cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29827907.v1
title: Supplementary Material for: Conversion Ability of Immunotherapy in Hepatocellular Carcinoma: Insights from the International Converse Study

description: Introduction: The potential for curative conversion with immunotherapy-based systemic treatment used with noncurative intent in patients with hepatocellular carcinoma (HCC) remains debated. This study aimed to provide a reliable epidemiological snapshot of response patterns to Atezolizumab plus Bevacizumab (AB) therapy, with a focus on curative conversion rates. 
Methods: Patients with HCC undergoing first-line noncurative AB or Lenvatinib (LENV, used as reference) from 2019 to 2023 were included, using centre-level aggregate data from a broad international consortium. The primary endpoint was the curative conversion rate, differentiating potential conversion (PC) - when objective response (OR) resulted in a consistent decrease in tumour burden and alpha-fetoprotein levels - from actual conversion (AC), when OR led to curative treatment. Secondary endpoints included OR, under-conversion (UC; [PC - AC]/OR) rates, and crude survival rates of AC patients. A meta-analytic approach was employed to analyse aggregate data. 
Results: Forty-eight international centres treating 2,379 patients with HCC with a noncurative intent (1,401 with AB and 978 with LENV) were included. A significant discrepancy was observed between PC (16% and 13% for AB and LENV, p=0.03) and AC rates (3% for both AB and LENV, p=0.14). 
UC rates remained similarly high (40% and 36% for AB and LENV, p=0.93), despite differing OR rates (29% and 24% for AB and LENV, p=0.01). Subgroup and meta-regression analyses did not identify any clear treatment, centre, or patient patterns that explained the high UC rate. The 3-year survival rate for the 72 patients who underwent a curative conversion after AB was 93%. 
Conclusions: Although patients treated with AB achieved higher OR and PC rates than those treated with LENV, AC remained similarly low, highlighting a potentially worrisome UC phenomenon in real life, also with novel immunotherapy-based combinations.
DOI: 10.5281/zenodo.16735750
title: Life Cycle Assessment data for Environmental Impact Assessment in COMMECT living labs

description: This dataset, developed as a part of Horizon Europe COMMECT project, contains LCI (Life Cycle Inventory) and LCIA (Life Cycle Impact Assessment) data used to complete LCA (Life Cycle Assessment) for the projects living labs. LCA was concluded in four living labs and five use cases:





Luxembourg – Digitalisation of Viticulture – In-Field Microclimate and Crop Monitoring in Vineyards




Serbia – Sustainable Agriculture and Preservation of Natural Environment – Shared digital agriculture platform




Türkiye – Smart Olive Tree Farming – Microclimate Monitoring for Early Disease and Pest Detection and Monitoring of Pest Insect Traps




Denmark – Connected Livestock Transport – Real-time video monitoring of the loading or unloading process



Dataset includes environmental data considering connectivity solution tested in living labs, and environmental data considering farming activities in reference year (2023) and farming activities with connectivity solution (year 2024). Dataset also include sensitivity analysis for the data.
DOI: 10.5281/zenodo.16735751
title: Life Cycle Assessment data for Environmental Impact Assessment in COMMECT living labs

description: This dataset, developed as a part of Horizon Europe COMMECT project, contains LCI (Life Cycle Inventory) and LCIA (Life Cycle Impact Assessment) data used to complete LCA (Life Cycle Assessment) for the projects living labs. LCA was concluded in four living labs and five use cases:





Luxembourg – Digitalisation of Viticulture – In-Field Microclimate and Crop Monitoring in Vineyards




Serbia – Sustainable Agriculture and Preservation of Natural Environment – Shared digital agriculture platform




Türkiye – Smart Olive Tree Farming – Microclimate Monitoring for Early Disease and Pest Detection and Monitoring of Pest Insect Traps




Denmark – Connected Livestock Transport – Real-time video monitoring of the loading or unloading process



Dataset includes environmental data considering connectivity solution tested in living labs, and environmental data considering farming activities in reference year (2023) and farming activities with connectivity solution (year 2024). Dataset also include sensitivity analysis for the data.
DOI: 10.15468/dl.m9vswu
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mulleripicus fuliginosus)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 118 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014334-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/5qrjif
title: Enterprise Survey Data for Just Transition in Ramgarh and Bokaro District

description: Description:
The dataset contains the raw data of the registered enterprise survey, conducted in coal-dependent districts of Jharkhand, namely Ramgarh and Bokaro. The objective of survey is to have a comprehensive understanding of coal dependency and economic diversification prospects. Apart from focused questions about coal dependency, the survey included probes around industries’ preferences about new sectors that can be established, and their readiness to diversify. We also assessed the challenges they face in the event of diversification. These survey questions were divided into five sections: General, Institution, Finance, Infrastructure, and Labour and Human Resources. These were chosen based on a previously developed economic diversification framework.

Registered enterprise
For the registered enterprises, we use stratified random sampling using three strata: 
Size of the district economy: Given that the Gross District Domestic Product (GDDP) of both districts is under $20 billion, a sample size ranging from 120 to 185 registered enterprises was deemed appropriate. This sample size slightly exceeds the World Bank's enterprise survey methodology, which recommends sample sizes based on GDDP. The larger sample size was chosen to ensure robust data collection across a diverse set of industries, enhance the statistical reliability of the findings, and account for potential non-response or data quality issues.

Sector classification (primary, secondary and tertiary): Each sector is sub-classified into coal and non-coal, consisting of enterprises with direct and indirect dependency on coal, and use of coal-based boilers.


Firm size: Each unit is classified into small, medium and large based on the number of people employed. The classification is created based on the list of industries, nature of employment and expert consultation. Small enterprises employ below 20 workers, medium enterprises 20-100 workers, and large above 100 workers. The classification is based on the type of industry and the number of employees in the different enterprises in the districts. 

Unregistered enterprise:
Unregistered enterprises refer to those businesses that are involved in manufacturing activities or provide/render services but have not registered themselves permanently with the relevant authorities, or have failed to file an EM-II (Entrepreneurs Memorandum) with the District Industries Centre (DIC) on or before the stipulated date of March 31, 2007

We included unregistered enterprises because they play a crucial role in the informal coal economy, In each district, we surveyed 55 unregistered enterprises using single-stage cluster sampling. We specifically targeted unregistered businesses in the primary market areas of the two districts with the highest concentration of such enterprises. This approach was chosen to ensure a representative sample from areas where the prevalence of unregistered businesses was most prominent.
DOI: 10.15468/dl.pckequ
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 (Vipera ursinii)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014336-250802193616735/datasets/export for details.
DOI: 10.15468/dl.jya7kq
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Solenopsis geminata",
 "Country is Vanuatu",
 "HasCoordinate is true"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014335-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.659w6s
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 (Vipera ursinii)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014337-250802193616735/datasets/export for details.
DOI: 10.5281/zenodo.16686536
title: Latest High Resolution Layers (HRL) from Copernicus Land Cover Services

description: We provide here some High Resolution Layers (HRL) from the Copernicus Land Cover Service optimized for file volume.



The data are compressed using either 1- or 2-byte rasters to save space and make for faster download. Due to contents, the non-compressed file sizes would be between x2 to x20 the size. Please take note that compressed TIF files imply they are a bit slower to process as algorithms have to uncompress the data.

These data were downloaded from the WeKeo Data and Information Access service. The mosaic of ~800 tiles that cover the EU contries are  aligned in the EPSG:3035 grid coordinate system. Each layer has its respective confidence layer.



CLCplus Backbone 2023 (raster 10 m)

Tree Cover Density 2021 (raster 10 m)

Grassland 2021 (raster 10 m)

Crop Type 2021 (raster 10 m)


 

Please read the
DOI: 10.5281/zenodo.16730882
title: Latest High Resolution Layers (HRL) from Copernicus Land Cover Services

description: We provide here some High Resolution Layers (HRL) from the Copernicus Land Cover Service optimized for file volume.



The data are compressed using either 1- or 2-byte rasters to save space and make for faster download. Due to contents, the non-compressed file sizes would be between x2 to x20 the size. Please take note that compressed TIF files imply they are a bit slower to process as algorithms have to uncompress the data.

These data were downloaded from the WeKeo Data and Information Access service. The mosaic of ~800 tiles that cover the EU contries are  aligned in the EPSG:3035 grid coordinate system. Each layer has its respective confidence layer.



CLCplus Backbone 2023 (raster 10 m)

Tree Cover Density 2021 (raster 10 m)

Grassland 2021 (raster 10 m)

Crop Type 2021 (raster 10 m)


 

Please read the
DOI: 10.15468/dl.awdt52
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 (Vipera ursinii)",
 "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/0014338-250802193616735/datasets/export for details.
DOI: 10.5281/zenodo.16743583
title: A Large Scale Survey of Motivation in Software Development

description: **Context:** Motivation is known to improve performance. In software development in particular, there has been considerable interest in themotivation of contributors to open-source.

**Objective:** We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relativeeffect on motivation. Since motivation is an internal subjective feeling, we alsoanalyze the validity of the answers.

**Method:** We conducted a survey with 66 questions on motivation which wascompleted by 521 developers. Most of the questions used an 11-point scale. Weevaluated the answers’ validity by comparing related questions, comparing toactual behavior on GitHub, and comparison with the same developer in afollow-up survey.

**Results:** Validity problems include moderate correlations between answers torelated questions, as well as self-promotion and mistakes in the answers. Despite these problems, predictive analysis—investigating how diverse motivatorsinfluence the probability of high motivation—provided valuable insights. Thecorrelations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of highmotivation. In addition, improvement analysis shows that an increase in mostmotivators predicts an increase in general motivation.

**Conclusions:** All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivationimprovement, and each motivator sheds light on a different aspect of motivation. Therefore models based on multiple motivators predict motivationimprovement with up to 94% accuracy, better than any single motivator.

**Keywords:** Motivation · Software engineering · Open-source development · Survey validity
DOI: 10.5281/zenodo.16743582
title: A Large Scale Survey of Motivation in Software Development

description: **Context:** Motivation is known to improve performance. In software development in particular, there has been considerable interest in themotivation of contributors to open-source.

**Objective:** We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relativeeffect on motivation. Since motivation is an internal subjective feeling, we alsoanalyze the validity of the answers.

**Method:** We conducted a survey with 66 questions on motivation which wascompleted by 521 developers. Most of the questions used an 11-point scale. Weevaluated the answers’ validity by comparing related questions, comparing toactual behavior on GitHub, and comparison with the same developer in afollow-up survey.

**Results:** Validity problems include moderate correlations between answers torelated questions, as well as self-promotion and mistakes in the answers. Despite these problems, predictive analysis—investigating how diverse motivatorsinfluence the probability of high motivation—provided valuable insights. Thecorrelations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of highmotivation. In addition, improvement analysis shows that an increase in mostmotivators predicts an increase in general motivation.

**Conclusions:** All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivationimprovement, and each motivator sheds light on a different aspect of motivation. Therefore models based on multiple motivators predict motivationimprovement with up to 94% accuracy, better than any single motivator.

**Keywords:** Motivation · Software engineering · Open-source development · Survey validity
DOI: 10.7910/dvn/id5yyl
title: Enterprise Survey in context of Jut Transition:Mancherial and Peddapalli Telangana

description: The dataset comprises data from surveys conducted among 424 registered and unregistered enterprises, providing a comprehensive understanding of coal dependency and prospects for economic diversification. Apart from focused questions about coal dependency, the survey included probes around industries’ preferences about new sectors that can be established, and their readiness to diversify. We also assessed the challenges they face in the event of diversification. These survey questions were divided into five sections: 1) General; 2) Institution; 3) Finance; 4) Infrastructure; and, 5) Labour and human resources. These sections were chosen based on a previously developed economic diversification framework.
DOI: 10.15468/dl.qh5evt
title: Occurrence Download

description: A dataset containing 99668 records in GBIF matching the query:
SELECT 
 "year", 
 "month",
 GBIF_EEARGCode(1000, decimalLatitude, decimalLongitude, COALESCE(coordinateUncertaintyInMeters, 1000)) AS eeaCellCode,
 speciesKey,
 species,
 genusKey,
 genus,
 familyKey,
 family,
 orderKey,
 "order",
 classKey,
 class,
 COUNT(*) AS occurrences, 
 COUNT(DISTINCT recordedBy) AS distinctObservers
 FROM
 occurrence
 WHERE 
 GBIF_Within('POLYGON ((4.743004 50.68406, 4.743004 50.877911, 4.171371 50.877911, 4.171371 50.68406, 4.743004 50.68406))', decimalLatitude, decimalLongitude) = TRUE AND
 hasCoordinate = TRUE AND 
 occurrenceStatus = 'PRESENT' 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 
 "year" IS NOT NULL AND 
 "month" IS NOT NULL AND 
 "year" &gt;= 1980 AND "year" &lt;= 2020 AND
 taxonKey IN (2914547,3113784,7502456,6027388,3923253,2481119,3135329,5275012,2481819,2891147,9118014,2874027,5331258,2650133,2685484,2926557,2787993,2723315,2926683,8397184,5304283,3133702,3033665,9444556,2673408,5371874,5410564,5329192,3033266,5289877,2727917,2704505,5334277,2739622,2882316,3085811,2701418,2914642,5365576,3085424,5371781,3189172,3039454,5341478,2360842,2480726,5369701,3054168,2671130,2704211,5386897,5365496,3029627,3033263,5366922,2480910,5301213,2882482,2729081,5290194,9490132,2882833,5388835,2756540,5371889,4149465,5314725,2878688,2890668,5405976,9213153,7277865,2681729,7960979,2477872,3190715,3113650,5385604,2888808,2724609,5333408,2702717,5384795,3191374,7883344,5664390,7702019,2676091,2493129,5371818,9752617,5290081,1704195,8848598,3136606,8165236,2876213,5331916,2722051,2951998,5743122,2726592,3112651,5301141,2432427,2704951,3152379,6363434,4109084,7903764,4567182,2721983,4932232,3137498,3188736,5356973,8214667,5333411,2700934,3033377,3189103,8278445,2873896,3189863,5371808,3012167,2432452,5403531,2475532,5284517,5373255,3128547,2706163,5414222,8229935,3033289,7531424,3040227,3033464,3040249,3113777,6363496,7693914,5347644,6027328,3046044,2875818,8925249,5409958,5538108,7297904,5334208,2681640,3152047,2880130,3171712,3033675,2727294,5329266,2687943,5376075,3172049,7459211,3087634,2913556,3034620,3034742,4567215,5347571,2926901,2480420,2888960,6026490,5365283,2673131,2769766,11071158,8773033,7589456,5414992,2878826,2431885,5374901,2681714,2987999,8365713,2721897,7972010,2675719,2913288,5301200,5403296,4116392,7270598,5347824,2723099,3169340,2676491,2927324,5331355,4408732,2701939,2722602,3172427)
 GROUP BY
 species,
 speciesKey,
 eeaCellCode,
 "year",
 "month",
 genusKey,
 genus,
 familyKey,
 family,
 orderKey,
 "order",
 classKey,
 class
 ORDER BY 
 "year" ASC,
 "month" ASC,
 speciesKey ASC

 The dataset's 99668 records were derived from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014310-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yqbk8p
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Micromoema xiphophora)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014341-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.apfw5h
title: Occurrence Download

description: A dataset containing 8479 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sisymbrium loeselii",
 "Geometry POLYGON ((26.04335 1.226334, 135.0263 1.226334, 135.0263 55.38525, 26.04335 55.38525, 26.04335 1.226334))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8479 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014342-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3qp8sg
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ADT3955"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014343-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bj5sjm
title: Occurrence Download

description: A dataset containing 2592699 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pl@ntNet observations"
 ]
}

 The dataset includes 2592699 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014327-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.