blacked out. The images were obtained from hospitals, clinics and pharmacies in Gazipur, Bangladesh. The intention is research and education. Please see README.pdf within the dataset for more information on using it.
DOI: 10.17632/zkmr9rz6hf
title: Single-cell RNA Sequencing of Human Atherosclerotic Plaques Implicates FABP5's Role in Smooth Muscle Cells Phenotypic Switching to Macrophage-like Cells During Disease Progression

description: This dataset contains raw and processed single-cell RNA sequencing (scRNA-seq) data from human atherosclerotic plaque tissues collected during carotid endarterectomy or coronary artery bypass grafting procedures. The samples were obtained from patients at Ningxia Medical University-affiliated hospitals, with informed consent and approval from the Institutional Ethics Committee.
DOI: 10.17632/zkmr9rz6hf.1
title: Single-cell RNA Sequencing of Human Atherosclerotic Plaques Implicates FABP5's Role in Smooth Muscle Cells Phenotypic Switching to Macrophage-like Cells During Disease Progression

description: This dataset contains raw and processed single-cell RNA sequencing (scRNA-seq) data from human atherosclerotic plaque tissues collected during carotid endarterectomy or coronary artery bypass grafting procedures. The samples were obtained from patients at Ningxia Medical University-affiliated hospitals, with informed consent and approval from the Institutional Ethics Committee.
DOI: 10.17632/hrfxycfwgz
title: Drone-Based Shrub and Grass Monitoring Dataset

description: This dataset contains drone-captured aerial images of shrubs, grass, and surrounding vegetation collected from the campus area of Daffodil International University, located in Savar, Dhaka, Bangladesh, Asia. The data was acquired using low-altitude drone footage and later converted into high-resolution image frames to support computer vision and deep learning research.

The primary objective of this dataset is to enable analysis of environmental impacts on shrub and grass life cycles using deep learning techniques. The images capture natural variations in vegetation structure, density, color, and growth patterns influenced by environmental factors such as sunlight exposure, soil conditions, seasonal changes, and human activity.

The dataset is suitable for a wide range of tasks, including vegetation classification, object detection, segmentation, lifecycle analysis, and environmental monitoring. It can be used to train and evaluate deep learning models such as convolutional neural networks for understanding plant health, growth stages, and stress indicators from aerial imagery.

All images were extracted from drone videos to ensure diversity in viewing angles, scales, and spatial context. Minimal preprocessing was applied to preserve real-world conditions and visual complexity. This makes the dataset particularly useful for developing robust models that can generalize to real environmental scenarios.

This dataset is intended for academic research, environmental studies, and machine learning experimentation. Researchers can use it to explore sustainable environmental monitoring approaches, assess vegetation dynamics, and develop data-driven solutions for mitigating environmental impacts on shrub and grass ecosystems.
DOI: 10.17632/hrfxycfwgz.2
title: Drone-Based Shrub and Grass Monitoring Dataset

description: This dataset contains drone-captured aerial images of shrubs, grass, and surrounding vegetation collected from the campus area of Daffodil International University, located in Savar, Dhaka, Bangladesh, Asia. The data was acquired using low-altitude drone footage and later converted into high-resolution image frames to support computer vision and deep learning research.

The primary objective of this dataset is to enable analysis of environmental impacts on shrub and grass life cycles using deep learning techniques. The images capture natural variations in vegetation structure, density, color, and growth patterns influenced by environmental factors such as sunlight exposure, soil conditions, seasonal changes, and human activity.

The dataset is suitable for a wide range of tasks, including vegetation classification, object detection, segmentation, lifecycle analysis, and environmental monitoring. It can be used to train and evaluate deep learning models such as convolutional neural networks for understanding plant health, growth stages, and stress indicators from aerial imagery.

All images were extracted from drone videos to ensure diversity in viewing angles, scales, and spatial context. Minimal preprocessing was applied to preserve real-world conditions and visual complexity. This makes the dataset particularly useful for developing robust models that can generalize to real environmental scenarios.

This dataset is intended for academic research, environmental studies, and machine learning experimentation. Researchers can use it to explore sustainable environmental monitoring approaches, assess vegetation dynamics, and develop data-driven solutions for mitigating environmental impacts on shrub and grass ecosystems.
DOI: 10.15468/dl.djhzne
title: Occurrence Download

description: A dataset containing 80647 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "Year 2021-2025",
 "GadmGid is one of (COL.3_2)",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample, Living Specimen, Occurrence evidence)"
 ]
 }
 ]
}

 The dataset includes 80647 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000448-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j7mdq6
title: Occurrence Download

description: A dataset containing 95720 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-4.03994 38.0809,-3.86047 38.0809,-3.86047 38.21309,-4.03994 38.21309,-4.03994 38.0809))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 95720 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000449-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/mdoow
title: House Unpassed Legislation 1850, Docket 2525, SC1/series 230, Petition of A.M. Polley

description: &lt;p&gt;Petition subject: Abolition of capital punishment &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:13481518"&gt;http://nrs.harvard.edu/urn-3:FHCL:13481518&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Leominster &lt;/p&gt; &lt;p&gt;Legislator, committee, or address that the petition was sent to: Solon Carter, Leominster &lt;/p&gt; &lt;p&gt;Selected signatures:&lt;ol&gt;&lt;li&gt;A.M. Polley&lt;/li&gt;&lt;li&gt;Elisha B. Foster&lt;/li&gt;&lt;li&gt;Dana Graham&lt;/li&gt;&lt;li&gt;Matilda Colburn&lt;/li&gt;&lt;/ol&gt; &lt;/p&gt; &lt;p&gt;Actions taken on dates: 1850-03-09 &lt;/p&gt; &lt;p&gt;Legislative action: Received in the House on March 9, 1850 and tabeled &lt;/p&gt; &lt;p&gt;Total signatures: 80 &lt;/p&gt; &lt;p&gt;Legislative action summary: Received, tabeled &lt;/p&gt; &lt;p&gt;Legal voter signatures (males not identified as non-legal): 53 &lt;/p&gt; &lt;p&gt;Female signatures: 17 &lt;/p&gt; &lt;p&gt;Other male signatures: 4 &lt;/p&gt; &lt;p&gt;Unidentified signatures: 6 &lt;/p&gt; &lt;p&gt;Female only signatures: No &lt;/p&gt; &lt;p&gt;Identifications of signatories: inhabitants, legal voters, females and others, [females], [other males], ["other persons"] &lt;/p&gt; &lt;p&gt;Prayer format was &lt;a href="http://en.wikipedia.org/wiki/Printing"&gt;printed&lt;/a&gt; vs. &lt;a href="http://en.wikipedia.org/wiki/Manuscript"&gt;manuscript&lt;/a&gt;: Printed &lt;/p&gt; &lt;p&gt;Signatory column format: column separated &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: no additional documents &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: House Unpassed 1850, Docket 2525 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;