cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0074921.10fu
title: SH0074921.10FU

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: 10.13145/bacdive6873.20251217.10
title: Listeria monocytogenes (Murray et al. 1926) Pirie 1940

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0074922.10fu
title: SH0074922.10FU

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: 10.13145/bacdive6874.20241212.9.2
title: Listeria monocytogenes (Murray et al. 1926) Pirie 1940

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive6874.20251217.10
title: Listeria monocytogenes (Murray et al. 1926) Pirie 1940

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0074923.10fu
title: SH0074923.10FU

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: 10.13145/bacdive6875.20250331.9.3
title: Listeria monocytogenes (Murray et al. 1926) Pirie 1940

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive6875.20251217.10
title: Listeria monocytogenes (Murray et al. 1926) Pirie 1940

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0074924.10fu
title: SH0074924.10FU

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: 10.13145/bacdive6876.20241212.9.2
title: Listeria seeligeri Rocourt and Grimont 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive6876.20251217.10
title: Listeria seeligeri Rocourt and Grimont 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0074925.10fu
title: SH0074925.10FU

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: 10.13145/bacdive6877.20241212.9.2
title: Listeria welshimeri Rocourt and Grimont 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0074926.10fu
title: SH0074926.10FU

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: 10.13145/bacdive6877.20251217.10
title: Listeria welshimeri Rocourt and Grimont 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive6878.20250331.9.3
title: Listeria welshimeri Rocourt and Grimont 1983

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15468/dl.kdcvva
title: Occurrence Download

description: A dataset containing 769241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "DatasetKey is one of (EOD – eBird Observation Dataset, International Barcode of Life project (iBOL), Insecta of Costa Rica (INBio), Plantae of Costa Rica (INBio), Tropicos MO Specimen Data, INSDC Sequences, Observation.org, Nature data from around the World, AntWeb, herbario, Tropicos Specimens Non-MO)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (e2e717bf-551a-4917-bdc9-4fa0f342c530, b3bf3a83-1f68-4541-a03f-330d30d567f5, 5c7a5c20-1bd0-11d8-a2da-b8a03c50a862, 90fd6680-349f-11d8-aa2d-b8a03c50a862, ada9d123-ddb4-467d-8891-806ea8d94230, b554c320-0560-11d8-b851-b8a03c50a862, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f, 66522820-055c-11d8-b84e-b8a03c50a862, 036cdc50-e7f5-11d9-9a0e-b8a03c50a862)",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 769241 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000668-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10889780
title: urbisphere_gb-london_UR-3: Transport database derived from UK road networks, OSM, and TfL public transport timetables, for modelling in London, UK
title: urbisphere–London Data Release and Technical Documentation

description: Files included in this archive

Documentation



 urbisphere_London_transport_database.pdf


JSON Database files (JSON: https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml (last accessed: 30/3/2024))



driving_transport.json  



Database of driving routes



cycling_transport.json



Database of cycling routes



walking_transport.json



Database of walking routes



public_transport.json



Database of public transport routes




Python 3.9 Code



London_travel_dictionaries.py



to create databases



assign_speed_limits.py



 to assign OSM speed limits to each road in GLA



reduce_sub_services.py



 to group transport routes within the TfL timetables




Data purpose

This dataset contains transport routes for walking, driving, cycling, and public transport (train, tube, and bus) within the Greater London (GLA), which can be used for simulations of human behaviour and movement, for example using agent-based models (e.g. Capel-Timms et al. 2021, McGrory et al. 2024b).

Associated publications



Hertwig et al. 2024b: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863

Hertwig et al. 2024c: urbisphere_gb-london_UR-7: Gridded total road lengths by type for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset].. Zenodo. https://doi.org/10.5281/zenodo.10889841

McGrory et al. 2024a: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation].. Zenodo. https://doi.org/10.5281/zenodo.10889900
DOI: 10.5281/zenodo.10889779
title: urbisphere_gb-london_UR-3: Transport database derived from UK road networks, OSM, and TfL public transport timetables, for modelling in London, UK
title: urbisphere–London Data Release and Technical Documentation

description: Files included in this archive

Documentation



 urbisphere_London_transport_database.pdf


JSON Database files (JSON: https://www.loc.gov/preservation/digital/formats/fdd/fdd000381.shtml (last accessed: 30/3/2024))



driving_transport.json  



Database of driving routes



cycling_transport.json



Database of cycling routes



walking_transport.json



Database of walking routes



public_transport.json



Database of public transport routes




Python 3.9 Code



London_travel_dictionaries.py



to create databases



assign_speed_limits.py



 to assign OSM speed limits to each road in GLA



reduce_sub_services.py



 to group transport routes within the TfL timetables




Data purpose

This dataset contains transport routes for walking, driving, cycling, and public transport (train, tube, and bus) within the Greater London (GLA), which can be used for simulations of human behaviour and movement, for example using agent-based models (e.g. Capel-Timms et al. 2021, McGrory et al. 2024b).

Associated publications



Hertwig et al. 2024b: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863

Hertwig et al. 2024c: urbisphere_gb-london_UR-7: Gridded total road lengths by type for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset].. Zenodo. https://doi.org/10.5281/zenodo.10889841

McGrory et al. 2024a: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation].. Zenodo. https://doi.org/10.5281/zenodo.10889900
DOI: 10.5281/zenodo.14860536
title: Data from Glacier ice melt dynamics revealed by terrestrial radar interferometry - (submitted to JGR Earth Surface)

description: 1. Terrestrial interferometry data. 

Terrestrial interferometry data used in this study are provided below and divided in four sub-categories. 

These are: a) 2D Interferograms, b) cumulated map of displacement, c) timeseries of cumulated displacement at Zone A and d) computed daily displacement at Zone A. 

In all dataset positive displacement is given in mm away from the line of sight of the sensor. 

1a) 2D Interferograms

2D interferograms in radar coordinates (Figure 5) are provided in nine separate files. The naming of the file is in the format TRI_MMDDHHmm_MMDDHHmm.txt

1b) Cumulated map of displacement

The georeferenced point-cloud of the cumulated displacement (Figure 6c) is provided in TRI_2022_07_06_00_39__2022_10_02_01_33_3PC.txt

1c) Time series of cumulated displacement in Zone A

The temporal evolution of the cumulated displacement at zone A during the entire monitoring period (Figure 7) is provided in TRI_ZONEA_TIMESERIES.csv  

1d) Computed daily displacement in Zone A 

Computed daily displacement at Zone A (mm/day), displayed in Figure 9a and derived from the dataset (1c) is provided in

TRI_ZONEA_DAILY_DISPLACEMENT.csv

2. Meteorological data.

Measured hourly temperature at the Punta Rocca weather station are provided in Meteo_PRHT.csv 

3. High resolution topography. 

High resolution topography data are provided in two separate georeferenced datasets: 

TOPO_20220708_pc.txt 

TOPO_20220920_dsm.txt
DOI: 10.5281/zenodo.15000634
title: Synthetic data for assessing and comparing local post-hoc explanation of detected process shift

description: Synthetic data for assessing and comparing local post-hoc explanation of detected process shift

DOI

10.5281/zenodo.15000635

 

Synthetic dataset contains data used in experiment described in article submitted to Computers in Industry journal entitled 

Assessing and Comparing Local Post-hoc Explanation for Shift Detection in Process Monitoring.

The citation will be updated immediately after the article will be accepted. 

 

Particular data.mat files are stored in a subfolder structure, which clearly assigns the particular file to

on of the tested cases. 

For example, data for experiments with normally distributed data, known number of shifted variables and 5 variables are stored in path \normal\known_number\5_vars\rho0.1.

The meaning of particular folders is explained here:

 

normal - all variables are normally distributed

not-normal - copula based multivariate distribution based on normal and gamma marginal distributions and defined correlation

 

known_number  - known number of shifted variables (methods used this information, which is not available in real world) 

unknown_number  - unknown number of shifted variables, realistic case

 

 

2_vars - data with 2 variables (n=2)

...

10_vars - data with 10 variables (n=2)

 

rho0.1 - correlation among all variables is 0.1

...

rho0.9 - correlation among all variables is 0.9

 

Each data.mat file contains the following variables:

 

  LIME_res      nval x n results of LIME explanation

  MYT_res       nval x n    results of MYT explanation

  NN_res        nval x n results of ANN explanation

  X                  p x 11000         Unshifted data              

  S                  n x n sigma matrix (covariance matrix) for the unshifted data              

  mu              1xn            mean parameter for the unshifted data             

  n                 1x1            number of variables (dimensionality)             

  trn_set         n x ntrn x 2    train set for ANN explainer, 

                      trn_set(:,:,1) are values of variables from shifted process

                      trn_set(:,:,2) labels denoting which variables are shifted 

                      trn_set(i,j,2) is 1 if ith variable of jth sample trn_set(:,j,1) is shifted

  val_set         n x 95 x 2          validation set used for testing and generating LIME_res, MYT_res and NN_res
DOI: 10.5281/zenodo.15000635
title: Synthetic data for assessing and comparing local post-hoc explanation of detected process shift

description: Synthetic data for assessing and comparing local post-hoc explanation of detected process shift

DOI

10.5281/zenodo.15000635

 

Synthetic dataset contains data used in experiment described in article submitted to Computers in Industry journal entitled 

Assessing and Comparing Local Post-hoc Explanation for Shift Detection in Process Monitoring.

The citation will be updated immediately after the article will be accepted. 

 

Particular data.mat files are stored in a subfolder structure, which clearly assigns the particular file to

on of the tested cases. 

For example, data for experiments with normally distributed data, known number of shifted variables and 5 variables are stored in path \normal\known_number\5_vars\rho0.1.

The meaning of particular folders is explained here:

 

normal - all variables are normally distributed

not-normal - copula based multivariate distribution based on normal and gamma marginal distributions and defined correlation

 

known_number  - known number of shifted variables (methods used this information, which is not available in real world) 

unknown_number  - unknown number of shifted variables, realistic case

 

 

2_vars - data with 2 variables (n=2)

...

10_vars - data with 10 variables (n=2)

 

rho0.1 - correlation among all variables is 0.1

...

rho0.9 - correlation among all variables is 0.9

 

Each data.mat file contains the following variables:

 

  LIME_res      nval x n results of LIME explanation

  MYT_res       nval x n    results of MYT explanation

  NN_res        nval x n results of ANN explanation

  X                  p x 11000         Unshifted data              

  S                  n x n sigma matrix (covariance matrix) for the unshifted data              

  mu              1xn            mean parameter for the unshifted data             

  n                 1x1            number of variables (dimensionality)             

  trn_set         n x ntrn x 2    train set for ANN explainer, 

                      trn_set(:,:,1) are values of variables from shifted process

                      trn_set(:,:,2) labels denoting which variables are shifted 

                      trn_set(i,j,2) is 1 if ith variable of jth sample trn_set(:,j,1) is shifted

  val_set         n x 95 x 2          validation set used for testing and generating LIME_res, MYT_res and NN_res
DOI: 10.15468/dl.gct5fx
title: Occurrence Download

description: A dataset containing 184956147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "Year 1950-2025"
 ]
}

 The dataset includes 184956147 records from 25212 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000647-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15205959
title: Dataset: Development of Mobile Learning Based on Augmented Reality to Strengthen Student Digital Ethics
DOI: 10.5281/zenodo.15205960
title: Dataset: Development of Mobile Learning Based on Augmented Reality to Strengthen Student Digital Ethics
DOI: 10.15468/dl.nhebxq
title: Occurrence Download

description: A dataset containing 116 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Zamenis hohenackeri (Strauch, 1873)"
 ]
}

 The dataset includes 116 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020752-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1070840.10fu
title: SH1070840.10FU

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.