cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15615767
title: Supplementary Dataset: Comprehensive Techno-Economic Analysis of Different Production Technologies of Multiple Energy Vectors – A Sub-Saharan Outlook

description: This dataset supports the research presented in journal paper titled "Comprehensive Techno-Economic Analysis of Different Production Technologies of Multiple Energy Vectors: A Sub-Saharan Outlook." It includes all input parameters, results and additional methodological details used in the techno-economic model, which evaluates bio-based energy vector production from Sub-Saharan Africa (Nigeria, Kenya, South Africa) for export to European demand centers.

The dataset is organised into two appendices:

Appendix A: Supplementary Tables – Model Parameters



Table A1. Selling prices of products and assumed plant lifespan

Table A2. Feedstock-to-product conversion factors across technologies

Table A3. Production costs per product-technology pair

Table A4. Operational expenditure (OPEX) per ton

Table A5. Capital expenditure (CAPEX) per ton of installed capacity

Table A6. Disaggregated transport costs (road and sea)

Table A7–A9. Leading import markets and top suppliers for hydrogen, ammonia, and methanol

Table A10. Bio-based energy product demand and EU demand centers

Table A11. Supplier share factors used in supply allocation


Appendix B: Extended Methodological Notes



B.1. Demand scenario calculations

B.2. Solver environment and optimisation settings

B.3. Transport cost estimation for Nigeria, Kenya, and South Africa

B.4. General OPEX as a percentage of CAPEX across sectors


Appendix C: Splementary Tables - Model Results 



C 1. Amount of Hydrogen, Ammonia and Methanol Exported from Nigeria to EU Demand Centres 

C 2. Amount of Hydrogen, Ammonia and Methanol Exported from Kenya to EU Demand Centres

C 3. Amount of Hydrogen, Ammonia and Methanol Exported from Nigeria to EU Demand Centres

C 4. Country-Wide Summary of Production and Economic Performance


These tables and notes form the basis for the mixed-integer linear programming (MILP) model and support reproducibility and transparency.
description: Supplementary Methods (S1–S3) provide detailed hydrogen balance constraints, stoichiometric assumptions, and model parameterisation.

Supplementary Methods



S1. Hydrogen Demand Assumptions for Bio-derived Energy Vectors 

S2. Hydrogen Supply from Biomass Gasification

S3. Hydrogen–technology Coupling and Balance Constraints
DOI: 10.5281/zenodo.17658118
title: Supplementary Dataset: Comprehensive Techno-Economic Analysis of Different Production Technologies of Multiple Energy Vectors – A Sub-Saharan Outlook

description: This dataset supports the research presented in journal paper titled "Comprehensive Techno-Economic Analysis of Different Production Technologies of Multiple Energy Vectors: A Sub-Saharan Outlook." It includes all input parameters, results and additional methodological details used in the techno-economic model, which evaluates bio-based energy vector production from Sub-Saharan Africa (Nigeria, Kenya, South Africa) for export to European demand centers.

The dataset is organised into two appendices:

Appendix A: Supplementary Tables – Model Parameters



Table A1. Selling prices of products and assumed plant lifespan

Table A2. Feedstock-to-product conversion factors across technologies

Table A3. Production costs per product-technology pair

Table A4. Operational expenditure (OPEX) per ton

Table A5. Capital expenditure (CAPEX) per ton of installed capacity

Table A6. Disaggregated transport costs (road and sea)

Table A7–A9. Leading import markets and top suppliers for hydrogen, ammonia, and methanol

Table A10. Bio-based energy product demand and EU demand centers

Table A11. Supplier share factors used in supply allocation


Appendix B: Extended Methodological Notes



B.1. Demand scenario calculations

B.2. Solver environment and optimisation settings

B.3. Transport cost estimation for Nigeria, Kenya, and South Africa

B.4. General OPEX as a percentage of CAPEX across sectors


Appendix C: Splementary Tables - Model Results 



C 1. Amount of Hydrogen, Ammonia and Methanol Exported from Nigeria to EU Demand Centres 

C 2. Amount of Hydrogen, Ammonia and Methanol Exported from Kenya to EU Demand Centres

C 3. Amount of Hydrogen, Ammonia and Methanol Exported from Nigeria to EU Demand Centres

C 4. Country-Wide Summary of Production and Economic Performance


These tables and notes form the basis for the mixed-integer linear programming (MILP) model and support reproducibility and transparency.
description: Supplementary Methods (S1–S3) provide detailed hydrogen balance constraints, stoichiometric assumptions, and model parameterisation.

Supplementary Methods



S1. Hydrogen Demand Assumptions for Bio-derived Energy Vectors 

S2. Hydrogen Supply from Biomass Gasification

S3. Hydrogen–technology Coupling and Balance Constraints
DOI: 10.15156/bio/sh0167299.10fu
title: SH0167299.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0167300.10fu
title: SH0167300.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0167301.10fu
title: SH0167301.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.tc239u
title: Occurrence Download

description: A dataset containing 5242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "Geometry POLYGON ((44 -16, 44 -21, 43 -21, 43 -23, 44 -23, 44 -25, 45 -25, 45 -26, 46 -26, 46 -25, 47 -25, 47 -24, 46 -24, 46 -19, 45 -19, 45 -17, 46 -17, 46 -18, 47 -18, 47 -17, 48 -17, 48 -15, 47 -15, 46 -15, 46 -16, 45 -16, 44 -16))"
 ]
}

 The dataset includes 5242 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001277-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0167302.10fu
title: SH0167302.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0167303.10fu
title: SH0167303.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0167304.10fu
title: SH0167304.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0167305.10fu
title: SH0167305.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.bbbsk2
title: Occurrence Download

description: A dataset containing 20619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (March, April, May, September, October, November)",
 "OccurrenceStatus is Present",
 "TaxonKey is Grus americana",
 "Year 2000-2026"
 ]
}

 The dataset includes 20619 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001278-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0167306.10fu
title: SH0167306.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.tw8p6x
title: Occurrence Download

description: A dataset containing 247 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Melampsoridium hiratsukanum",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 247 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028716-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jj2mfn
title: Occurrence Download

description: A dataset containing 6968 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "DatasetKey is one of (iNaturalist Research-grade Observations, EOD – eBird Observation Dataset, Observation.org, Nature data from around the World, Xeno-canto - Bird sounds from around the world, NABU|naturgucker, Registro de Fauna Silvestre en el Inventario Nacional Forestal y de Fauna Silvestre-SERFOR, Perú, Museum of Comparative Zoology, Harvard University)",
 "GbifRegion is Latin America and the Caribbean",
 "Geometry POLYGON((-78.14025 -13.17448,-69.08702 -17.01854,-70.63126 -7.64659,-80.00322 -4.18536,-78.14025 -13.17448))",
 "OccurrenceStatus is Present",
 "TaxonKey is Rupicola peruvianus",
 "Year 2015-2025"
 ]
}

 The dataset includes 6968 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028715-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tvabwm
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Melampsoridium hiratsukanum",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028717-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ft2t82
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Melampsoridium hiratsukanum",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 88 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028718-250717081556266/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.29618495
title: LiDAR dataset

description: LiDAR dataset used for visioning the working of the project.
DOI: 10.6084/m9.figshare.29618495.v1
title: LiDAR dataset

description: LiDAR dataset used for visioning the working of the project.
DOI: 10.15468/dl.35wxn7
title: Occurrence Download

description: A dataset containing 5459 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Suncus etruscus"
 ]
}

 The dataset includes 5459 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028719-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cydc6a
title: Occurrence Download

description: A dataset containing 3544 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuphea aequipetala"
 ]
}

 The dataset includes 3544 records from 109 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028720-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15482/usda.adc/29202737
title: Benefit Redemption Patterns in SNAP: FY 2022 Public-Use File

description: The Benefit Redemption Patterns in SNAP: FY 2022 study examines how participants redeemed benefits in FY 2022. Public-use data files are provided as part of this study, and they include information on SNAP participants’ benefit redemption patterns such as the average number and dollar amount of transactions made with SNAP benefits in a month, the types and number of retailers at which participants redeem their benefits, and the number of days or weeks that elapse before participants redeem most of their monthly benefit.The public-use files can be shared with a larger research community, and therefore pose a higher risk of disclosure of sensitive individual and retailer information. For this reason, potentially sensitive data have been removed from these public-use files, and analyses using these files will yield estimates that are different than those presented in the Benefit Redemption Patterns in SNAP: FY 2022 final report tables. More information can be found in the “Addressing disclosure risk” section below. Additional detail on the data, methodology, and analytic considerations can be found in Appendix F (Detailed Description of the Methodology) of the Benefit Redemption Patterns in SNAP: FY 2022 final report.
DOI: 10.15482/usda.adc/29202737.v1
title: Benefit Redemption Patterns in SNAP: FY 2022 Public-Use File

description: The Benefit Redemption Patterns in SNAP: FY 2022 study examines how participants redeemed benefits in FY 2022. Public-use data files are provided as part of this study, and they include information on SNAP participants’ benefit redemption patterns such as the average number and dollar amount of transactions made with SNAP benefits in a month, the types and number of retailers at which participants redeem their benefits, and the number of days or weeks that elapse before participants redeem most of their monthly benefit.The public-use files can be shared with a larger research community, and therefore pose a higher risk of disclosure of sensitive individual and retailer information. For this reason, potentially sensitive data have been removed from these public-use files, and analyses using these files will yield estimates that are different than those presented in the Benefit Redemption Patterns in SNAP: FY 2022 final report tables. More information can be found in the “Addressing disclosure risk” section below. Additional detail on the data, methodology, and analytic considerations can be found in Appendix F (Detailed Description of the Methodology) of the Benefit Redemption Patterns in SNAP: FY 2022 final report.
DOI: 10.15482/usda.adc/29202851
title: Fiscal Year 2022 Supplemental Nutrition Assistance Program Quality Control Database

description: The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA). SNAP provides millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2023, SNAP served an average of 42.2 million people monthly and paid out $107 billion in benefits, including emergency allotments to supplement SNAP benefits during the COVID-19 public health emergency.1The characteristics of SNAP participants and households and the size of the SNAP caseload change over time in response to changes in program rules as well as economic and demographic trends. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) Database. This database is an edited version of the raw data file of monthly case reviews that are conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for their SNAP caseloads.