cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wsturg
title: Occurrence Download

description: A dataset containing 11727 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 {
 "or" : [
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "Geometry POLYGON((-111.05811 -13.93503,-43.94189 -13.93503,-43.94189 12.93503,-111.05811 12.93503,-111.05811 -13.93503))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Campephilus gayaquilensis"
 ]
}

 The dataset includes 11727 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004341-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2djc7y
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.800225 24.343506, 88.723153 24.337929, 88.647998 24.321337, 88.576628 24.294142, 88.51081600000001 24.25702, 88.45219 24.210894, 88.402197 24.156909, 88.362065 24.096401, 88.33277200000001 24.03087, 88.31502500000001 23.961934, 88.30924400000001 23.891296, 88.315551 23.820695, 88.33377299999999 23.751869, 88.363443 23.686507, 88.403818 23.626215, 88.45389400000001 23.572467, 88.51243700000001 23.526579, 88.578007 23.489672, 88.648999 23.462648, 88.723679 23.446165, 88.800225 23.440626, 88.87677100000001 23.446165, 88.95145100000001 23.462648, 89.022443 23.489672, 89.088013 23.526579, 89.146556 23.572467, 89.19663199999999 23.626215, 89.23700700000001 23.686507, 89.266677 23.751869, 89.284899 23.820695, 89.291206 23.891296, 89.285425 23.961934, 89.267678 24.03087, 89.23838499999999 24.096401, 89.19825299999999 24.156909, 89.14825999999999 24.210894, 89.089634 24.25702, 89.023822 24.294142, 88.95245199999999 24.321337, 88.877297 24.337929, 88.800225 24.343506))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004343-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6t6hph
title: Occurrence Download

description: A dataset containing 64757 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Fulica rufifrons"
 ]
}

 The dataset includes 64757 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004314-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18334094
title: Raw code and datasets for CECL
DOI: 10.5281/zenodo.18334095
title: Raw code and datasets for CECL
DOI: 10.5281/zenodo.15516868
title: Object-Centric Event Log (OCEL) of the Enron Email Dataset

description: Description:

This dataset provides an object-centric event log (OCEL) representation of the publicly available Enron email corpus. The OCEL format allows for a richer analysis of interconnected processes and objects, making it particularly suitable for advanced process mining techniques, communication pattern analysis, and social network exploration.

The event logs were generated from a pre-processed CSV version of the Enron emails using a custom Python script leveraging the PM4Py library. The script parses individual emails to extract key information, including:



Timestamps: Derived from the 'Date' field of emails, parsed into timezone-aware datetime objects.

Activities: Inferred from email subject prefixes (e.g., "Re:" becomes "Response", "Fw:" becomes "Forwarding", "Invitation:" becomes "Invitation"). Emails without recognized prefixes are assigned a "Default" activity.

Objects: Two primary object types are identified:



EMAILADDRESS: Extracted from 'From', 'To', and 'Cc' fields.

MESSAGEID: Extracted from 'Message-ID', 'In-Reply-To', and 'References' fields, prefixed with "MID_" in the OCEL to ensure unique object identifiers across types.



Attributes: Event attributes include the original cleaned subject and content of the email.

Relationships: Events (emails) are linked to EMAILADDRESS objects with qualifiers 'FROM', 'TO', or 'CC'. Events are linked to MESSAGEID objects with qualifiers 'MESSAGEID' (for the email's own ID), 'INREPLYTO', or 'REFERENCES' to trace conversational threads.


To accommodate various analytical needs and computational resources, the dataset is provided in three distinct checkpoints:



Top 10,000 Emails: An OCEL generated from the first 10,000 emails processed.

Top 100,000 Emails: An OCEL generated from the first 100,000 emails processed.

All Emails: An OCEL generated from all emails processed by the script from the input emails.csv file.


Each checkpoint is available in the .jsonocel format (OCEL 2.0 standard), ready for use with PM4Py and other OCEL-compatible process mining tools. This dataset can be valuable for researchers and practitioners seeking to apply object-centric process discovery, conformance checking, and enhancement techniques to a large, real-world communication log.

Keywords: Object-Centric Event Log, OCEL, Process Mining, Enron Dataset, Email Analysis, Communication Networks, Social Network Analysis, PM4Py
DOI: 10.5281/zenodo.18334093
title: Object-Centric Event Log (OCEL) of the Enron Email Dataset

description: Description:

This dataset provides an object-centric event log (OCEL) representation of the publicly available Enron email corpus. The OCEL format allows for a richer analysis of interconnected processes and objects, making it particularly suitable for advanced process mining techniques, communication pattern analysis, and social network exploration.

The event logs were generated from a pre-processed CSV version of the Enron emails using a custom Python script leveraging the PM4Py library. The script parses individual emails to extract key information, including:



Timestamps: Derived from the 'Date' field of emails, parsed into timezone-aware datetime objects.

Activities: Inferred from email subject prefixes (e.g., "Re:" becomes "Response", "Fw:" becomes "Forwarding", "Invitation:" becomes "Invitation"). Emails without recognized prefixes are assigned a "Default" activity.

Objects: Two primary object types are identified:



EMAILADDRESS: Extracted from 'From', 'To', and 'Cc' fields.

MESSAGEID: Extracted from 'Message-ID', 'In-Reply-To', and 'References' fields, prefixed with "MID_" in the OCEL to ensure unique object identifiers across types.



Attributes: Event attributes include the original cleaned subject and content of the email.

Relationships: Events (emails) are linked to EMAILADDRESS objects with qualifiers 'FROM', 'TO', or 'CC'. Events are linked to MESSAGEID objects with qualifiers 'MESSAGEID' (for the email's own ID), 'INREPLYTO', or 'REFERENCES' to trace conversational threads.


To accommodate various analytical needs and computational resources, the dataset is provided in three distinct checkpoints:



Top 10,000 Emails: An OCEL generated from the first 10,000 emails processed.

Top 100,000 Emails: An OCEL generated from the first 100,000 emails processed.

All Emails: An OCEL generated from all emails processed by the script from the input emails.csv file.


Each checkpoint is available in the .jsonocel format (OCEL 2.0 standard), ready for use with PM4Py and other OCEL-compatible process mining tools. This dataset can be valuable for researchers and practitioners seeking to apply object-centric process discovery, conformance checking, and enhancement techniques to a large, real-world communication log.

Keywords: Object-Centric Event Log, OCEL, Process Mining, Enron Dataset, Email Analysis, Communication Networks, Social Network Analysis, PM4Py
DOI: 10.15468/dl.tjy3t8
title: Occurrence Download

description: A dataset containing 1304812 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Fulmarus glacialis"
 ]
}

 The dataset includes 1304812 records from 491 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004315-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jpn34c
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.800225 24.343506, 88.723153 24.337929, 88.647998 24.321337, 88.576628 24.294142, 88.51081600000001 24.25702, 88.45219 24.210894, 88.402197 24.156909, 88.362065 24.096401, 88.33277200000001 24.03087, 88.31502500000001 23.961934, 88.30924400000001 23.891296, 88.315551 23.820695, 88.33377299999999 23.751869, 88.363443 23.686507, 88.403818 23.626215, 88.45389400000001 23.572467, 88.51243700000001 23.526579, 88.578007 23.489672, 88.648999 23.462648, 88.723679 23.446165, 88.800225 23.440626, 88.87677100000001 23.446165, 88.95145100000001 23.462648, 89.022443 23.489672, 89.088013 23.526579, 89.146556 23.572467, 89.19663199999999 23.626215, 89.23700700000001 23.686507, 89.266677 23.751869, 89.284899 23.820695, 89.291206 23.891296, 89.285425 23.961934, 89.267678 24.03087, 89.23838499999999 24.096401, 89.19825299999999 24.156909, 89.14825999999999 24.210894, 89.089634 24.25702, 89.023822 24.294142, 88.95245199999999 24.321337, 88.877297 24.337929, 88.800225 24.343506))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004346-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18243760
title: SEKOLAH YANG MENYENANGKAN
title: Rahasia Menciptakan Lingkungan  Pembelajaran yang Ideal

description: This research aims to explore and analyze how Aqidah Akhlak instruction is implemented in fostering the religious character of sixth-grade students at MI Miftahul Ulum Karangploso. The study is grounded in the view that Aqidah Akhlak learning serves as a fundamental foundation for cultivating faith-based values and moral conduct among students at the Islamic primary school level. A qualitative approach with a descriptive design was employed to gain a comprehensive understanding of the learning process and the dynamics of religious character development within a natural educational context. Data collection techniques included direct classroom observation, in-depth interviews with Aqidah Akhlak teachers and selected students, and documentation analysis of lesson plans and madrasa religious programs. The findings indicate that the implementation of Aqidah Akhlak learning is conducted through three interconnected stages, namely planning, implementation, and evaluation. In the planning stage, teachers have developed instructional plans aligned with the current curriculum; however, indicators specifically targeting religious character development have not been formulated in a detailed and measurable manner. During the implementation stage, learning activities emphasize teacher exemplification, routine religious practices, and the integration of learning materials with students’ everyday experiences.
DOI: 10.5281/zenodo.18321072
title: Fluorescence quantitative PCR data

description: This dataset was generated by fluorescence quantitative PCR (qPCR). Specifically, samples 1, 2, and 3 in RNA data1 correspond to strain M1R; samples 4, 5, and 6 in RNA data1 correspond to strain M2R; samples 7, 8, and 9 in RNA data1 correspond to strain M3R; samples 1, 2, and 3 in RNA data2 correspond to strain ΔVM2R; samples 4, 5, and 6 in RNA data2 correspond to strain ΔM3R; and samples 1, 2, and 3 in RNA data3 correspond to strain PM2R.
DOI: 10.5281/zenodo.18321073
title: Fluorescence quantitative PCR data

description: This dataset was generated by fluorescence quantitative PCR (qPCR). Specifically, samples 1, 2, and 3 in RNA data1 correspond to strain M1R; samples 4, 5, and 6 in RNA data1 correspond to strain M2R; samples 7, 8, and 9 in RNA data1 correspond to strain M3R; samples 1, 2, and 3 in RNA data2 correspond to strain ΔVM2R; samples 4, 5, and 6 in RNA data2 correspond to strain ΔM3R; and samples 1, 2, and 3 in RNA data3 correspond to strain PM2R.
DOI: 10.15468/dl.ubjtkv
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.713815 24.359779, 88.63673300000001 24.354202, 88.56156799999999 24.33761, 88.49019 24.310415, 88.424369 24.273293, 88.365736 24.227167, 88.315737 24.173182, 88.275599 24.112674, 88.246303 24.047143, 88.228554 23.978207, 88.22277200000001 23.907569, 88.22908099999999 23.836968, 88.247305 23.768142, 88.276979 23.702781, 88.317359 23.642489, 88.367442 23.588741, 88.425991 23.542854, 88.49157 23.505947, 88.56257100000001 23.478923, 88.63726 23.46244, 88.713815 23.456901, 88.79037 23.46244, 88.865059 23.478923, 88.93606 23.505947, 89.001639 23.542854, 89.060188 23.588741, 89.110271 23.642489, 89.150651 23.702781, 89.180325 23.768142, 89.198549 23.836968, 89.204858 23.907569, 89.19907600000001 23.978207, 89.181327 24.047143, 89.15203099999999 24.112674, 89.11189299999999 24.173182, 89.061894 24.227167, 89.00326099999999 24.273293, 88.93744 24.310415, 88.866062 24.33761, 88.790897 24.354202, 88.713815 24.359779))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004348-260120142942310/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/14577496
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.23e6wq
title: Occurrence Download

description: A dataset containing 2302860 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Gallinago gallinago"
 ]
}

 The dataset includes 2302860 records from 2062 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004323-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/veabnb
title: Onderzoek naar kinderskelet in kruipruimte te Boazum, gemeente Súdwest Fryslân Onderzoek naar kinderskelet in kruipruimte te Boazum

description: &lt;p&gt;Het onderzoek aan het kinderskelet en het verkennend booronderzoek is in opdracht van de gemeente uitgevoerd ten behoeve van de ruiming van het skelet.&lt;/p&gt;
DOI: 10.14459/2025mp1834460
title: Technoeconomic analysis results of long-haul electric trucks considering dynamic wireless power transfer

description: This public dataset contains an adapted version of the time-series simulation tool “SimSES”. The adapted version allows working with electric trucks that are charged with dynamic wireless power transfer. Driving profiles for different scenarios are included in this dataset. Furthermore, simulation results for the following five different scenarios are included: 
Scenario A: 800kWh battery capacity, no DWPT 
Scenario B: 800kWh battery capacity, 40% DWPT coverage, 200kW DWPT power 
Scenario C: 400kWh battery capacity, 40% DWPT coverage, 200kW DWPT power 
Scenario D: 400kWh battery capacity, 95% DWPT coverage, 200kW DWPT power 
Scenario E: 400kWh battery capacity, 95% DWPT coverage, 100kW DWPT power 
Please refer to the README files that are part of the dataset for a more detailed description.
DOI: 10.5281/zenodo.17591447
title: Datasets of figures of On the fate of deep eutectic solvents after their use as reaction media: the CO2 production during downstream and ultimate disposal

description: Datasets of figures of On the fate of deep eutectic solvents after their use as reaction media: the CO2 production during downstream and ultimate disposal
DOI: 10.5281/zenodo.17591446
title: Datasets of figures of On the fate of deep eutectic solvents after their use as reaction media: the CO2 production during downstream and ultimate disposal

description: Datasets of figures of On the fate of deep eutectic solvents after their use as reaction media: the CO2 production during downstream and ultimate disposal
DOI: 10.15468/dl.f2xaty
title: Occurrence Download

description: A dataset containing 1 records in GBIF matching the query:
SELECT
 GBIF_EEARGCode(
 1000,
 decimalLatitude,
 decimalLongitude,
 COALESCE(coordinateUncertaintyInMeters, 1000)
 ) AS cell,
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE AND
 speciesKey IS NOT NULL AND
 NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND
 NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND
 NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND
 NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') AND
 decimalLatitude &gt;= 0.0 AND
 decimalLatitude &lt;= 4.045127286108716 AND
 decimalLongitude &gt;= -54.96226433444633 AND
 decimalLongitude &lt;= -49.88887061126883 AND
 "year" &gt;= 2000 AND
 "year" &lt;= 2025 AND
 (coordinateUncertaintyInMeters &lt;= 1000 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY cell
 ORDER BY species_richness DESC

 The dataset's 1 records were derived from 294 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030619-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13897729
title: Single-cell RNA-seq Atlas of the mouse hepatocyte and cholangiocyte lineages
DOI: 10.5281/zenodo.13897728
title: Single-cell RNA-seq Atlas of the mouse hepatocyte and cholangiocyte lineages
DOI: 10.5281/zenodo.17570474
title: High resolution Local energy zones map in China (2020) to support urban-scale energy-carbon research

description: Given the recent emergence of the Low Energy Zone (LEZ) concept in academic literature, there is a pressing need for a consistent, high-resolution, and accurate spatial dataset to support carbon-related studies. To bridge this data gap, we developed a systematic framework for delineating LEZs across China. This dataset, produced via the Google Earth Engine (GEE) platform, integrates multi-source remote sensing imagery and geospatial big data. It leverages a customized sample collection strategy, domain-specific feature engineering, and a robust classification algorithm implemented on GEE. The resulting product is the first 100 m resolution LEZ map of China for the year 2020. This dataset is designed to facilitate fine-grained carbon emission modeling, policy assessment, and cross-regional comparisons, thereby supporting spatially explicit climate mitigation strategies and urban energy-carbon research.
DOI: 10.5281/zenodo.17570473
title: High resolution Local energy zones map in China (2020) to support urban-scale energy-carbon research

description: Given the recent emergence of the Low Energy Zone (LEZ) concept in academic literature, there is a pressing need for a consistent, high-resolution, and accurate spatial dataset to support carbon-related studies. To bridge this data gap, we developed a systematic framework for delineating LEZs across China. This dataset, produced via the Google Earth Engine (GEE) platform, integrates multi-source remote sensing imagery and geospatial big data. It leverages a customized sample collection strategy, domain-specific feature engineering, and a robust classification algorithm implemented on GEE. The resulting product is the first 100 m resolution LEZ map of China for the year 2020. This dataset is designed to facilitate fine-grained carbon emission modeling, policy assessment, and cross-regional comparisons, thereby supporting spatially explicit climate mitigation strategies and urban energy-carbon research.
DOI: 10.6084/m9.figshare.27861108.v1
title: <b>Impact of AI on Personalized Learning Environments - Teacher Perspective Survey</b>

description: 专家小组成员讨论了 AI 对个性化教育、增强批判性思维和提高信息素养的影响，同时考虑了它带来的道德和实际挑战。
DOI: 10.15468/dl.n7kctw
title: Occurrence Download

description: A dataset containing 17673 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (France, Spain)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Myotis capaccinii",
 "Year 2000-2025"
 ]
}

 The dataset includes 17673 records from 191 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030645-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17599435
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251113k Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251113k Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.17599436
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251113k Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251113k Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.6084/m9.figshare.27861108
title: Generative AI in Personal Learning Environments: A Qualitative Study of Teachers’ Experiences in Chinese Higher Education<b>’</b><b> Experiences in Chinese Higher Education</b>

description: 专家小组成员讨论了 AI 对个性化教育、增强批判性思维和提高信息素养的影响，同时考虑了它带来的道德和实际挑战。
DOI: 10.6084/m9.figshare.27861108.v3
title: Generative AI in Personal Learning Environments: A Qualitative Study of Teachers’ Experiences in Chinese Higher Education<b>’</b><b> Experiences in Chinese Higher Education</b>

description: 专家小组成员讨论了 AI 对个性化教育、增强批判性思维和提高信息素养的影响，同时考虑了它带来的道德和实际挑战。
DOI: 10.6084/m9.figshare.27861108.v2
title: Generative AI in Personal Learning Environments: A Qualitative Study of Teachers’ Experiences in Chinese Higher Education<b>’</b><b> Experiences in Chinese Higher Education</b>

description: 专家小组成员讨论了 AI 对个性化教育、增强批判性思维和提高信息素养的影响，同时考虑了它带来的道德和实际挑战。