cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.t7nqkm
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.749202 24.282509, 88.672166 24.276932, 88.597047 24.26034, 88.525712 24.233145, 88.459931 24.196024, 88.40133299999999 24.149898, 88.35136300000001 24.095912, 88.311249 24.035405, 88.281969 23.969873, 88.26423 23.900937, 88.25845 23.830298, 88.264754 23.759696, 88.282967 23.690869, 88.312623 23.625508, 88.35297799999999 23.565214, 88.403031 23.511466, 88.461546 23.465577, 88.527086 23.42867, 88.598046 23.401645, 88.672691 23.385162, 88.749202 23.379622, 88.82571299999999 23.385162, 88.900358 23.401645, 88.971318 23.42867, 89.036858 23.465577, 89.095373 23.511466, 89.145426 23.565214, 89.18578100000001 23.625508, 89.21543699999999 23.690869, 89.23365 23.759696, 89.239954 23.830298, 89.234174 23.900937, 89.216435 23.969873, 89.187155 24.035405, 89.147041 24.095912, 89.097071 24.149898, 89.038473 24.196024, 88.972692 24.233145, 88.901357 24.26034, 88.826238 24.276932, 88.749202 24.282509))"},{"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/0006442-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/se4idp/e8srxq
title: grc1leg.hlp

description: :unav
DOI: 10.25625/se4idp/yjrftg
title: heatplot.pkg

description: :unav
DOI: 10.25625/se4idp/xr7fbi
title: Codebook_hh_members.tab

description: :unav
DOI: 10.25625/se4idp/kyv7sz
title: colorcheck.sthlp

description: :unav
DOI: 10.25625/se4idp/sce7kv
title: estout.ado

description: :unav
DOI: 10.25625/se4idp/mmneds
title: ivhettest.ado

description: :unav
DOI: 10.25625/se4idp/m6swwy
title: ivactest.hlp

description: :unav
DOI: 10.25625/se4idp/a8f9cv
title: endline.tab

description: :unav
DOI: 10.25625/se4idp/6jakjq
title: _eststo.hlp

description: :unav
DOI: 10.25625/se4idp/rdrw7u
title: ivreset.ado

description: :unav
DOI: 10.15468/dl.vgq729
title: Occurrence Download

description: A dataset containing 7907 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leibnitzia"
 ]
}

 The dataset includes 7907 records from 260 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006441-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/se4idp/1red91
title: coefplot.ado

description: :unav
DOI: 10.25625/se4idp/drvtcq
title: hexplot.sthlp

description: :unav
DOI: 10.25625/se4idp/vekcv8
title: Questionnaire_baseline.xlsx

description: :unav
DOI: 10.25625/se4idp/xi25x9
title: colorcheck.ado

description: :unav
DOI: 10.25625/se4idp/teomiz
title: owncodebook.ado

description: :unav
DOI: 10.25625/se4idp/ascsix
title: _eststo.ado

description: :unav
DOI: 10.25625/se4idp/koxtpt
title: stata.trk

description: :unav
DOI: 10.25625/se4idp/pbhko4
title: outreg_update.sthlp

description: :unav
DOI: 10.25625/se4idp/igmldr
title: frmttable.sthlp

description: :unav
DOI: 10.25625/se4idp/hwtabb
title: Codebook_endline.tab

description: :unav
DOI: 10.25625/se4idp/uukjcx
title: esttab.hlp

description: :unav
DOI: 10.25625/se4idp/g4a7vz
title: ivreg2_cue.ado

description: :unav
DOI: 10.25625/se4idp/5k9jmn
title: backup.trk

description: :unav
DOI: 10.5281/zenodo.18346585
title: SEM Withered Plant (BSE Series) — Kikoh Matsuura (Art–Science Archive)

description: Parent (series) record for *SEM Withered Plant (BSE)* by Kikoh Matsuura.  This record serves as the hub for all child datasets in the series.

**Files included:**- A series overview (TXT) documenting scope, naming rules, citation style, and file policy- A machine-readable index (CSV) that will be updated to list child datasets (DOIs, imaging parameters, etc.)- Withered Plant — Declaration (PDF) — issued once / no future revisions / no images- Withered Plant: Phase I (DOI 1–100) — Record (PDF) — fixed record / no future revisions

Each child dataset contains raw, uncompressed BMP images (≈10 per set, number varies) acquired with a personally owned TECHNEX TinySEM M3-i using the BSE detector under high-vacuum conditions.  Child records provide detailed READMEs with exact parameters (accelerating voltage, working distance, vacuum, pixel size, field of view, frame time, coating).

This parent provides a stable concept DOI for the entire *SEM Withered Plant (BSE)* series.  Child datasets link back via *IsPartOf*, and this parent enumerates them via *HasPart*.  

*This series visualizes the hidden order and chance in withered plants through SEM-BSE, leaving a unique trace at the boundary between art and science.*
DOI: 10.25625/se4idp/flxsg5
title: estadd.ado

description: :unav
DOI: 10.25625/se4idp/uhzh8p
title: _mrsvmat.hlp

description: :unav
DOI: 10.25625/se4idp/vcwhe2
title: symbolpalette.hlp

description: :unav
DOI: 10.25625/se4idp/fb6bf8
title: ranktest.ado

description: :unav
DOI: 10.25625/se4idp/xwxa7u
title: palettes.hlp

description: :unav
DOI: 10.25625/se4idp/d1zbe4
title: colorpalette.ado

description: :unav
DOI: 10.15468/dl.nc3vvz
title: Occurrence Download

description: A dataset containing 2571 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Girardinia"
 ]
}

 The dataset includes 2571 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006444-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/se4idp/2avpeh
title: qqvalue.sthlp

description: :unav
DOI: 10.25625/se4idp/bx6lc7
title: ivreg2.hlp

description: :unav
DOI: 10.25625/se4idp/vzccmw
title: colorpalette9.ado

description: :unav
DOI: 10.25625/se4idp/5jcmcr
title: estout.hlp

description: :unav
DOI: 10.25625/se4idp/wcrqhn
title: Replication_RCT.do

description: :unav
DOI: 10.25625/se4idp/dbspxl
title: ivreg2.ado

description: :unav
DOI: 10.25625/se4idp/weiiro
title: estpost.ado

description: :unav
DOI: 10.25625/se4idp/6zl5od
title: colorpalette9.hlp

description: :unav
DOI: 10.25625/se4idp/ngmfqb
title: colrspace_library_namedcolors.sthlp

description: :unav
DOI: 10.25625/se4idp/3cdybr
title: estpost.hlp

description: :unav
DOI: 10.25625/se4idp/0yle71
title: colrspace_library_rgbmaps.sthlp

description: :unav
DOI: 10.25625/se4idp/r8c97c
title: colrspace.sthlp

description: :unav
DOI: 10.25625/se4idp/xwi1v8
title: colrspace_library_lsmaps.sthlp

description: :unav
DOI: 10.25625/se4idp
title: Replication data for: The effect of SMS reminders on health screening uptake: A randomized experiment in Indonesia

description: Replication data for "The effect of SMS reminders on health screening uptake: A randomized experiment in Indonesia"
DOI: 10.25625/se4idp/susn6k
title: _README.txt

description: :unav
DOI: 10.15468/dl.zjfend
title: Occurrence Download

description: A dataset listing the 877 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "OccurrenceStatus is Present",
 "TaxonKey is Cambaridae"
 ]
}

 The dataset's 877 records were derived from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006424-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v6458f
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Oryza rufipogon",
 "Year 1970-2000"
 ]
}

 The dataset includes 63 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006445-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/1d3885ff-dc9c-4d05-9e4d-2d8f562d9c8d
title: Data underlying the MSc thesis "Extraction Sequence Optimization for the Dredge Mining of Heavy Mineral Sands: A Meta-Heuristic Approach"

description: This dataset contains the implementation of a genetic algorithm in Python developed during the MSc thesis "Extraction Sequence Optimization for the Dredge Mining of Heavy Mineral Sands: A Meta-Heuristic Approach" (January 2026).<br>The genetic algorithm optimizes the life of mine extraction sequence for dredge mined heavy mineral sands projects. This is done by dividing defined spatial extraction limits into mining periods. The optimization objective is the project value in the form of the NPV (net present value), a measure of value that takes into account the time value of money. The extraction sequence is subjected to the following constraints, sequences breaking these constraints are penalized: -) Spatially continuity: consecutive mining periods must be spatially adjacent -) Fixed offtake rate: fixed mass of delivered product per mining period -) Equipment capacity: maximum capacity per period of extraction equipment (dredger) and processing equipmentThe sequence is defined by a centroid for every mining period, with all blocks in the block model being associated with the closest period centroid. This way the extraction sequence takes the form of a voronoi diagram.
DOI: 10.4121/1d3885ff-dc9c-4d05-9e4d-2d8f562d9c8d.v1
title: Data underlying the MSc thesis "Extraction Sequence Optimization for the Dredge Mining of Heavy Mineral Sands: A Meta-Heuristic Approach"

description: This dataset contains the implementation of a genetic algorithm in Python developed during the MSc thesis "Extraction Sequence Optimization for the Dredge Mining of Heavy Mineral Sands: A Meta-Heuristic Approach" (January 2026).<br>The genetic algorithm optimizes the life of mine extraction sequence for dredge mined heavy mineral sands projects. This is done by dividing defined spatial extraction limits into mining periods. The optimization objective is the project value in the form of the NPV (net present value), a measure of value that takes into account the time value of money. The extraction sequence is subjected to the following constraints, sequences breaking these constraints are penalized: -) Spatially continuity: consecutive mining periods must be spatially adjacent -) Fixed offtake rate: fixed mass of delivered product per mining period -) Equipment capacity: maximum capacity per period of extraction equipment (dredger) and processing equipmentThe sequence is defined by a centroid for every mining period, with all blocks in the block model being associated with the closest period centroid. This way the extraction sequence takes the form of a voronoi diagram.
DOI: 10.15468/dl.zmqsu6
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((65.35277 43.24421,101.64723 43.24421,101.64723 57.75579,65.35277 57.75579,65.35277 43.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Papaver tenellum"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006447-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yaryt8
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Paeonia anomala"
 ]
}

 The dataset includes 80 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006446-260120142942310/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.30846809
title: Necoda-compressed datasets.

description: We present Necoda, a transformative deep learning framework that compresses functional neural imaging data by over 1,000-fold while faithfully preserving signal fidelity. This work addresses the critical "data gravity" bottleneck that currently impedes research reproducibility, data sharing, and collaborative discovery in neuroscience.To demonstrate real-world utility, we applied Necoda to 5.05TB large-scale, multi-modality datasets and form a 50.1 GB data capsule. One representative case is the Allen Brain Observatory visual coding dataset. We compressed 4.82 TB of ABO dataset into a portable 4.81 GB package, along with the analysis script.
DOI: 10.6084/m9.figshare.30846809.v2
title: Necoda-compressed Allen Brain Observatory calcium imaging dataset

description: We present Necoda, a transformative deep learning framework that compresses functional neural imaging data by over 1,000-fold while faithfully preserving signal fidelity. This work addresses the critical "data gravity" bottleneck that currently impedes research reproducibility, data sharing, and collaborative discovery in neuroscience.To demonstrate real-world utility, we applied Necoda to the Allen Brain Observatory visual coding dataset. We compressed 4.82 TB of data into a portable 4.81 GB package.
DOI: 10.6084/m9.figshare.30846809.v3
title: Necoda-compressed datasets.

description: We present Necoda, a transformative deep learning framework that compresses functional neural imaging data by over 1,000-fold while faithfully preserving signal fidelity. This work addresses the critical "data gravity" bottleneck that currently impedes research reproducibility, data sharing, and collaborative discovery in neuroscience.To demonstrate real-world utility, we applied Necoda to 5.05TB large-scale, multi-modality datasets and form a 50.1 GB data capsule. One representative case is the Allen Brain Observatory visual coding dataset. We compressed 4.82 TB of ABO dataset into a portable 4.81 GB package, along with the analysis script.
DOI: 10.15468/dl.yuehav
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((49.35277 37.24421,85.64723 37.24421,85.64723 51.75579,49.35277 51.75579,49.35277 37.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Paracaryum karataviense"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006448-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1285/i15900932v45s1p59
title: Some problems on intrinsically harmonic forms

description: In this short note we recall the definition of intrinsically harmonic forms, some known results and some open problems.
DOI: 10.15468/dl.zugw5v
title: Occurrence Download

description: A dataset containing 146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Angola",
 "TaxonKey is Danaus chrysippus"
 ]
}

 The dataset includes 146 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006449-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/xpc5x4
title: Abbassa tilapia strain comparison trial

description: Abbassa tilapia strain comparison trial. G15 compared with a commercial strain in earthen pond environment
description: Type: Dataset; Sub-type(s): Experimental data
DOI: 10.15468/dl.bfy662
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.83695299999999 24.326539, 88.759891 24.320962, 88.684746 24.30437, 88.61338600000001 24.277175, 88.547583 24.240053, 88.488964 24.193927, 88.43897800000001 24.139942, 88.39885 24.079434, 88.369561 24.013903, 88.351816 23.944967, 88.346036 23.874328, 88.35234199999999 23.803727, 88.37056200000001 23.734901, 88.400228 23.669539, 88.440597 23.609246, 88.490667 23.555499, 88.54920199999999 23.50961, 88.614763 23.472704, 88.68574700000001 23.445679, 88.760417 23.429196, 88.83695299999999 23.423657, 88.913489 23.429196, 88.988159 23.445679, 89.05914300000001 23.472704, 89.12470399999999 23.50961, 89.183239 23.555499, 89.23330900000001 23.609246, 89.273678 23.669539, 89.303344 23.734901, 89.321564 23.803727, 89.32787 23.874328, 89.32209 23.944967, 89.304345 24.013903, 89.27505600000001 24.079434, 89.234928 24.139942, 89.18494200000001 24.193927, 89.126323 24.240053, 89.06052 24.277175, 88.98916 24.30437, 88.91401500000001 24.320962, 88.83695299999999 24.326539))"},{"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/0006451-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.30320.16641/1
title: Dr Chrysanthus Chukwuma Sr Resume/CV
DOI: 10.15468/dl.72dfv3
title: Occurrence Download

description: A dataset containing 2528 records in GBIF matching the query:
SELECT
 '150' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 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;= -4.045127286108716
 AND decimalLatitude &lt;= 0.0
 AND decimalLongitude &gt;= -54.96226433444633
 AND decimalLongitude &lt;= -49.88887061126883
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 2528 records were derived from 188 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006436-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18342897
title: AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems

description: AnoMod is a multimodal anomaly dataset built on two open-source microservice systems, SocialNetwork and TrainTicket. The goal of the dataset is to support research on anomaly detection and fine-grained root cause analysis (RCA) in complex microservice architectures.

We design and inject four categories of anomalies—performance-level, service-level, database-level and code-level—to emulate realistic failure scenarios observed in production systems. For each anomaly case, we collect five data modalities:1. Logs: timestamped textual records from application containers capturing events, errors and contextual information.  2. Metrics: time-series measurements collected via Prometheus (CPU, memory, disk, network, request counts and process-level indicators).  3. Distributed traces: request-level call chains captured with Jaeger (for SocialNetwork) and SkyWalking (for TrainTicket), revealing service dependencies and latency.  4. API responses: client-side observations of HTTP status codes, latencies and response bodies, providing a black-box view of user-visible behaviour.  5. Code coverage reports: coverage information generated by gcov (C++) and JaCoCo (Java) linking runtime execution to source code lines and branches, enabling fine-grained RCA.

Data were collected using an automated three-phase pipeline: (i) EvoMaster generates workloads based on OpenAPI specifications to simulate realistic user requests; (ii) anomalies are injected using a predefined anomaly library with configurations adapted from ChaosMesh/ChaosBlade; (iii) for each anomaly case, the system is reset to a clean state, the anomaly is activated, the workload is executed and all five modalities are captured synchronously. Each anomaly run is saved in its own folder.

The dataset supports cross-modal anomaly detection, fusion/ablation studies and service/code-level RCA research. For detailed directory structure and instructions to access the data and scripts, see the README files in the GitHub repository. This dataset accompanies the paper ‘AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems’ published at MSR’26 (Rio de Janeiro, April 13–14, 2026).
DOI: 10.5281/zenodo.18342898
title: AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems

description: AnoMod is a multimodal anomaly dataset built on two open-source microservice systems, SocialNetwork and TrainTicket. The goal of the dataset is to support research on anomaly detection and fine-grained root cause analysis (RCA) in complex microservice architectures.

We design and inject four categories of anomalies—performance-level, service-level, database-level and code-level—to emulate realistic failure scenarios observed in production systems. For each anomaly case, we collect five data modalities:1. Logs: timestamped textual records from application containers capturing events, errors and contextual information.  2. Metrics: time-series measurements collected via Prometheus (CPU, memory, disk, network, request counts and process-level indicators).  3. Distributed traces: request-level call chains captured with Jaeger (for SocialNetwork) and SkyWalking (for TrainTicket), revealing service dependencies and latency.  4. API responses: client-side observations of HTTP status codes, latencies and response bodies, providing a black-box view of user-visible behaviour.  5. Code coverage reports: coverage information generated by gcov (C++) and JaCoCo (Java) linking runtime execution to source code lines and branches, enabling fine-grained RCA.

Data were collected using an automated three-phase pipeline: (i) EvoMaster generates workloads based on OpenAPI specifications to simulate realistic user requests; (ii) anomalies are injected using a predefined anomaly library with configurations adapted from ChaosMesh/ChaosBlade; (iii) for each anomaly case, the system is reset to a clean state, the anomaly is activated, the workload is executed and all five modalities are captured synchronously. Each anomaly run is saved in its own folder.

The dataset supports cross-modal anomaly detection, fusion/ablation studies and service/code-level RCA research. For detailed directory structure and instructions to access the data and scripts, see the README files in the GitHub repository. This dataset accompanies the paper ‘AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice Systems’ published at MSR’26 (Rio de Janeiro, April 13–14, 2026).