cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.801349
title: Visual core description (VCD) from ODP Hole 180-1109B

description: Sediment depth is given in mbsf. Use "further details" link to download a zip archive of all visual core descriptions.
DOI: 10.25549/gar-c81-38609
title: Hamlin Garland, letter, to Lorado Zodac Taft

description: Hamlin Garland (The Player's Club), 16 Gramercy Park, New York, New York, USA, letter, to Lorado Zodac Taft. "The world won't want to do an article upon you and[?] with illustrations of all the things you want in." -- first line.
DOI: 10.1594/pangaea.270947
title: Paleontological investigation on planktonic foraminifers of ODP Hole 180-1116A
DOI: 10.1594/pangaea.798261
title: Protocol files of X-ray diffraction analysis from ODP Hole 126-787B

description: X-ray diffraction protocol files stored as pdf in a zip archive.
DOI: 10.1594/pangaea.800741
title: Visual core description (VCD) from ODP Hole 126-788D

description: Sediment depth is given in mbsf. Use "further details" link to download a zip archive of all visual core descriptions.
DOI: 10.1594/pangaea.255733
title: Geochemistry of interstitial water of ODP Hole 126-787B
DOI: 10.1594/pangaea.261800
title: Paleomagnetic measurements on ODP Hole 126-792E
DOI: 10.1594/pangaea.791983
title: Thermal conductivity of ODP Hole 138-847B
DOI: 10.1594/pangaea.791984
title: Thermal conductivity of ODP Hole 138-848B
DOI: 10.25549/gar-c81-32382
title: Edmund C. Stedman, letter, 1889-09-20, to Hamlin Garland

description: Edmund C. Stedman (A Library of American Literature), 3 East 14th Street, New York, New York, USA, letter, 1889 September 20, to Hamlin Garland. "I am right sorry." -- first line.
DOI: 10.1594/pangaea.255739
title: Geochemistry of interstitial water of ODP Hole 126-791B
DOI: 10.1594/pangaea.252991
title: Downhole temperature in ODP Hole 180-1109C
DOI: 10.1594/pangaea.257239
title: Gas elements of ODP Hole 180-1111A
DOI: 10.25549/gar-c81-31352
title: Blanche Hazard Sprague, letter, 1935-08-10, to Hamlin Garland

description: Blanche Hazard Sprague, 29 Newbury Street, Brockton, Massachusetts, USA, letter, 1935 August 10, to Hamlin Garland. "With your 'Afternoon Neighbors' beside me on my desk, I take courage to ask some questions and favors." -- first line.
DOI: 10.1594/pangaea.797153
title: Close-up images of ODP Hole 180-1115C

description: See further details for a thumbnail overview catalog.
DOI: 10.1594/pangaea.799647
title: Section images from ODP Hole 180-1109A
DOI: 10.1594/pangaea.794227
title: P-wave velocity (PWS split-core system) on ODP Hole 180-1109C
DOI: 10.1594/pangaea.794229
title: P-wave velocity (PWS split-core system) on ODP Hole 180-1110A
DOI: 10.1594/pangaea.266276
title: Magnetic susceptibility on ODP Hole 180-1109B
DOI: 10.1594/pangaea.789895
title: Paleomagnetic measurements on ODP Hole 180-1115C
DOI: 10.25549/gar-c81-30958
title: Clifford Smyth, letter, to Hamlin Garland

description: Clifford Smyth (The Literary Digest International Book Review), 354-360 4th Avenue, New York, New York, USA, letter, [no year] January 30, to Hamlin Garland. "I have just received your note of the 29th, and I am greatly surprised to learn from it that you had not, long ere [sic] this, receieved a letter I had written you in reply to yorus of January 10th." -- first line.
DOI: 10.1594/pangaea.796515
title: Close-up images of ODP Hole 126-792A

description: See further details for a thumbnail overview catalog.
DOI: 10.1594/pangaea.797931
title: Depth-age model of ODP Hole 135-838A
DOI: 10.1594/pangaea.791349
title: Shear strength of ODP Hole 126-792D
DOI: 10.1594/pangaea.799649
title: Section images from ODP Hole 180-1109C
DOI: 10.25549/gar-c81-32080
title: Homer Schiff Saint-Gaudens, letter, 1907-08-17, to Hamlin Garland

description: Homer Schiff Saint-Gaudens, letter, 1907 August 17, to Hamlin Garland. "I thank you very deeply for your good letter." -- first line.
DOI: 10.15468/dl.edaynh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Leptosomiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-120 -10,-110 -10,-110 0,-120 0,-120 -10))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024765-251009101135966/datasets/export for details.
DOI: 10.13140/rg.2.2.21343.05288
title: Paccoud - Buy-to-let gentrification - Online Appendices
DOI: 10.13140/rg.2.2.12800.43521
title: Research Achievements of Dr. Muhammad Darawad 2016
DOI: 10.13140/rg.2.2.16496.66567
title: Astroide 2D, 3D y 4D
DOI: 10.15468/dl.hn9ye4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Scaphopoda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-120 -10,-110 -10,-110 0,-120 0,-120 -10))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024770-251009101135966/datasets/export for details.
DOI: 10.15468/dl.2w5rg3
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphorbia hypericifolia",
 "Country is Palau",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077671-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m8euzy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Casuarina cunninghamiana",
 "Country is Eritrea",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077673-250717081556266/datasets/export for details.
DOI: 10.15468/dl.77fynr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Dendropsophus koechlini)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077674-250717081556266/datasets/export for details.
DOI: 10.5281/zenodo.15253377
title: Supplemental Data for 'Hacktive Matter: data-driven discovery through hackathon-based cross-disciplinary coding'
DOI: 10.5281/zenodo.15253378
title: Supplemental Data for 'Hacktive Matter: data-driven discovery through hackathon-based cross-disciplinary coding'
DOI: 10.5281/zenodo.18760693
title: MODERNMETHODSOFAI-ASSISTEDTEACHINGINMASSIVEOPENONLINECOURSES(MOOCS)

description: MassiveOpenOnlineCourses(MOOCs)haveemergedasarevolutionarymodelofeducation,providinglarge-scaleaccesstolearningopportunitiesacrosstheglobe.Despitetheirrapidexpansionandpopularity,MOOCsfacesignificantpedagogicalchallenges,includinglowcompletionrates,limitedpersonalization,insufficientlearnerengagement,andminimalinstructorsupport.TheintegrationofArtificialIntelligence(AI)intoMOOCplatformshasintroducedinnovativeteachingandlearningmethodsaimedatovercomingtheselimitations.ThisarticleexploresmodernAI-assistedteachingmethodsinMOOCsbyexaminingtheirtheoreticalfoundationsandpracticalapplications.Thestudyhighlightstheroleofadaptivelearningsystems,intelligenttutoringsystems,learninganalytics,automatedassessment,andAI-drivencontentdeliveryinenhancingtheeffectivenessandqualityofMOOC-basededucation.ThefindingssuggestthatAItechnologieshavethepotentialtotransformMOOCsintomorelearner-centered,efficient,andinclusiveeducationalenvironments.
DOI: 10.5281/zenodo.18760692
title: MODERNMETHODSOFAI-ASSISTEDTEACHINGINMASSIVEOPENONLINECOURSES(MOOCS)

description: MassiveOpenOnlineCourses(MOOCs)haveemergedasarevolutionarymodelofeducation,providinglarge-scaleaccesstolearningopportunitiesacrosstheglobe.Despitetheirrapidexpansionandpopularity,MOOCsfacesignificantpedagogicalchallenges,includinglowcompletionrates,limitedpersonalization,insufficientlearnerengagement,andminimalinstructorsupport.TheintegrationofArtificialIntelligence(AI)intoMOOCplatformshasintroducedinnovativeteachingandlearningmethodsaimedatovercomingtheselimitations.ThisarticleexploresmodernAI-assistedteachingmethodsinMOOCsbyexaminingtheirtheoreticalfoundationsandpracticalapplications.Thestudyhighlightstheroleofadaptivelearningsystems,intelligenttutoringsystems,learninganalytics,automatedassessment,andAI-drivencontentdeliveryinenhancingtheeffectivenessandqualityofMOOC-basededucation.ThefindingssuggestthatAItechnologieshavethepotentialtotransformMOOCsintomorelearner-centered,efficient,andinclusiveeducationalenvironments.
DOI: 10.5281/zenodo.17375414
title: GDB-20 Database - Part2

description: About 

GDB-20 enumerates small organic molecules up to 20 atoms of C, N, O, S, F, Cl, Br, and I following simple chemical stability and synthetic feasibility rules.

By combining systematic graph enumeration with machine learning–based generation, we assembled 12,092,137,338 unique molecules as part of the newest GDB-20 database, representing a subset of an estimated 32 trillion possible molecules within this chemical space.

 

How to cite

To cite GDB-20, please reference:

Ye Buehler, Sacha Javor, and Jean-Louis Reymond. “Sampling a GDB-20 Database of 32 Trillion Drug-Like Molecules by Generative Artificial Intelligence.” ChemRxiv, 2026, https://doi.org/10.26434/chemrxiv.15000288/v1.

Dataset DOI: 

https://doi.org/10.5281/zenodo.17368725.

https://doi.org/10.5281/zenodo.17375415.

 

Download

You can download the databases and subsets of it using the links provided. All the molecules are stored in canonized SMILES format and compressed as tar/gz archive (for Windows users: Download 7-zip to open archives).

 

GDB-20 (Part1)

GDB-20-Set (50 thousand)     GDB20.50000.smi.gz    470.05 KB

GDB-20-Set (50 million)    GDB20.50000000.smi.gz     484.05 MB

GDB-20-Set (HAC1-17)    GDB20.HAC1-17.tar.gz     2.84 GB

GDB-20-Set (HAC18)     GDB20.HAC18.tar.gz    13.73 GB

GDB-20-Set (HAC19)     GDB20.HAC19.tar.gz     17.85 GB

GDB-20 (Part2)

GDB-20-Set (HAC20)     GDB20.HAC20.tar.gz    35.30 GB

 

Terms and conditions: The GDB databases may be downloaded free of charge. In published research involving GDB, cite the appropriate references mentioned above. GDB must not be used as part of or in patents. GDB and large portions thereof must not be redistributed without the express written permission of Jean-Louis Reymond.
DOI: 10.5281/zenodo.17375415
title: GDB-20 Database - Part2

description: About 

GDB-20 enumerates small organic molecules up to 20 atoms of C, N, O, S, F, Cl, Br, and I following simple chemical stability and synthetic feasibility rules.

By combining systematic graph enumeration with machine learning–based generation, we assembled 12,092,137,338 unique molecules as part of the newest GDB-20 database, representing a subset of an estimated 32 trillion possible molecules within this chemical space.

 

How to cite

To cite GDB-20, please reference:

Ye Buehler, Sacha Javor, and Jean-Louis Reymond. “Sampling a GDB-20 Database of 32 Trillion Drug-Like Molecules by Generative Artificial Intelligence.” ChemRxiv, 2026, https://doi.org/10.26434/chemrxiv.15000288/v1.

Dataset DOI: 

https://doi.org/10.5281/zenodo.17368725.

https://doi.org/10.5281/zenodo.17375415.

 

Download

You can download the databases and subsets of it using the links provided. All the molecules are stored in canonized SMILES format and compressed as tar/gz archive (for Windows users: Download 7-zip to open archives).

 

GDB-20 (Part1)

GDB-20-Set (50 thousand)     GDB20.50000.smi.gz    470.05 KB

GDB-20-Set (50 million)    GDB20.50000000.smi.gz     484.05 MB

GDB-20-Set (HAC1-17)    GDB20.HAC1-17.tar.gz     2.84 GB

GDB-20-Set (HAC18)     GDB20.HAC18.tar.gz    13.73 GB

GDB-20-Set (HAC19)     GDB20.HAC19.tar.gz     17.85 GB

GDB-20 (Part2)

GDB-20-Set (HAC20)     GDB20.HAC20.tar.gz    35.30 GB

 

Terms and conditions: The GDB databases may be downloaded free of charge. In published research involving GDB, cite the appropriate references mentioned above. GDB must not be used as part of or in patents. GDB and large portions thereof must not be redistributed without the express written permission of Jean-Louis Reymond.
DOI: 10.15468/dl.rztt3q
title: Occurrence Download

description: A dataset containing 1522950 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Andrenidae"
 ]
}

 The dataset includes 1522950 records from 1170 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007492-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18759183
title: UTR annotations for TriTrypDB trypanosomatid parasite genomes

description: This dataset provides 5′ and 3′ untranslated region (UTR) annotations and Open Reading Frames (ORFs) corrected based on UTRs for 47 trypanosomatid genomes hosted on TriTrypDB (version 68). Trypanosomatids, including human pathogens such as Leishmania and Trypanosoma species, utilise polycistronic transcription where mature mRNAs are generated via trans-splicing and polyadenylation. These data were generated using the slapquant software suite, which identifies spliced leader acceptor sites (SLAS) and polyadenylation sites (PAS) from short-read RNA-seq data using a clipped-alignment strategy, then uses these to define UTRs and potential ORF corrections.

Files Included:





Annotated GFF Files: Genome-wide functional annotation files for 47 species/strains containing identified SLAS, PAS, and resulting 5′ and 3′ UTRs (files containing the _slaputrs filename suffix).




Corrected CDS/ORFs Files: Corrected protein-coding sequences (CDS) where trans-splicing evidence (SLAS positions) suggested a more likely upstream or downstream start codon than the original annotation (files containing the -fixedorfs filename suffix).



Result files are named after the corresponding genome identifier on TriTrypDB, and should be used with the corresponding genome sequence fasta file from TriTrypDB version 68.

Software Availability: The tools used to generate these files are available at: https://github.com/Wheeler-Lab/slapquant.
DOI: 10.5281/zenodo.18759184
title: UTR annotations for TriTrypDB trypanosomatid parasite genomes

description: This dataset provides 5′ and 3′ untranslated region (UTR) annotations and Open Reading Frames (ORFs) corrected based on UTRs for 47 trypanosomatid genomes hosted on TriTrypDB (version 68). Trypanosomatids, including human pathogens such as Leishmania and Trypanosoma species, utilise polycistronic transcription where mature mRNAs are generated via trans-splicing and polyadenylation. These data were generated using the slapquant software suite, which identifies spliced leader acceptor sites (SLAS) and polyadenylation sites (PAS) from short-read RNA-seq data using a clipped-alignment strategy, then uses these to define UTRs and potential ORF corrections.

Files Included:





Annotated GFF Files: Genome-wide functional annotation files for 47 species/strains containing identified SLAS, PAS, and resulting 5′ and 3′ UTRs (files containing the _slaputrs filename suffix).




Corrected CDS/ORFs Files: Corrected protein-coding sequences (CDS) where trans-splicing evidence (SLAS positions) suggested a more likely upstream or downstream start codon than the original annotation (files containing the -fixedorfs filename suffix).



Result files are named after the corresponding genome identifier on TriTrypDB, and should be used with the corresponding genome sequence fasta file from TriTrypDB version 68.

Software Availability: The tools used to generate these files are available at: https://github.com/Wheeler-Lab/slapquant.
DOI: 10.5281/zenodo.17368725
title: GDB-20 Database - Part1

description: About 

GDB-20 enumerates small organic molecules up to 20 atoms of C, N, O, S, F, Cl, Br, and I following simple chemical stability and synthetic feasibility rules.

By combining systematic graph enumeration with machine learning–based generation, we assembled 12,092,137,338 unique molecules as part of the newest GDB-20 database, representing a subset of an estimated 32 trillion possible molecules within this chemical space.

 

How to cite

To cite GDB-20, please reference:

Ye Buehler, Sacha Javor, and Jean-Louis Reymond. “Sampling a GDB-20 Database of 32 Trillion Drug-Like Molecules by Generative Artificial Intelligence.” ChemRxiv, 2026, https://doi.org/10.26434/chemrxiv.15000288/v1.

Dataset DOI:

https://doi.org/10.5281/zenodo.17368725.

https://doi.org/10.5281/zenodo.17375415.

 

Download

You can download the databases and subsets of it using the links provided. All the molecules are stored in canonized SMILES format and compressed as tar/gz archive (for Windows users: Download 7-zip to open archives).

 

GDB-20 (Part1)

GDB-20-Set (50 thousand)     GDB20.50000.smi.gz    470.05 KB

GDB-20-Set (50 million)    GDB20.50000000.smi.gz     484.05 MB

GDB-20-Set (HAC1-17)    GDB20.HAC1-17.tar.gz     2.84 GB

GDB-20-Set (HAC18)     GDB20.HAC18.tar.gz    13.73 GB

GDB-20-Set (HAC19)     GDB20.HAC19.tar.gz     17.85 GB

GDB-20 (Part2)

GDB-20-Set (HAC20)     GDB20.HAC20.tar.gz    35.30 GB

 

Terms and conditions: The GDB databases may be downloaded free of charge. In published research involving GDB, cite the appropriate references mentioned above. GDB must not be used as part of or in patents. GDB and large portions thereof must not be redistributed without the express written permission of Jean-Louis Reymond.
DOI: 10.5281/zenodo.17368724
title: GDB-20 Database - Part1

description: About 

GDB-20 enumerates small organic molecules up to 20 atoms of C, N, O, S, F, Cl, Br, and I following simple chemical stability and synthetic feasibility rules.

By combining systematic graph enumeration with machine learning–based generation, we assembled 12,092,137,338 unique molecules as part of the newest GDB-20 database, representing a subset of an estimated 32 trillion possible molecules within this chemical space.

 

How to cite

To cite GDB-20, please reference:

Ye Buehler, Sacha Javor, and Jean-Louis Reymond. “Sampling a GDB-20 Database of 32 Trillion Drug-Like Molecules by Generative Artificial Intelligence.” ChemRxiv, 2026, https://doi.org/10.26434/chemrxiv.15000288/v1.

Dataset DOI:

https://doi.org/10.5281/zenodo.17368725.

https://doi.org/10.5281/zenodo.17375415.

 

Download

You can download the databases and subsets of it using the links provided. All the molecules are stored in canonized SMILES format and compressed as tar/gz archive (for Windows users: Download 7-zip to open archives).

 

GDB-20 (Part1)

GDB-20-Set (50 thousand)     GDB20.50000.smi.gz    470.05 KB

GDB-20-Set (50 million)    GDB20.50000000.smi.gz     484.05 MB

GDB-20-Set (HAC1-17)    GDB20.HAC1-17.tar.gz     2.84 GB

GDB-20-Set (HAC18)     GDB20.HAC18.tar.gz    13.73 GB

GDB-20-Set (HAC19)     GDB20.HAC19.tar.gz     17.85 GB

GDB-20 (Part2)

GDB-20-Set (HAC20)     GDB20.HAC20.tar.gz    35.30 GB

 

Terms and conditions: The GDB databases may be downloaded free of charge. In published research involving GDB, cite the appropriate references mentioned above. GDB must not be used as part of or in patents. GDB and large portions thereof must not be redistributed without the express written permission of Jean-Louis Reymond.
DOI: 10.15468/dl.emyjjt
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "TaxonKey is Lonchophylla handleyi"
 ]
}

 The dataset includes 91 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007565-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y5d8fx
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Armenia",
 "TaxonKey is Infundibulicybe geotropa"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007566-260221153910048/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.30341143
title: RoadSens-4M Dataset

description: The <b>RoadSens-4M Dataset</b> provides a multimodal collection of sensor, video, weather, and GIS data designed to support research in intelligent transportation systems, road condition monitoring, and machine-learning-based anomaly detection. This dataset integrates synchronized smartphone sensor data (accelerometer, gyroscope, magnetometer, GPS) with video annotations, weather, and geospatial information to accurately identify and classify road surface anomalies, including bumps, potholes, and normal road segments.<b>The dataset comprises 103 data sessions organized in a hierarchical structure to facilitate flexible access and multi-level analysis.</b> It is divided into four main components: <b>Raw Data</b>, <b>Combined CSV with GIS and Weather Data</b>, <b>Isolated Data</b>, and <b>GIS Data</b>. Each session folder contains all corresponding sensor CSV files, including both calibrated and uncalibrated readings from the accelerometer, gyroscope, magnetometer, barometer, compass, gravity, and GPS sensors, along with annotation and metadata files. Within every session, a dedicated <b>camera subfolder</b> holds annotation data and a text file linking to the corresponding video stored on Google Drive, allowing researchers to access complete recordings without manual segmentation.The <b>merged CSV files</b> combine synchronized sensor, GIS, and weather information (temperature, humidity, wind speed, and atmospheric pressure) with a sampling interval of <b>0.01 seconds</b>, ensuring high temporal resolution. The <b>Isolated Data</b> folder further separates normal and anomaly samples to enable focused comparative analysis, while the <b>GIS Data</b> folder contains QGIS and elevation files for spatial and topographical visualization.This well-structured organization ensures seamless integration of sensor, video, geographic, and environmental data, supporting efficient navigation and in-depth multimodal research. The <b>raw data</b> are hosted separately on Google Drive and can be accessed via the following link:<br>🔗 https://drive.google.com/drive/folders/16tRSgXy6bjgIcJZzdw3U5unw7jpsKAHB?usp=drive_link