cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nng9hn
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sedum booleanum B.L.Turner"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0122312-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3dfnm3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lindmania vinotincta B.Holst & Vivas"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010353-230530130749713/datasets/export for details.
DOI: 10.5281/zenodo.7574215
title: Informes de casos de estudio finales en hábitats humanos (anexo del libro: La visión sistémica del ambiente construido, 2024). [Data sets for the article: The Habitat Intervention Design Process -Part II]

description: From the book: Estos informes están escritos en español por los estudiantes que se mencionan en cada informe, quienes son los autores. Cada uno de estos seis documentos contiene casos de estudio y muestra la implementación de la metodología del 'Proceso de Diseño de Intervención del Hábitat' (IDP) en cada uno de los 6 casos mencionados en el libro (Capítulo 2.5 y Capítulo 3.2) (Libro: La visión sistémica del ambiente construido). 

Estos hacen parte de la Fase 2 Fundamentos Prácticos del Modelo del proyecto de investigación "Modelo Pedagógico para la Enseñanza del Diseño de Intervención del Hábitat en Programas de Educación Superior". 

From the article: These reports are Spanish-written. Each of these six documents contains one of the case studies and shows the implementation of the 'Habitat Intervention Design Process' (IDP) methodology in each of the 6 case studies mentioned in the article "The Habitat Intervention Design Process -Part II: A Transdisciplinary Model in the Pedagogy of the Design of the Built Environment" published in The International Journal of Design Education in its 2023 version. Find it here: https://doi.org/10.18848/2325-128X/CGP/v17i02/155-195
DOI: 10.15468/dl.vmh9hy
title: Occurrence Download

description: A dataset containing 1554 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-1000000.0",
 "Geometry POLYGON((-5.59382 36.05801,-5.57132 36.05801,-5.57132 36.0736,-5.59382 36.0736,-5.59382 36.05801))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1554 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0208602-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7574214
title: Informes de casos de estudio finales en hábitats humanos (anexo del libro: La visión sistémica del ambiente construido, 2024). [Data sets for the article: The Habitat Intervention Design Process -Part II]

description: From the book: Estos informes están escritos en español por los estudiantes que se mencionan en cada informe, quienes son los autores. Cada uno de estos seis documentos contiene casos de estudio y muestra la implementación de la metodología del 'Proceso de Diseño de Intervención del Hábitat' (IDP) en cada uno de los 6 casos mencionados en el libro (Capítulo 2.5 y Capítulo 3.2) (Libro: La visión sistémica del ambiente construido). 

Estos hacen parte de la Fase 2 Fundamentos Prácticos del Modelo del proyecto de investigación "Modelo Pedagógico para la Enseñanza del Diseño de Intervención del Hábitat en Programas de Educación Superior". 

From the article: These reports are Spanish-written. Each of these six documents contains one of the case studies and shows the implementation of the 'Habitat Intervention Design Process' (IDP) methodology in each of the 6 case studies mentioned in the article "The Habitat Intervention Design Process -Part II: A Transdisciplinary Model in the Pedagogy of the Design of the Built Environment" published in The International Journal of Design Education in its 2023 version. Find it here: https://doi.org/10.18848/2325-128X/CGP/v17i02/155-195
DOI: 10.15468/dl.6t4zns
title: Occurrence Download

description: A dataset containing 295 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-0.49814 39.14831,-0.51765 38.93091,-0.18966 38.95856,-0.24163 39.14831,-0.24163 39.15193,-0.49814 39.15193,-0.49814 39.14831))",
 "TaxonKey is Antirrhinum valentinum Font Quer"
 ]
}

 The dataset includes 295 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0094629-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w4b4gk
title: Occurrence Download

description: A dataset containing 19956 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Parthenium hysterophorus L."
 ]
}

 The dataset includes 19956 records from 267 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010063-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b3sjhp
title: Occurrence Download

description: A dataset containing 4570 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-9.01765 38.5631,-8.95983 38.5631,-8.95983 38.60711,-9.01765 38.60711,-9.01765 38.5631))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 4570 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010362-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jn68g2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "DecimalLatitude is greater than or equal to 50.106102354641",
 "DecimalLatitude is less than or equal to 50.1176553374646",
 "DecimalLongitude is greater than or equal to 70.6164634700554",
 "DecimalLongitude is less than or equal to 70.634429330023",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0208591-230224095556074/datasets/export for details.
DOI: 10.15468/dl.y3zznm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cnidaria",
 "DatasetKey is Coleoptera: Records for Kent",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-25.03991 31.75, 41.65954 31.75, 41.65954 71.53037, -25.03991 71.53037, -25.03991 31.75))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0208598-230224095556074/datasets/export for details.
DOI: 10.15468/dl.9tns38
title: Occurrence Download

description: A dataset containing 15773 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pistia stratiotes L."
 ]
}

 The dataset includes 15773 records from 420 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010035-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1396095.10fu
title: SH1396095.10FU

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/sh1396096.10fu
title: SH1396096.10FU

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: 10.17026/ar/p4nfwt
title: Programma van Eisen Opgraving, Kerkpad 4, Loppersum, Gemeente Eemsdelta (GR)

description: De locatie ligt binnen de oude kern van Loppersum en deels binnen de voormalige begrenzing van het kerkhof van de noordelijker gelegen Petrus en Pauluskerk. Op het bestemmingsplan heeft het gebied een ‘Waarde – Archeologie 2’. De voor ‘Waarde – Archeologie 2’ aangewezen gronden zijn mede bestemd voor het behoud van archeologische waarden. De geplande werkzaamheden overschrijden de vrijstellingsgrenzen in het vigerende bestemmingsplan en daarom is archeologisch onderzoek noodzakelijk. Wegens de hoge archeologische verwachting heeft het bevoegd gezag, de gemeente Eemsdelta, besloten dat direct een opgraving dient te worden uitgevoerd. Omdat momenteel nog bestaande bebouwing aanwezig is dient de ondergrondse sloop te worden uitgevoerd onder archeologische begeleiding (opgraving, variant begeleiden), gevolgd door een reguliere archeologische opgraving van de uit te graven bouwkuip.
DOI: 10.15156/bio/sh1396099.10fu
title: SH1396099.10FU

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/sh1396100.10fu
title: SH1396100.10FU

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/sh1396102.10fu
title: SH1396102.10FU

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/sh1396104.10fu
title: SH1396104.10FU

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/sh1396105.10fu
title: SH1396105.10FU

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/sh1396106.10fu
title: SH1396106.10FU

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/sh1396110.10fu
title: SH1396110.10FU

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/sh1396111.10fu
title: SH1396111.10FU

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.jsa8vw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solenogastres)",
 "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((-130 -30,-120 -30,-120 -20,-130 -20,-130 -30))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014374-251009101135966/datasets/export for details.
DOI: 10.5281/zenodo.17253517
title: Synthetically Generated CDR

description: Python was used to create the dataset. Starting from a real dataset of 300 rows, we were able to generate synthetic datathat largely preserves the synthetic properties of the real data. How we created the dataset will be presented in the nextparagraphs.




The most important aspect of the CDR is the way the cell id of the telecom tower is chosen. Using the list of all Cell Towers in Romania, after the userchoosed the area of interest, only the cell ids of the towers in this area are stored in the dataset (See the paper listed for the entire procedure). In this way cell_id, longitude and latitude fields are created. Longitude and latitude represent the geographical coordinates of the tower.For example the Romanian tower with cell_id 26.133.003 has longitude 27,60346 and latitude 47,143381.A unique_id is used to differentiate between the different lines in the CDRs used. It is created using uuid4, from theuuid library in python. uuid4 allows the creation of unique ids using random numbers and digits. Example: 49d791e7-a38e-4d33-b947-2e49d3c734b0. The identifying call elements are generated using 15 random numbers from 0 to 9. Forthis, the python random library was used, with the choice method.

 

The caller id is also generated randomly, using the random library from python. Some of the features were generated randomly, and some have been generated synthetically. For generation we usedSynthetic Data Vault (SDV) , which is a library in python for generating synthetic tabular data. The initial CDR waspre-processed so that it could be inserted into the synthetic data generation library, the timestamp column was splitinto several columns in order to generate each detail that could be used in a Machine Learning model.For example, the timestamp of the call for one of the callers is 06.12.2023/13:20:01. Out of this we made multiplefeatures, using the pandas library. After preprocessing, the data is entered into the SDV library. The first step is to generate a metadata for the dataframe used. This step is necessary for calling the method that generates the synthetic data. This is also done via SDV. For the generation we used FAST ML from SDV which is a synthesizer optimized for modeling speed.

The generated dataset preserves (to a large extent) the properties of the real dataset, considering that 2000 data entries were generated from a dataset with only 300 entries.The hour of the call, minute of the call, second of the call columns keep the normal ranges for this type of data, 0-24,0-59, respectively 0-59. 

 

To see an analysis between real and generated data, please check the paper listed in the description.