cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nt6byf
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-104.21159 21.18161,-104.02524 21.20329,-103.97164 21.25174,-103.90008 21.23354,-103.81891 21.25681,-103.74243 21.38185,-103.76298 21.39435,-103.74968 21.41679,-103.72588 21.41706,-103.77461 21.50246,-103.82223 21.5019,-103.90935 21.64729,-103.88627 21.70171,-103.91807 21.78091,-104.13677 21.81305,-104.16104 21.83819,-104.15484 21.87648,-104.18249 21.89838,-104.16677 21.9814,-104.2217 21.99972,-104.25899 21.98326,-104.2799 22.00842,-104.36139 21.98172,-104.31449 22.23083,-104.27127 22.30472,-104.27252 22.37795,-104.36597 22.43388,-104.51571 22.38055,-104.59974 22.4747,-104.64446 22.4835,-104.64985 22.58214,-104.74585 22.58047,-104.76808 22.6597,-104.88156 22.67036,-104.97792 22.52839,-104.97942 22.59525,-105.02873 22.65162,-105.03274 22.67703,-104.87251 22.72787,-104.93163 22.76179,-104.92624 22.82879,-104.98491 22.84041,-104.97919 22.89148,-105.01098 22.92909,-104.99093 22.95497,-105.32465 22.95772,-105.3621 23.07796,-105.41355 23.07046,-105.41265 23.03544,-105.44344 23.02839,-105.44883 22.97093,-105.5072 22.96643,-105.47099 22.89716,-105.49404 22.85842,-105.52628 22.77805,-105.50274 22.66709,-105.40979 22.65642,-105.39053 22.57403,-105.26042 22.5832,-105.20083 22.53029,-105.06543 22.6063,-105.00977 22.57236,-105.00084 22.48016,-105.05827 22.444,-104.98001 22.16206,-104.84946 22.13269,-104.81917 21.99311,-104.90605 21.90551,-104.93083 21.7904,-104.84896 21.78875,-104.74081 21.8353,-104.52585 21.46963,-104.48505 21.4703,-104.48416 21.42255,-104.34514 21.24965,-104.34514 21.24965,-104.34514 21.24965,-104.21159 21.18161)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is b554c320-0560-11d8-b851-b8a03c50a862",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 25 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021476-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ek4gfr
title: Occurrence Download

description: A dataset containing 606364 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Illicium simonsii, Illicium wardii, Illigera khasiana, Impatiens acuminata, Impatiens amphorata, Impatiens amplexicaulis, Impatiens angustiflora, Impatiens apsotis, Impatiens arguta, Impatiens arunensis, Impatiens bahanensis, Impatiens bajurenses, Impatiens bella, Impatiens benthamii, Impatiens bicolor, Impatiens bicornuta, Impatiens brachycentra, Impatiens bracteata, Impatiens bracteolata, Impatiens cathcartii, Impatiens chinensis, Impatiens conaensis, Impatiens cothurnoides, Impatiens crassicaudex, Impatiens cymbifera, Impatiens decipiens, Impatiens delavayi, Impatiens discolor, Impatiens drepanophora, Impatiens edgeworthii, Impatiens exilis, Impatiens falcifer, Impatiens florigera, Impatiens fragicolor, Impatiens gamblei, Impatiens gammiei, Impatiens glandulifera, Impatiens glauca, Impatiens gorepaniensis, Impatiens graciliflora, Impatiens harae, Impatiens hobsonii, Impatiens inayatii, Impatiens infirma, Impatiens infundibularis, Impatiens insignis, Impatiens jurpia, Impatiens kathmanduensis, Impatiens kharensis, Impatiens khasiana, Impatiens kingii, Impatiens laevigata, Impatiens laxiflora, Impatiens leptocarpa, Impatiens leptoceras, Impatiens linghziensis, Impatiens longipes, Impatiens mallae, Impatiens margaritifera humilis, Impatiens margaritifera margaritifera, Impatiens margaritifera purpurascens, Impatiens medogensis, Impatiens mishmiensis, Impatiens nubigena, Impatiens nyimana, Impatiens occultans, Impatiens porrecta, Impatiens prainii, Impatiens puberula, Impatiens pulchra, Impatiens purpurea, Impatiens racemosa ecalcarata, Impatiens racemosa racemosa, Impatiens racemulosa, Impatiens radiata, Impatiens reidii, Impatiens scabrida, Impatiens scitula, Impatiens scullyi, Impatiens serrata, Impatiens serratifolia, Impatiens spirifer, Impatiens stenantha, Impatiens sulcata, Impatiens sunkoshiensis, Impatiens tenuibracteata, Impatiens thomsonii, Impatiens trilobata, Impatiens tripetala, Impatiens tuberculata, Impatiens uncipetala, Impatiens urticifolia, Impatiens wallichii, Impatiens williamsii, Imperata cylindrica cylindrica, Incarvillea arguta, Incarvillea beresowskii, Incarvillea compacta, Incarvillea diffusa)"
 ]
}

 The dataset includes 606364 records from 1223 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042074-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndxvv6
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Felis bieti",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028196-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cfp5um
title: Occurrence Download

description: A dataset containing 640124 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to -5.914722",
 "DecimalLatitude is less than or equal to -0.914722",
 "DecimalLongitude is greater than or equal to -80.0",
 "DecimalLongitude is less than or equal to -75.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 640124 records from 304 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028149-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17815390
title: Discordance between online information and male hypogonadism clinical guidelines: a global multilingual content analysis - Supplemental Material

description: Supplemental material
DOI: 10.5281/zenodo.17815389
title: Discordance between online information and male hypogonadism clinical guidelines: a global multilingual content analysis - Supplemental Material

description: Supplemental material
DOI: 10.6084/m9.figshare.30787730
title: <b>Urban Multi-UAV Path Planning Simulation Dataset (2-D Dynamic Urban MEC Scenarios)</b>

description: This dataset accompanies the manuscript “PAIR: A Hybrid A* with PPO Path Planner for Multi-UAV Navigation in 2-D Dynamic Urban MEC Environments” by Bahaa Hussien Taher et al., submitted to the MDPI journal <i>Drones</i>. It provides a reproducible benchmark for evaluating multi-UAV path planning algorithms in cluttered, dynamic urban airspace.The dataset consists of nine procedurally generated 2-D urban scenarios on a 100×100 grid. Each scenario includes (i) static obstacles with three density levels (approximately 15%, 25%, and 40%), (ii) two spatial regimes (“clustered” and “dispersed”), (iii) static or Markovian dynamic obstacles (Move / Pause / Detour states), and (iv) start–goal positions for nine UAVs. Dynamic scenarios contain trajectories for up to 30 moving obstacles over 1,000 time steps.Data are provided as CSV tables (scenarios.csv, static_obstacles.csv, uav_positions.csv, dynamic_obstacles.csv) and optionally as a single JSON file aggregating all scenarios. These files allow users to benchmark classical planners (A*, D* Lite, CBS–D*), metaheuristics (PSO), and learning-based planners (e.g., PPO-based PAIR) on metrics such as mission success rate, travel time, energy surrogate, and unified path-quality score.A plain-text README file in the repository documents the file structure, field definitions, recommended Python environment, and an example workflow for loading the data and running simulations.
DOI: 10.6084/m9.figshare.30787730.v1
title: <b>Urban Multi-UAV Path Planning Simulation Dataset (2-D Dynamic Urban MEC Scenarios)</b>

description: This dataset accompanies the manuscript “PAIR: A Hybrid A* with PPO Path Planner for Multi-UAV Navigation in 2-D Dynamic Urban MEC Environments” by Bahaa Hussien Taher et al., submitted to the MDPI journal <i>Drones</i>. It provides a reproducible benchmark for evaluating multi-UAV path planning algorithms in cluttered, dynamic urban airspace.The dataset consists of nine procedurally generated 2-D urban scenarios on a 100×100 grid. Each scenario includes (i) static obstacles with three density levels (approximately 15%, 25%, and 40%), (ii) two spatial regimes (“clustered” and “dispersed”), (iii) static or Markovian dynamic obstacles (Move / Pause / Detour states), and (iv) start–goal positions for nine UAVs. Dynamic scenarios contain trajectories for up to 30 moving obstacles over 1,000 time steps.Data are provided as CSV tables (scenarios.csv, static_obstacles.csv, uav_positions.csv, dynamic_obstacles.csv) and optionally as a single JSON file aggregating all scenarios. These files allow users to benchmark classical planners (A*, D* Lite, CBS–D*), metaheuristics (PSO), and learning-based planners (e.g., PPO-based PAIR) on metrics such as mission success rate, travel time, energy surrogate, and unified path-quality score.A plain-text README file in the repository documents the file structure, field definitions, recommended Python environment, and an example workflow for loading the data and running simulations.
DOI: 10.5281/zenodo.17815476
title: Global Procurement Research Dataset (GPRD): Cross-Country Procurement Data and Causal Analysis Toolkit

description: The Global Procurement Research Dataset (GPRD) harmonizes 2.3+ million public procurement contract records from Ukraine (ProZorro), Colombia (SECOP), and the UK (Contracts Finder) using the Open Contracting Data Standard (OCDS) v1.1.

This dataset supports causal research on how procurement rules affect competition, prices, quality, and innovation. Key features:

**Data Sources:**
- Ukraine ProZorro: 1.2M contracts (2016-2024)
- Colombia SECOP II: 680K contracts (2015-2024)  
- UK Contracts Finder: 420K contracts (2016-2024)

**Identification Strategies:**
1. Regression Discontinuity Designs (RDD) at monetary thresholds where procurement rules change discretely
2. Difference-in-Differences (DiD) analysis of e-procurement reform adoption
3. Event studies with Callaway-Sant'Anna staggered adoption estimators

**Novel Features:**
- "Mechanism Index": NLP-based measure of contract restrictiveness, complexity, and innovation orientation from tender text
- PatentsView linkage for supplier innovation capacity
- Cross-country harmonized CPV/UNSPSC sector classifications
- Threshold indicators for EU and national procurement regulations

**Contents:**
1. src/ - Complete Python pipeline for data acquisition, harmonization, causal analysis
2. Data/ - Raw and processed procurement records from all three countries
3. DATA_CODEBOOK.md - Documentation for 80+ GPRD variables
4. Research_paper/paper.tex - Full manuscript (Nature Human Behaviour format)
5. requirements.txt - Python dependencies

**Key Findings:**
- 15.2pp increase in competitive bidding above publication thresholds
- 8.3% reduction in contract prices
- 23% lower text restrictiveness in above-threshold contracts
- Suppliers of above-threshold contracts hold 2.4× more patents

Associated manuscript targets Nature Human Behaviour / Nature Sustainability.
description: Data sources:
- Ukraine ProZorro: https://prozorro.gov.ua/en (Open data under Creative Commons)
- Colombia SECOP: https://www.colombiacompra.gov.co/secop-ii (Open data)
- UK Contracts Finder: https://www.gov.uk/contracts-finder (Open Government Licence)
- PatentsView: https://patentsview.org/ (Public domain, USPTO)

Code is MIT licensed. Data derivatives are CC-BY-4.0.
DOI: 10.5281/zenodo.17815477
title: Global Procurement Research Dataset (GPRD): Cross-Country Procurement Data and Causal Analysis Toolkit

description: The Global Procurement Research Dataset (GPRD) harmonizes 2.3+ million public procurement contract records from Ukraine (ProZorro), Colombia (SECOP), and the UK (Contracts Finder) using the Open Contracting Data Standard (OCDS) v1.1.

This dataset supports causal research on how procurement rules affect competition, prices, quality, and innovation. Key features:

**Data Sources:**
- Ukraine ProZorro: 1.2M contracts (2016-2024)
- Colombia SECOP II: 680K contracts (2015-2024)  
- UK Contracts Finder: 420K contracts (2016-2024)

**Identification Strategies:**
1. Regression Discontinuity Designs (RDD) at monetary thresholds where procurement rules change discretely
2. Difference-in-Differences (DiD) analysis of e-procurement reform adoption
3. Event studies with Callaway-Sant'Anna staggered adoption estimators

**Novel Features:**
- "Mechanism Index": NLP-based measure of contract restrictiveness, complexity, and innovation orientation from tender text
- PatentsView linkage for supplier innovation capacity
- Cross-country harmonized CPV/UNSPSC sector classifications
- Threshold indicators for EU and national procurement regulations

**Contents:**
1. src/ - Complete Python pipeline for data acquisition, harmonization, causal analysis
2. Data/ - Raw and processed procurement records from all three countries
3. DATA_CODEBOOK.md - Documentation for 80+ GPRD variables
4. Research_paper/paper.tex - Full manuscript (Nature Human Behaviour format)
5. requirements.txt - Python dependencies

**Key Findings:**
- 15.2pp increase in competitive bidding above publication thresholds
- 8.3% reduction in contract prices
- 23% lower text restrictiveness in above-threshold contracts
- Suppliers of above-threshold contracts hold 2.4× more patents

Associated manuscript targets Nature Human Behaviour / Nature Sustainability.
description: Data sources:
- Ukraine ProZorro: https://prozorro.gov.ua/en (Open data under Creative Commons)
- Colombia SECOP: https://www.colombiacompra.gov.co/secop-ii (Open data)
- UK Contracts Finder: https://www.gov.uk/contracts-finder (Open Government Licence)
- PatentsView: https://patentsview.org/ (Public domain, USPTO)

Code is MIT licensed. Data derivatives are CC-BY-4.0.
DOI: 10.6084/m9.figshare.30787979
title: PARPI: Supplementary_tables

description: Supplementary tables from PARPI: A long-read plastome assembler that is reference- and platform-independent
DOI: 10.6084/m9.figshare.30787979.v1
title: PARPI: Supplementary_tables

description: Supplementary tables from PARPI: A long-read plastome assembler that is reference- and platform-independent
DOI: 10.15468/dl.sz7kap
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Drexel University, Academy of Natural Sciences - Algae",
 "Country is Croatia"
 ]
}

 The dataset includes 81 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028197-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tsajmt
title: Occurrence Download

description: A dataset containing 1805 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Historical landings from the Rijeka (Croatia) fish market between 1914 and 1932",
 "Country is Croatia"
 ]
}

 The dataset includes 1805 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028198-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/tnw3ud
title: PAN-00178052 - pseudo coin brooch cast group B

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00178052&lt;/p&gt;
description: Modified: 2025-09-04T14:39:35+02:00
DOI: 10.5281/zenodo.17788624
title: Data from: Species identity and spatial scale drive context‑dependent tree diversity effects in a Finnish forest experiment

description: Tree species diversity is known to affect tree growth and leaf traits, which in turn can influence various ecosystem processes. However, the reported direction of these tree diversity effects is inconsistent, indicating that their outcomes depend strongly on ecological context. Using the long-term Satakunta forest diversity experiment in Finland, we investigated how the effects of tree species diversity on growth and leaf traits vary with species identity, stand density, and spatial scale.

By comparing the responses of light-demanding Scots pine (Pinus sylvestris) and shade-tolerant Norway spruce (Picea abies), we show that the factors influencing diameter at breast height (DBH) and leaf traits differed between species with contrasting life-history strategies. Pine needle terpene concentrations were lowest in neighbourhoods dominated by conspecifics, while spruce needle terpenes were unaffected by tree species composition. Increasing canopy cover reduced spruce dry needle mass but had no effect on the dry needle mass of pines. Likewise, the factors that influenced tree growth differed between species; spruce DBH was lowest in thinned stands but was unaffected by tree species composition, whereas pine DBH did not vary significantly with plot density but was reduced in neighbourhoods containing silver birch (Betula pendula).

Our findings also indicate that diversity effects on leaf traits primarily operate at the local scale, as statistical models assessing the effect of immediate neighbours on pine terpenes yielded significant results, whereas models assessing diversity effects on a plot-level did not. In contrast, both pine and spruce DBH responded to plot and immediate-neighbour level factors, implying that tree growth is influenced by broader stand-level conditions, while leaf traits responses to diversity are more localised.
DOI: 10.5281/zenodo.16738986
title: Data from: Species identity and spatial scale drive context‑dependent tree diversity effects in a Finnish forest experiment

description: Tree species diversity is known to affect tree growth and leaf traits, which in turn can influence various ecosystem processes. However, the reported direction of these tree diversity effects is inconsistent, indicating that their outcomes depend strongly on ecological context. Using the long-term Satakunta forest diversity experiment in Finland, we investigated how the effects of tree species diversity on growth and leaf traits vary with species identity, stand density, and spatial scale.

By comparing the responses of light-demanding Scots pine (Pinus sylvestris) and shade-tolerant Norway spruce (Picea abies), we show that the factors influencing diameter at breast height (DBH) and leaf traits differed between species with contrasting life-history strategies. Pine needle terpene concentrations were lowest in neighbourhoods dominated by conspecifics, while spruce needle terpenes were unaffected by tree species composition. Increasing canopy cover reduced spruce dry needle mass but had no effect on the dry needle mass of pines. Likewise, the factors that influenced tree growth differed between species; spruce DBH was lowest in thinned stands but was unaffected by tree species composition, whereas pine DBH did not vary significantly with plot density but was reduced in neighbourhoods containing silver birch (Betula pendula).

Our findings also indicate that diversity effects on leaf traits primarily operate at the local scale, as statistical models assessing the effect of immediate neighbours on pine terpenes yielded significant results, whereas models assessing diversity effects on a plot-level did not. In contrast, both pine and spruce DBH responded to plot and immediate-neighbour level factors, implying that tree growth is influenced by broader stand-level conditions, while leaf traits responses to diversity are more localised.
DOI: 10.15468/dl.mjhh39
title: Occurrence Download

description: A dataset containing 2561 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Luciola"
 ]
}

 The dataset includes 2561 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028199-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17788265
title: Data for Cosmic CO and [CII] Backgrounds and the Fueling of Star Formation over 12 Gyr

description: Data for Cosmic CO and [CII] Backgrounds and the Fueling of Star Formation over 12 Gyr

We provide empirical tomographic measurements of the cosmic CO and [CII] line backgrounds arising from the aggregate emission of galaxies and potentially diffuse gas in large-scale structure. Previously, only the most luminous galaxies were detected in these lines, revealing just the tip of the iceberg. Using the intensity mapping method, we measure the total background integrated over the entire line-emitting populations and report the cosmic mean. These measurements are based on detections of line excesses above the cosmic infrared background (CIB) continuum—at 7σ significance for CO and 3σ for [CII]—using CIB–galaxy correlations over 0 < z < 4.2, originally measured in Chiang et al. (2025, ApJ, 992, 1, 65). We use these redshift-dependent, multi-band correlation amplitudes to isolate the line contributions, which are released here.

The [CII] background corresponds to the 158 µm fine-structure line. The CO background includes the nine rotational transitions from J = 1–0 to 9–8, beginning at 115.27 GHz with equal spacing in frequency. Two sets of line posteriors are provided:

1. Comoving luminosity density   "rho_CO_CII--dense_z_fiducial.ecsv" gives the fiducial full-model posterior, sampled in dense    redshift bins over 0 < z < 10.   "rho_CO_CII--8_z_bins.ecsv" gives refits with independent line amplitudes in 8 redshift bins    within the range probed by the data (0 < z < 4.2), with all other nuisance parameters fixed to    their global best-fit values.    Both files are in units of L_sun/Mpc^3.

2. Monopole   "Tb_cosmic_CO_CII.ecsv" provides brightness temperatures in microkelvin (µK).   "I_nu_cosmic_CO_CII.ecsv" provides intensities in Jy/sr.   The two are equivalent. Both are derived from the full-model posterior, sampled densely over    0 < z < 10 and tabulated across observed frequency in GHz. Best-fit values correspond to    posterior medians, with lower and upper bounds indicating the 68% credible interval.

This data release serves as an empirical baseline for future line intensity mapping efforts and forecasts, and as a reference for comparison with galaxy surveys in astrophysical studies.