cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17539098
title: Replication Package for "Modeling OSS Popularity and Survival: A Non-Linear and Structural Analysis of GitHub Projects"

description: This replication package accompanies the manuscript titled “Modeling OSS Popularity and Survival: A Non-Linear and Structural Analysis of GitHub Projects”. It includes curated datasets, preprocessing scripts, and modeling pipelines used to investigate the non-linear dynamics of OSS popularity and the structural predictors of project survival. The dataset comprises over 60,000 GitHub repositories sampled across multiple time windows (3m, 6m, 9m, 12m), with features spanning development activity, engagement metrics, readability proxies, and documentation presence. All experiments were conducted using Python 3.11 with reproducible scripts and fixed random seeds. The name field has been removed to preserve project neutrality and avoid reputational bias. This package enables full replication of the GAM and Weibull AFT models presented in the manuscript.
DOI: 10.5281/zenodo.17539126
title: Replication Package for "Modeling OSS Popularity and Survival: A Non-Linear and Structural Analysis of GitHub Projects"

description: This replication package accompanies the manuscript titled “Modeling OSS Popularity and Survival: A Non-Linear and Structural Analysis of GitHub Projects”. It includes curated datasets, preprocessing scripts, and modeling pipelines used to investigate the non-linear dynamics of OSS popularity and the structural predictors of project survival. The dataset comprises over 60,000 GitHub repositories sampled across multiple time windows (3m, 6m, 9m, 12m), with features spanning development activity, engagement metrics, readability proxies, and documentation presence. All experiments were conducted using Python 3.11 with reproducible scripts and fixed random seeds. The name field has been removed to preserve project neutrality and avoid reputational bias. This package enables full replication of the GAM and Weibull AFT models presented in the manuscript.
DOI: 10.15468/dl.n2yxks
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76826 24.46013, 88.69111700000001 24.454553, 88.615893 24.437961, 88.54445800000001 24.410766, 88.47858600000001 24.373644, 88.41990800000001 24.327518, 88.36987000000001 24.273533, 88.329702 24.213026, 88.300383 24.147495, 88.282622 24.07856, 88.276837 24.007922, 88.283152 23.937323, 88.301391 23.868498, 88.33108900000001 23.803138, 88.37150099999999 23.742847, 88.421623 23.689101, 88.480217 23.643214, 88.545846 23.606309, 88.616901 23.579285, 88.691647 23.562803, 88.76826 23.557264, 88.84487300000001 23.562803, 88.919619 23.579285, 88.990674 23.606309, 89.056303 23.643214, 89.114897 23.689101, 89.165019 23.742847, 89.205431 23.803138, 89.235129 23.868498, 89.25336799999999 23.937323, 89.259683 24.007922, 89.25389800000001 24.07856, 89.236137 24.147495, 89.206818 24.213026, 89.16665 24.273533, 89.116612 24.327518, 89.057934 24.373644, 88.992062 24.410766, 88.920627 24.437961, 88.845403 24.454553, 88.76826 24.46013))"},{"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 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004616-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y74x3g
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocalamus giganteus",
 "Year 1950-2020"
 ]
}

 The dataset includes 45 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004617-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17809516
title: Data for Luminosity function of quasars at 1.0 < z < 3.5 from SDSS and DESI

description: Supplementary material to DESI's publication TITLE to comply with the data management plan. Data can be used to draw figures in articles. 

The files MyTable(Stripe82No4flags)***.fits and MyTable(Fall&SpringNo4flags)***.fits provide photometric information for three distinct datasets of point sources:(1) quasars,(2) stars or galaxies, and(3) point sources without any spectroscopic identification.

These two files correspond to the deep region and the wide region, respectively, and are used to produce Figure~1 of the paper.

The file my_boss_dr9qlf_sim***.fits contains the photometric information of simulated quasars and is used to generate Figure~2.

The files MyStripe82QLF***.fits and MyFall&SpringQLF***.fits provide the quasar luminosity function (QLF) data, which are used to produce Figures~3 and~4.

The file rho_data.csv contains the data for the cumulative density evolution of quasars and is used to produce Figure~5.
DOI: 10.5281/zenodo.17809515
title: Data for Luminosity function of quasars at 1.0 < z < 3.5 from SDSS and DESI

description: Supplementary material to DESI's publication TITLE to comply with the data management plan. Data can be used to draw figures in articles. 

The files MyTable(Stripe82No4flags)***.fits and MyTable(Fall&SpringNo4flags)***.fits provide photometric information for three distinct datasets of point sources:(1) quasars,(2) stars or galaxies, and(3) point sources without any spectroscopic identification.

These two files correspond to the deep region and the wide region, respectively, and are used to produce Figure~1 of the paper.

The file my_boss_dr9qlf_sim***.fits contains the photometric information of simulated quasars and is used to generate Figure~2.

The files MyStripe82QLF***.fits and MyFall&SpringQLF***.fits provide the quasar luminosity function (QLF) data, which are used to produce Figures~3 and~4.

The file rho_data.csv contains the data for the cumulative density evolution of quasars and is used to produce Figure~5.
DOI: 10.15468/dl.4uz95e
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74391 24.46051, 88.66676699999999 24.454933, 88.591542 24.438341, 88.52010799999999 24.411146, 88.454235 24.374024, 88.395557 24.327898, 88.345519 24.273913, 88.305351 24.213406, 88.276032 24.147875, 88.25827 24.07894, 88.252486 24.008302, 88.25879999999999 23.937703, 88.27704 23.868878, 88.306738 23.803518, 88.34715 23.743227, 88.397272 23.689481, 88.455867 23.643594, 88.521495 23.606689, 88.59255 23.579665, 88.667297 23.563183, 88.74391 23.557644, 88.82052299999999 23.563183, 88.89527 23.579665, 88.966325 23.606689, 89.031953 23.643594, 89.090548 23.689481, 89.14067 23.743227, 89.181082 23.803518, 89.21078 23.868878, 89.22902000000001 23.937703, 89.23533399999999 24.008302, 89.22955 24.07894, 89.211788 24.147875, 89.182469 24.213406, 89.142301 24.273913, 89.092263 24.327898, 89.033585 24.374024, 88.96771200000001 24.411146, 88.896278 24.438341, 88.82105300000001 24.454933, 88.74391 24.46051))"},{"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 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004620-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mtasxf
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Morocco",
 "OccurrenceStatus is Present",
 "TaxonKey is Erica ciliaris"
 ]
}

 The dataset includes 44 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004621-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18327178
title: PYVOA - GEO 7

description: Blabla
DOI: 10.5281/zenodo.18327066
title: PYVOA - GEO 6

description: Blabla
DOI: 10.15468/dl.3nmtfz
title: Occurrence Download

description: A dataset containing 292 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (CAN.11.9_1, CAN.11.42_1, CAN.11.90_1, CAN.11.62_1, CAN.11.12_1, CAN.11.23_1, CAN.11.64_1, CAN.11.54_1, CAN.11.29_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Spartina pectinata, Spartina pectinata, Spartina pectinata pectinata, Spartina pectinata variegata, Spartina pectinata suttiei, Spartina pectinata pectinata, Sporobolus michauxianus, Spartina, Deschampsia cespitosa, Deschampsia cespitosa anadyrensis, Alnus incana, Triantha glutinosa, Prunus pumila, Prunus depressa, Triantha glutinosa glutinosa, Tofieldia glutinosa, Tofieldia glutinosa glutinosa, Tofieldia, Tofieldia, Solidago simplex, Arabis lyrata, Anemone virginiana, Anemone riparia, Schizachyrium scoparium, Danthonia spicata compressa, Packera paupercula, Campanula rotundifolia, Andropogon gerardii gerardii, Danthonia, Arabis, Andropogon, Anemone virginiana cylindroidea, Andropogon scoparius septentrionalis, Senecio pauperculus, Senecio balsamitae pauperculus, Solidago racemosa)"
 ]
}

 The dataset includes 292 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004622-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vqx3nv
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "Geometry POLYGON((-99.52039 19.53085,-97.70167 19.53085,-97.70167 21.97003,-99.52039 21.97003,-99.52039 19.53085))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Balantiopteryx plicata"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004623-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/jt1grb
title: Electrophoretically deposited nano TiO2/Ag powder to protect fuel cell Ni cathode from dissolution in molten carbonate

description: Data deposited in the form of an Excel file containing precise numerical results of performance test (current-voltage dependence), electrical conductivity measurement, and solubility tests.The functional properties of a porous nickel cathode from MCFC fuel cell produced by tape casting and reinforced with Ni foam were enhanced by electrophoretic deposition (EPD) of TiO2/Ag particles. The obtained results demonstrate a significant improvement in cell efficiency, with power output increasing from 90 mW/cm2 for the reference Ni cathode to 100 mW/cm2 for the modified cathode. Furthermore, cathode solubility was reduced by 300 %.
DOI: 10.17026/ls/ratczg
title: Replication Data for: Drivers and mechanisms of ecosystem multifunctionality in secondary tropical forests

description: Data belonging to the manuscript "Drivers and mechanisms of ecosystem multifunctionality in secondary tropical forests" authored by Tomonari Matsuo, Lucy Amissah, Masha T. van der Sande, Fons van der Plas, Jazz Kok, Salim Mohammed Abdul, Lucas Chojnacki, Tijs Kuzee, Lhouyangdar Khulpu, and Lourens Poorter. Published in Ecosystems in 2026.
DOI: 10.15468/dl.rs4fpq
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74391 24.46051, 88.66676699999999 24.454933, 88.591542 24.438341, 88.52010799999999 24.411146, 88.454235 24.374024, 88.395557 24.327898, 88.345519 24.273913, 88.305351 24.213406, 88.276032 24.147875, 88.25827 24.07894, 88.252486 24.008302, 88.25879999999999 23.937703, 88.27704 23.868878, 88.306738 23.803518, 88.34715 23.743227, 88.397272 23.689481, 88.455867 23.643594, 88.521495 23.606689, 88.59255 23.579665, 88.667297 23.563183, 88.74391 23.557644, 88.82052299999999 23.563183, 88.89527 23.579665, 88.966325 23.606689, 89.031953 23.643594, 89.090548 23.689481, 89.14067 23.743227, 89.181082 23.803518, 89.21078 23.868878, 89.22902000000001 23.937703, 89.23533399999999 24.008302, 89.22955 24.07894, 89.211788 24.147875, 89.182469 24.213406, 89.142301 24.273913, 89.092263 24.327898, 89.033585 24.374024, 88.96771200000001 24.411146, 88.896278 24.438341, 88.82105300000001 24.454933, 88.74391 24.46051))"},{"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 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004624-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18327569
title: WP5.1 Stochastic and Empirical Multi-Hazard Event Sets for Europe: Tornado Stochastic Event Set for Europe

description: Stochastic event sets of possible tornadoes in Europe from proprietary modeling integrating the historical return periods from the RAIN-project and the historical rates of tornadoes across Europe from the NARSIS project.

The data is delivered in GEOJSON (referenced to the EPSG:4326 coordinate system) and CSV format and contains detailed attributes for each simulated track. Each record includes the following parameters:



Spatial Extent: Start and end coordinates provided in decimal degrees (start_latitude, start_longitude, end_latitude, end_longitude).

Dimensions: Path length and path width (path_length, path_width) measured in kilometers (km).

Intensity: Tornado magnitude classified according to the Fujita Scale (tornado_intensity), accompanied by the peak estimated wind speed in km/h.
DOI: 10.5281/zenodo.18327570
title: WP5.1 Stochastic and Empirical Multi-Hazard Event Sets for Europe: Tornado Stochastic Event Set for Europe

description: Stochastic event sets of possible tornadoes in Europe from proprietary modeling integrating the historical return periods from the RAIN-project and the historical rates of tornadoes across Europe from the NARSIS project.

The data is delivered in GEOJSON (referenced to the EPSG:4326 coordinate system) and CSV format and contains detailed attributes for each simulated track. Each record includes the following parameters:



Spatial Extent: Start and end coordinates provided in decimal degrees (start_latitude, start_longitude, end_latitude, end_longitude).

Dimensions: Path length and path width (path_length, path_width) measured in kilometers (km).

Intensity: Tornado magnitude classified according to the Fujita Scale (tornado_intensity), accompanied by the peak estimated wind speed in km/h.
DOI: 10.21227/a2ad-d703
title: "A Beginner's Guide to Drifting with Drift Hunters"

description: "So, you've heard about the thrill of drifting, the controlled chaos of sending a car sideways through a corner, and you're itching to try it out. Maybe you don't have access to a real-life drift car, or maybe you just want a low-stakes way to learn the basics. That's where virtual drifting games come in, and one particularly accessible and addictive option is drift hunters.This free-to-play browser game offers a surprising amount of depth and fun, allowing you to experience the joys of drifting without leaving your seat. This isn't a graphically intensive racing sim, but it offers a satisfyingly simplistic gameplay loop that will keep you entertained. Let's dive into how to get started and some tips to help you become a virtual drift king.Getting Started: Picking Your Ride and Setting UpThe beauty of drift hunters is its accessibility. Head over to the website and you\u2019re immediately greeted with a selection of cars. You start with a basic, but functional, vehicle. Each car has its own stats for weight, horsepower, and handling. As you earn points by drifting, you can unlock a wide array of iconic drift cars, from Japanese classics to powerful European beasts. Experimenting with different cars is a key part of the fun, as each one handles slightly differently, requiring you to adjust your technique.Before you hit the track, familiarize yourself with the controls. They're pretty straightforward:Arrow keys\/WASD: Steering, acceleration, and braking.Spacebar: Handbrake. This is your best friend for initiating drifts.Shift: Gear up.Ctrl: Gear down.You can also adjust the camera angle to your preference for optimal visibility. Finding the right camera angle that suits your playstyle is crucial for consistently landing smooth drifts.The Gameplay: Drifting 101The core gameplay loop is simple: drift, earn points, upgrade cars, repeat. Points are awarded based on the angle, speed, and length of your drifts. The longer and more controlled your drift, the higher your score. Mastering the art of linking drifts together seamlessly is the key to racking up serious points.The game features a variety of tracks, each with its own unique challenges. From tight, technical circuits to wide-open touge runs, there's a track to suit every drifting style. Learning the layout of each track and identifying the best drifting spots is crucial for maximizing your score.The key to drifting in this game is to find the sweet spot between speed, angle, and control. Too much speed, and you'll spin out. Too little angle, and you won't get a good score. The handbrake is your primary tool for initiating drifts, but mastering throttle control and weight shifting is essential for maintaining them."
DOI: 10.5281/zenodo.18315428
title: Experimental data for battery swelling prediction under mechanical constraints

description: The dataset contains experimental results for fresh cells 01, 02, 03, and 04 measured at preload forces of 0 kN, 1.5 kN, 5 kN, and 15 kN, respectively. The log files include the cycling (charge/discharge) data, and the SP files contain the measurements of cell thickness variation.
DOI: 10.5281/zenodo.18315429
title: Experimental data for battery swelling prediction under mechanical constraints

description: The dataset contains experimental results for fresh cells 01, 02, 03, and 04 measured at preload forces of 0 kN, 1.5 kN, 5 kN, and 15 kN, respectively. The log files include the cycling (charge/discharge) data, and the SP files contain the measurements of cell thickness variation.
DOI: 10.15468/dl.wf5ujm
title: Occurrence Download

description: A dataset containing 221399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((98 25,101.5 25,101.5 25.17522,101.5 29.5,98 29.5,98 25))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1950-2026"
 ]
}

 The dataset includes 221399 records from 156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004597-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h3udtd
title: Occurrence Download

description: A dataset containing 273 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus cubensis"
 ]
}

 The dataset includes 273 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004625-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.826fqu
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocalamus hamiltonii"
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004626-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48804/kc6bfn
title: Comparing contact resistance of edge, top, and hybrid contacted two-dimensional materials

description: We theoretically investigate the performance and scalability of edge, top, and hybrid contacts to MoS2 monolayers. We compare the contact resistance for different contact geometries while analyzing the impact of parameters such as doping concentration, surrounding dielectrics, metal work functions, van-der-Waals gap thickness, and contact length. Our model uses a quantum transport approach including important mechanisms such as image-force barrier lowering to model the barriers in different geometries.
DOI: 10.6084/m9.figshare.30744218
title: MetImputBERT

description: MetImputBERT is a python tool for imputing missing values in NMR metabolomics data using a pretrained transformer-based model.
DOI: 10.6084/m9.figshare.30744218.v2
title: MetImputBERT

description: MetImputBERT is a python tool for imputing missing values in NMR metabolomics data using a pretrained transformer-based model.
DOI: 10.6084/m9.figshare.30744218.v1
title: MetImputBERT

description: MetImputBERT is a python tool for imputing missing values in NMR metabolomics data using a pretrained transformer-based model.
DOI: 10.6084/m9.figshare.31123213
title: Supplementary Material for: Alcohol Use and Hidradenitis Suppurativa: An Unclear Relationship

description: Hidradenitis suppurativa (HS) may be linked to behavioral factors that exacerbate inflammation, gut microbiome, and healing. This review evaluates current evidence on the relationship between alcohol consumption and HS. Emerging studies show high incidences of alcohol and substance use disorders in HS patients. However, observational studies remain inconsistent: HS patients may experience a higher alcohol-related burden, yet its association to disease progression and baseline severity remains unclear. Limitations of existing studies include self-reported exposures of alcohol, heterogeneous outcome measures, and potential confounding factors, such as stress. Biologic plausibility remains, as alcohol can promote dysbiosis, inflammation, and oxidative stress that may influence disease activity and healing. This review highlights the need for larger, controlled trials that determine whether the reduction or elimination of alcohol may improve HS outcomes.