cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0962158.10fu
title: SH0962158.10FU

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/sh0962159.10fu
title: SH0962159.10FU

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: 10.48597/a5q2-5sg2
title: Inorganics in air and particle phase at Diabla Gora

description: Measurement of Inorganics in air and particle phase at Diabla Gora
DOI: 10.15156/bio/sh0962160.10fu
title: SH0962160.10FU

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: 10.48597/pmya-5aed
title: Inorganics in air and particle phase at Diabla Gora

description: Measurement of Inorganics in air and particle phase at Diabla Gora
DOI: 10.48597/q28n-rtc4
title: Sulphate_corrected at Diabla Gora

description: Measurement of Sulphate_corrected at Diabla Gora
DOI: 10.15156/bio/sh0962161.10fu
title: SH0962161.10FU

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: 10.48597/bmzh-j2hg
title: Sulphate_corrected at Diabla Gora

description: Measurement of Sulphate_corrected at Diabla Gora
DOI: 10.15156/bio/sh0962162.10fu
title: SH0962162.10FU

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: 10.48597/4hhd-r9q6
title: Measurement of Sulphate_corrected at Diabla Gora

description: Measurement of Sulphate_corrected at Diabla Gora
DOI: 10.48597/hqmx-24ss
title: Measurement of Inorganics in air and particle phase at Diabla Gora

description: Measurement of Inorganics in air and particle phase at Diabla Gora
DOI: 10.5281/zenodo.10972888
title: Raw results of the simulation experiments performed in the educacional study evaluating a guaranteed service

description: This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation  results presented in [1].

[1] Suárez-González A., Rodríguez Rubio R., López-Ardao J.C., López-García C, Herrería-Alonso S. (2021). Educational case study: evaluation of guaranteed service on low earth orbit satellite networks. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 215-219. DOI: https://doi.org/10.46354/i3m.2021.emss.029
DOI: 10.5281/zenodo.8246620
title: Statistical Process Control Benchmark Dataset

description: Datasets to the planned publication "Generalized Statistical Process Control via 1D-ResNet Pretraining" by Tobias Schulze, Louis Huebser, Sebastian Beckschulte and Robert H. Schmitt (Chair for Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University)

Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects according to https://doi.org/10.5281/zenodo.8249487). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.

The train data were used to train the G-SPC model. The test data were used for benchmarking purposes

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.
DOI: 10.5281/zenodo.8249487
title: Statistical Process Control Benchmark Dataset

description: Datasets to the planned publication "Generalized Statistical Process Control via 1D-ResNet Pretraining" by Tobias Schulze, Louis Huebser, Sebastian Beckschulte and Robert H. Schmitt (Chair for Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University)

Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects according to https://doi.org/10.5281/zenodo.8249487). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.

The train data were used to train the G-SPC model. The test data were used for benchmarking purposes

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.
DOI: 10.15468/dl.i4woru
title: Occurrence Download

description: A dataset containing 239821 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Material sample"
 ]
 },
 "Depth 0.0-9999.0m",
 "Year 1781-2017",
 "Geometry POLYGON ((191.25 -0.7031073524364783, 283.359375 -1.4061088354351594, 262.265625 19.973348786110602, 243.28125 37.04817289772996, 230.62499999999997 58.44773280389084, 203.55468749999997 62.431074232920906, 201.09375 70.1403642720717, 181.40625 70.1403642720717, 169.45312499999997 62.91523303947614, 146.6015625 62.103882522897855, 118.828125 37.16031654673677, 99.140625 -1.4061088354351594, 191.25 -0.7031073524364783))",
 "HasCoordinate is true",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 239821 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004436-171025204727650/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10912708
title: Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches

description: Compressed trajectories files of natural and synthetic ligands (TUG-891, alpha-Linolenic acid, Linoleic acid and Oleic acid) as supporting information for a research article in Molecular Informatics exploring binding hotspots of FFA4.

Each compressed trajectories comprise of a pdb file (xx_Rx.pdb) and its associated trajectory file (xx_Rx_TRJ.trr ) to be read using vmd.
DOI: 10.5281/zenodo.10912709
title: Distinct binding hotspots for natural and synthetic agonists of FFA4 from in silico approaches

description: Compressed trajectories files of natural and synthetic ligands (TUG-891, alpha-Linolenic acid, Linoleic acid and Oleic acid) as supporting information for a research article in Molecular Informatics exploring binding hotspots of FFA4.

Each compressed trajectories comprise of a pdb file (xx_Rx.pdb) and its associated trajectory file (xx_Rx_TRJ.trr ) to be read using vmd.
DOI: 10.5281/zenodo.10972887
title: Raw results of the simulation experiments performed in the educacional study evaluating a guaranteed service

description: This zip file contains the simulation results, gnuplot script and data files needed to generate the figures showing the simulation  results presented in [1].

[1] Suárez-González A., Rodríguez Rubio R., López-Ardao J.C., López-García C, Herrería-Alonso S. (2021). Educational case study: evaluation of guaranteed service on low earth orbit satellite networks. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 215-219. DOI: https://doi.org/10.46354/i3m.2021.emss.029
DOI: 10.5281/zenodo.10973053
title: HER2 RNA Molecular Simulation Data

description: This is a data file for the HER2 RNA molecular simulation research by Arli Aditya Parikesit. More on me, please kindly check this link: orcid.org/0000-0001-8716-3926
DOI: 10.5281/zenodo.10973054
title: HER2 RNA Molecular Simulation Data

description: This is a data file for the HER2 RNA molecular simulation research by Arli Aditya Parikesit. More on me, please kindly check this link: orcid.org/0000-0001-8716-3926
DOI: 10.15468/dl.t2hywq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Subantarctic Fur sealsat Heard Island, 1987/88"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008300-210914110416597/datasets/export for details.
DOI: 10.5281/zenodo.10973055
title: Canberra PIPS detector FD 50-14-300 RM (aka Vanessa II): performance data

description: Model: FD 50-14-300 RM

Serial number: 149187

Lot number: 3705.326D.377.21

CERN internal name: Vanessa II
DOI: 10.15468/dl.zzemw4
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Kovalevskiella kovalevskiana (Kirp.) Kamelin"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0137834-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10973056
title: Canberra PIPS detector FD 50-14-300 RM (aka Vanessa II): performance data

description: Model: FD 50-14-300 RM

Serial number: 149187

Lot number: 3705.326D.377.21

CERN internal name: Vanessa II
DOI: 10.15468/dl.jgnxgh
title: Occurrence Download

description: A dataset containing 16604 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Iberian Odonata distribution: data of the BOS Arthropod Collection (Univ. Oviedo, Spain)"
 ]
}

 The dataset includes 16604 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004436-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10889452
title: Dataset on waste separation in Indonesia

description: The dataset contains data on observed waste separation in a gated community in Bogor, Indonesia. Get more information on the project: Creating Value in Plastics through Digital Technology - Prevent Waste Alliance (prevent-waste.net) 

 

The data is provided in the following formats:



 .csv file  (105281-zenodo-10889452_anon-df_2024-04-15_V1)  [character set "West Europe / Windows-1252"]


Additional files:



.txt documentation of VBA code for data anonymising (documentation_makro_anonymisingHH_ID.txt) 
description: The data was collected during a step wedged randomised controlled trial. Data was collected by observing artifacts of behaviour, i.e., waste disposed in curbside containers, rather than relying on self-reporting. A team of enumerators preceded the regular waste collection trucks in nine weeks between August 2022 and November 2023. A total of 881 unique households were visited. These households represented the entirety of the studied community. Visits were scheduled once in the baseline week and twice every subsequent week of measurement over four months. Measurements were repeated once in May 2023 and once in November 2023. This procedure resulted in 14521 unique observations. On each visit, enumerators visually assessed the waste discarded in the blue bins (reserved for inorganic materials) and the other bin (for residual materials), and took note which materials, if any, the bins contained. Enumerators also coded for additional observations like the absence of residence or the inaccessibility of the bins for inspection.

In the original data set, the household ID (HH_ID) was built from the subare code (cluster), street (Blok) and house number (No). To protect personal data of observed households, the HH_ID was anonymised using a VBA Code which is described om the file "documentation_makro_anonymisingHH_ID". 

Note: Information of proximity effects / spatial dependency analysis was lost with anonymisation. 

 

Explanation of columns: "Cluster": sub-area of the analyzed total area (gated community) "Stiker di tong biru? ya=1, tidak=0": Does the bin carry a sticker acting as a resident's self-commitment to separate waste | 0=No und 1=Yes,"Pilah (separated: only inorganics)": Recycling bin content, captures whether bin contains only inorganic materials. Pilah = 1 indicates correct separation."Pilah (separated: only organics/ residuals)": Bin content for residual waste bin, captures whether bin contains only organic materials. "date": Date of observation"week": consecutive numbering of the weeks during which data collection took place"treated": indicates whether household has received a treatment
DOI: 10.5281/zenodo.10972951
title: 101 million cell case simulation of subsurface CO2 storage simulated with OPM Flow

description: This dataset contains both the simulation description (DECK files) and the simulation output for a SPE11C derived test case with 101 million grid cells being simulated for 1000 years. The simulation was carried out on the Karolina cluster at IT4I using 32 CPU nodes on the project DD-23-66. The version of OPM Flow was a pre-2024.04 release version.This upload is a preliminary upload as the current output is corrupt (the output needs to be read carefully in MRST/MATLAB). An updated upload is soon to follow. This upload is for perservation of historical data.
DOI: 10.5281/zenodo.10972952
title: 101 million cell case simulation of subsurface CO2 storage simulated with OPM Flow

description: This dataset contains both the simulation description (DECK files) and the simulation output for a SPE11C derived test case with 101 million grid cells being simulated for 1000 years. The simulation was carried out on the Karolina cluster at IT4I using 32 CPU nodes on the project DD-23-66. The version of OPM Flow was a pre-2024.04 release version.This upload is a preliminary upload as the current output is corrupt (the output needs to be read carefully in MRST/MATLAB). An updated upload is soon to follow. This upload is for perservation of historical data.
DOI: 10.15468/dl.6k0rfj
title: Occurrence Download

description: A dataset containing 139 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-81.8811 33.61462,-85.26489 32.82421,-85.13306 30.24009,-80.82642 30.20211,-80.5188 32.17561,-81.8811 33.61462))",
 "HasCoordinate is true",
 "TaxonKey is Taxodium distichum (L.) Rich.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 139 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007655-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w3jmnb
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Magnolia quetzal and Magnolia mayae, a new species and a new record, respectively, for the flora of Guatemala"
 ]
}

 The dataset's 2 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0198427-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10889451
title: Dataset on waste separation in Indonesia

description: The dataset contains data on observed waste separation in a gated community in Bogor, Indonesia. Get more information on the project: Creating Value in Plastics through Digital Technology - Prevent Waste Alliance (prevent-waste.net) 

 

The data is provided in the following formats:



 .csv file  (105281-zenodo-10889452_anon-df_2024-04-15_V1)  [character set "West Europe / Windows-1252"]


Additional files:



.txt documentation of VBA code for data anonymising (documentation_makro_anonymisingHH_ID.txt) 
description: The data was collected during a step wedged randomised controlled trial. Data was collected by observing artifacts of behaviour, i.e., waste disposed in curbside containers, rather than relying on self-reporting. A team of enumerators preceded the regular waste collection trucks in nine weeks between August 2022 and November 2023. A total of 881 unique households were visited. These households represented the entirety of the studied community. Visits were scheduled once in the baseline week and twice every subsequent week of measurement over four months. Measurements were repeated once in May 2023 and once in November 2023. This procedure resulted in 14521 unique observations. On each visit, enumerators visually assessed the waste discarded in the blue bins (reserved for inorganic materials) and the other bin (for residual materials), and took note which materials, if any, the bins contained. Enumerators also coded for additional observations like the absence of residence or the inaccessibility of the bins for inspection.

In the original data set, the household ID (HH_ID) was built from the subare code (cluster), street (Blok) and house number (No). To protect personal data of observed households, the HH_ID was anonymised using a VBA Code which is described om the file "documentation_makro_anonymisingHH_ID". 

Note: Information of proximity effects / spatial dependency analysis was lost with anonymisation. 

 

Explanation of columns: "Cluster": sub-area of the analyzed total area (gated community) "Stiker di tong biru? ya=1, tidak=0": Does the bin carry a sticker acting as a resident's self-commitment to separate waste | 0=No und 1=Yes,"Pilah (separated: only inorganics)": Recycling bin content, captures whether bin contains only inorganic materials. Pilah = 1 indicates correct separation."Pilah (separated: only organics/ residuals)": Bin content for residual waste bin, captures whether bin contains only organic materials. "date": Date of observation"week": consecutive numbering of the weeks during which data collection took place"treated": indicates whether household has received a treatment