cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c7nz8
title: CCDC 2182120: Experimental Crystal Structure Determination

description: Related Article: D. Upmann, P.G. Jones|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|183|doi:10.1107/S2056989025000805
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c7n6h
title: CCDC 2182096: Experimental Crystal Structure Determination

description: Related Article: D. Upmann, P.G. Jones|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|183|doi:10.1107/S2056989025000805
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c7n7j
title: CCDC 2182097: Experimental Crystal Structure Determination

description: Related Article: D. Upmann, P.G. Jones|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|183|doi:10.1107/S2056989025000805
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2m7fq1
title: CCDC 2420223: Experimental Crystal Structure Determination

description: Related Article: D. Upmann, P.G. Jones|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|183|doi:10.1107/S2056989025000805
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2m7fr2
title: CCDC 2420224: Experimental Crystal Structure Determination

description: Related Article: D. Upmann, P.G. Jones|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|183|doi:10.1107/S2056989025000805
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25399/unisadata.28512584
title: Developing an indigenous mathematics, science and Technology education master’s programme : a three universities collaboration

description: The study investigates the indigenisation of the Mathematics, Science and Technology (MST) Education Master’s Programme with a view to developing a joint MST Master’s programme for UNISA University of Botswana and Chinhoyi University of Technology. This is an NRF-funded project involving these three universities (UNISA, University of Botswana and Chinhoyi University of Technology). In 2021, the proposer of the project, Prof MT Gumbo, applied for the Comprehensive Programme for Rated Researchers. He included three academics representing the three MST streams from the University of Botswana (UB) and Chinhoyi University of Technology (CUT). The project has been running since 2022.The study investigates the indigenisation of the Mathematics, Science and Technology (MST) Education Master’s Programme with a view to developing a joint MST Master’s programme for UNISA University of Botswana and Chinhoyi University of Technology.Research and teaching of MST in higher education suffer Westernisation which stalls sustainable development and adds to poverty in the Southern African Development Community (SADC) (Kwaira & Gumbo, 2017; Moalosi, Marope & Setlhatlhanyo, 2017); Nhemachena, Hlabangane & Matowanyika, 2020). The content, teaching and research in Mathematics, Science and Technology (MST) have and continue to deny indigenous students opportunities to learn and research about indigenous knowledge systems (IKS). This is despite the existence of indigenous science and technology (including mathematics) for centuries (Opoku Jnr & James, 2021) which are expressed through indigenous industries in the specialist fields such as blacksmithing, woodcarving, textile-weaving and dyeing (Nhemachena et al., 2020). This long-time existence of ethno-MST are endowed with epistemologies, skills and resources which provide the basis for teaching and research. Indigenous knowledge (IK), including MST, is important for local people’s survival as it is applied in their daily activities (Gumbo, 2017). There is therefore a need to indigenise, as part of decolonial project, the MST programmes in order to realise the benefits of sustainability that ethno-MST can bring to indigenous communities. Datta (2018) advocates for a collaborative research knowledge that is culturally appropriate, honouring and careful of indigenous communities. The scope of universities’ attempts to decolonise (indigenise/Africanise) the academic programmes is narrow as they mainly focus on the undergraduate programme and neglect the postgraduate programme.
DOI: 10.25399/unisadata.28512584.v1
title: Developing an indigenous mathematics, science and Technology education master’s programme : a three universities collaboration

description: The study investigates the indigenisation of the Mathematics, Science and Technology (MST) Education Master’s Programme with a view to developing a joint MST Master’s programme for UNISA University of Botswana and Chinhoyi University of Technology. This is an NRF-funded project involving these three universities (UNISA, University of Botswana and Chinhoyi University of Technology). In 2021, the proposer of the project, Prof MT Gumbo, applied for the Comprehensive Programme for Rated Researchers. He included three academics representing the three MST streams from the University of Botswana (UB) and Chinhoyi University of Technology (CUT). The project has been running since 2022.The study investigates the indigenisation of the Mathematics, Science and Technology (MST) Education Master’s Programme with a view to developing a joint MST Master’s programme for UNISA University of Botswana and Chinhoyi University of Technology.Research and teaching of MST in higher education suffer Westernisation which stalls sustainable development and adds to poverty in the Southern African Development Community (SADC) (Kwaira & Gumbo, 2017; Moalosi, Marope & Setlhatlhanyo, 2017); Nhemachena, Hlabangane & Matowanyika, 2020). The content, teaching and research in Mathematics, Science and Technology (MST) have and continue to deny indigenous students opportunities to learn and research about indigenous knowledge systems (IKS). This is despite the existence of indigenous science and technology (including mathematics) for centuries (Opoku Jnr & James, 2021) which are expressed through indigenous industries in the specialist fields such as blacksmithing, woodcarving, textile-weaving and dyeing (Nhemachena et al., 2020). This long-time existence of ethno-MST are endowed with epistemologies, skills and resources which provide the basis for teaching and research. Indigenous knowledge (IK), including MST, is important for local people’s survival as it is applied in their daily activities (Gumbo, 2017). There is therefore a need to indigenise, as part of decolonial project, the MST programmes in order to realise the benefits of sustainability that ethno-MST can bring to indigenous communities. Datta (2018) advocates for a collaborative research knowledge that is culturally appropriate, honouring and careful of indigenous communities. The scope of universities’ attempts to decolonise (indigenise/Africanise) the academic programmes is narrow as they mainly focus on the undergraduate programme and neglect the postgraduate programme.
DOI: 10.15468/dl.8ubbyh
title: Occurrence Download

description: A dataset containing 1268425 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-13.86493 49.7055,7.1279 49.7055,7.1279 59.53948,-13.86493 59.53948,-13.86493 49.7055))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fulica atra Linnaeus, 1758",
 "Year 2015-2025"
 ]
}

 The dataset includes 1268425 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000670-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bys9gp
title: Occurrence Download

description: A dataset containing 28885 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Avena sativa L.",
 "Year is greater than or equal to 2005",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 28885 records from 587 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000683-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pxnb7gh646.1
title: Augmented Smart Home with Weather Information

description: This dataset builds upon the publicly available work by Taranvee, who collected household energy consumption data at one-minute resolution from a smart meter monitoring multiple appliances (e.g., dishwasher, home office, fridge, kitchen). The original release also includes regional weather information (humidity, temperature, atmospheric pressure, etc.), providing a rich contextual layer for understanding how environmental factors influence residential power usage.

In this augmented version, we introduce additional consumption columns representing distinct IoT devices, Car charger,Water heater,Air conditioning,Home Theater,Outdoor lights,microwave,Laundry,Pool Pump

Each of these new columns tracks an appliance's energy usage in kilowatts ([kW]), effectively broadening the dataset’s scope for modeling complex, multi-device scenarios within a single smart home

Original work: https://www.kaggle.com/datasets/taranvee/smart-home-dataset-with-weather-information
DOI: 10.15468/dl.vcba26
title: Occurrence Download

description: A dataset containing 28885 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Avena sativa L.",
 "Year is greater than or equal to 2005",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 28885 records from 587 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000649-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.87vs82
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Ailuropoda melanoleuca",
 "Year 1897-2025"
 ]
}

 The dataset includes 87 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058764-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zw2k5u
title: Occurrence Download

description: A dataset containing 232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Teucrium lepicephalum)",
 "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 232 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058763-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yr8h26
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gaviiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((150 -60,180 -60,180 -30,150 -30,150 -60))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058766-250920141307145/datasets/export for details.
DOI: 10.15156/bio/sh1062808.10fu
title: SH1062808.10FU

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/sh1062809.10fu
title: SH1062809.10FU

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/sh1062811.10fu
title: SH1062811.10FU

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/sh1062814.10fu
title: SH1062814.10FU

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/sh1062816.10fu
title: SH1062816.10FU

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/sh1062818.10fu
title: SH1062818.10FU

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/sh1062819.10fu
title: SH1062819.10FU

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/sh1062820.10fu
title: SH1062820.10FU

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/sh1062823.10fu
title: SH1062823.10FU

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.