cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15016858
title: Mapping targetable sites on the human surfaceome for the design of novel binders

description: The human cell surfaceome, integral to cell communication and disease mechanisms, presents a prime target for therapeutic intervention. De novo protein binder design against these cell surface proteins offers a promising yet underexplored strategy for drug development. However, the vast search space and limited data on natural or competitive binders have historically limited experimental success. In this study, we systematically analyzed the entire human surfaceome, identifying approximately 4,500 targetable sites and introducing high-quality binder seeds tailored for protein design applications. To validate these seeds, we implemented two experimental approaches (protein scaffolding and peptide cyclization) on three representative targets (FGFR2, IFNAR2, and HER3). Our results revealed a high success rate, emphasizing the precision and therapeutic potential of these seeds, as well as the need for constant improvements of computational protein design pipelines utilizing machine learning and physics-based methods. Additionally, we present SURFACE-Bind, an interactive database offering open access to all generated data. The high-throughput computational design methods and target-specific binder seeds established here pave the way for a new generation of targeted therapeutics for the human surfaceome.
description: This Zenodo dataset provides the following data:

1) Surfaceome_predicted_sites.zip: The predicted binding sites in mesh representation form for each protein.

2) Surfaceome_top10_seeds_per_site.zip, and Surfaceome_top100_seeds_per_site.zip: Top 10, and top 100 predicted binder seeds for each target protein, respectively, sorted based on their complementarity score.

3) Top10_seeds_aligned_on_AF.zip: Top 10 predicted binder seeds aligned on the reference AlphaFold structure.
DOI: 10.5281/zenodo.15016859
title: Mapping targetable sites on the human surfaceome for the design of novel binders

description: The human cell surfaceome, integral to cell communication and disease mechanisms, presents a prime target for therapeutic intervention. De novo protein binder design against these cell surface proteins offers a promising yet underexplored strategy for drug development. However, the vast search space and limited data on natural or competitive binders have historically limited experimental success. In this study, we systematically analyzed the entire human surfaceome, identifying approximately 4,500 targetable sites and introducing high-quality binder seeds tailored for protein design applications. To validate these seeds, we implemented two experimental approaches (protein scaffolding and peptide cyclization) on three representative targets (FGFR2, IFNAR2, and HER3). Our results revealed a high success rate, emphasizing the precision and therapeutic potential of these seeds, as well as the need for constant improvements of computational protein design pipelines utilizing machine learning and physics-based methods. Additionally, we present SURFACE-Bind, an interactive database offering open access to all generated data. The high-throughput computational design methods and target-specific binder seeds established here pave the way for a new generation of targeted therapeutics for the human surfaceome.
description: This Zenodo dataset provides the following data:

1) Surfaceome_predicted_sites.zip: The predicted binding sites in mesh representation form for each protein.

2) Surfaceome_top10_seeds_per_site.zip, and Surfaceome_top100_seeds_per_site.zip: Top 10, and top 100 predicted binder seeds for each target protein, respectively, sorted based on their complementarity score.

3) Top10_seeds_aligned_on_AF.zip: Top 10 predicted binder seeds aligned on the reference AlphaFold structure.
DOI: 10.4121/de21d6c2-95f7-42e5-a3cf-f5b66b559924.v1
title: Data underlying the publication: Small-scale and large-scale agriculture across water-scarce and water-abundant regions

description: We aim to estimate the geographic distribution of small-scale and large-scale agriculture across water-scarce and water-abundant regions, their blue and green water consumption, and the water stress (stress from a lack of blue water) and soil fertility stress on their crop. We combined three definitions of small-scale agriculture and used a soil fertility-enhanced crop model to estimate crop production and water consumption.<br>This dataset contains country-level results (55 countries). Code and gridded output will be available upon request.<br>A detailed method description and analysis are under preparation/review. We will update it here upon acceptance.
DOI: 10.4121/de21d6c2-95f7-42e5-a3cf-f5b66b559924.v2
title: Data underlying the publication: Small-scale and large-scale agriculture across water-scarce and water-abundant regions

description: We aim to estimate the geographic distribution of small-scale and large-scale agriculture across water-scarce and water-abundant regions, their blue and green water consumption, and the water stress (stress from a lack of blue water) and soil fertility stress on their crop. We combined three definitions of small-scale agriculture and used a soil fertility-enhanced crop model to estimate crop production and water consumption.<br>This dataset contains country-level results (55 countries). Code and gridded output will be available upon request.<br>A detailed method description and analysis are under preparation/review. We will update it here upon acceptance.
DOI: 10.4121/de21d6c2-95f7-42e5-a3cf-f5b66b559924.v3
title: Data underlying the publication: Small-scale and large-scale agriculture across water-scarce and water-abundant regions

description: We aim to estimate the geographic distribution of small-scale and large-scale agriculture across water-scarce and water-abundant regions, their blue and green water consumption, and the water stress (stress from a lack of blue water) and soil fertility stress on their crop. We combined three definitions of small-scale agriculture and used a soil fertility-enhanced crop model to estimate crop production and water consumption. <br>This dataset contains country-level and grid-level results (55 countries). Crop code is open source and freely available on GitHub (https://github.com/Han-Su22/ACEA) which is also archived in Zenado (DOI: 10.5281/zenodo.10510933) via a Creative Commons Attribution 4.0 International license. All the code, input data, and output data required to reproduce the results in this study will be archived for at least 10 years after publication within the University of Twente, Multidisciplinary Water Management (MWM) group. The MWM group will make the code and data available to anyone upon request.<br>A detailed method description and analysis are under preparation/review. We will update it here upon acceptance.<br>
DOI: 10.4121/de21d6c2-95f7-42e5-a3cf-f5b66b559924.v4
title: Data underlying the publication: Small-scale and large-scale agriculture across water-scarce and water-abundant regions

description: We aim to estimate the geographic distribution of small-scale and large-scale agriculture across water-scarce and water-abundant regions, their blue and green water consumption, and the water stress (stress from a lack of blue water) and soil fertility stress on their crop. We combined three definitions of small-scale agriculture and used a soil fertility-enhanced crop model to estimate crop production and water consumption.<br>This dataset contains country-level and grid-level results (55 countries). Crop code is open source and freely available on GitHub (https://github.com/Han-Su22/ACEA) which is also archived in Zenado (DOI: 10.5281/zenodo.10510933) via a Creative Commons Attribution 4.0 International license. All the code, input data, and output data required to reproduce the results in this study will be archived for at least 10 years after publication within the University of Twente, Multidisciplinary Water Management (MWM) group. The MWM group will make the code and data available to anyone upon request.<br>A detailed method description and analysis can be found in the paper below (please also cite the paper when using the dataset). Feel free to contact the corresponding author Han Su (h.su@utwente.nl) when you have any questions.<br>Su, H., Foster, T., Hogeboom, R.J., Luna-Gonzalez, D.V., Mialyk, O., Willaarts, B., Wang, Y., Krol, M.S., 2025. Nutrient production, water consumption, and stresses of large-scale versus small-scale agriculture: A global comparative analysis based on a gridded crop model. Global Food Security 45, 100844.https://doi.org/10.1016/j.gfs.2025.100844
DOI: 10.4121/de21d6c2-95f7-42e5-a3cf-f5b66b559924.v5
title: Data underlying the publication: Small-scale and large-scale agriculture across water-scarce and water-abundant regions

description: We aim to estimate the geographic distribution of small-scale and large-scale agriculture across water-scarce and water-abundant regions, their blue and green water consumption, and the water stress (stress from a lack of blue water) and soil fertility stress on their crop. We combined three definitions of small-scale agriculture and used a soil fertility-enhanced crop model to estimate crop production and water consumption.<br>This dataset contains country-level and grid-level results (55 countries). Crop code is open source and freely available on GitHub (https://github.com/Han-Su22/ACEA) which is also archived in Zenado (DOI: 10.5281/zenodo.10510933) via a Creative Commons Attribution 4.0 International license. All the code, input data, and output data required to reproduce the results in this study will be archived for at least 10 years after publication within the University of Twente, Multidisciplinary Water Management (MWM) group. The MWM group will make the code and data available to anyone upon request.<br>A detailed method description and analysis can be found in the paper below (please also cite the paper when using the dataset). Feel free to contact the corresponding author Han Su (h.su@utwente.nl) when you have any questions.<br>Su, H., Foster, T., Hogeboom, R.J., Luna-Gonzalez, D.V., Mialyk, O., Willaarts, B., Wang, Y., Krol, M.S., 2025. Nutrient production, water consumption, and stresses of large-scale versus small-scale agriculture: A global comparative analysis based on a gridded crop model. Global Food Security 45, 100844.https://doi.org/10.1016/j.gfs.2025.100844
DOI: 10.15468/dl.enrzjk
title: Occurrence Download

description: A dataset containing 1027 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Natator depressus (Garman, 1880)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2015",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1027 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006673-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8hyqc8
title: Occurrence Download

description: A dataset containing 1982215 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "Geometry POLYGON((-9.62402 39.27795,-6.10862 39.27795,-6.10862 42.23268,-9.62402 42.23268,-9.62402 39.27795))",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 1982215 records from 194 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006667-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xmmkbf
title: Occurrence Download

description: A dataset containing 199203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (France)",
 "TaxonKey is Alnus glutinosa (L.) Gaertn."
 ]
}

 The dataset includes 199203 records from 1614 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006666-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1091292.10fu
title: SH1091292.10FU

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/sh1091294.10fu
title: SH1091294.10FU

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/sh1091295.10fu
title: SH1091295.10FU

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/sh1091298.10fu
title: SH1091298.10FU

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/sh1091303.10fu
title: SH1091303.10FU

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/sh1091307.10fu
title: SH1091307.10FU

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/sh1091308.10fu
title: SH1091308.10FU

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/sh1091312.10fu
title: SH1091312.10FU

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/sh1091316.10fu
title: SH1091316.10FU

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/sh1091317.10fu
title: SH1091317.10FU

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.