cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.z9hpea
title: Occurrence Download

description: A dataset containing 368 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Dactylis glomerata L."
 ]
}

 The dataset includes 368 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0187940-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hgsvtt
title: Occurrence Download

description: A dataset containing 957 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tuta absoluta (Meyrick, 1917)"
 ]
}

 The dataset includes 957 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014128-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a38ben
title: Occurrence Download

description: A dataset containing 223 species occurrences available in GBIF matching the query:
{
 "SpeciesKey" : [
 "is one of (Tithonia tubiformis (Jacq.) Cass.)"
 ]
}

 The dataset includes 223 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040935-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.16n9t4
title: Occurrence Download

description: A dataset containing 466 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Asplenium riparium Liebm."
 ]
}

 The dataset includes 466 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017231-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5wj3p5
title: Occurrence Download

description: A dataset containing 227 species occurrences available in GBIF matching the query:
{
 "SpeciesKey" : [
 "is one of (Malephora purpureocrocea (Haw.) Schwantes)"
 ]
}

 The dataset includes 227 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040934-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q43mu3
title: Occurrence Download

description: A dataset containing 146 species occurrences available in GBIF matching the query:
{
 "SpeciesKey" : [
 "is one of (Pinus pinaster Besser, 1809)"
 ]
}

 The dataset includes 146 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040940-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xvn9un
title: Occurrence Download

description: A dataset containing 998 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 12.5759301333778",
 "DecimalLongitude is less than 12.5789896865622",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 998 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014141-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cmff9m
title: Occurrence Download

description: A dataset containing 156 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Histiogamphelus briggsii McCulloch, 1914"
 ]
}

 The dataset includes 156 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0346865-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.85mdnm
title: Occurrence Download

description: A dataset containing 257 species occurrences available in GBIF matching the query:
{
 "SpeciesKey" : [
 "is one of (Derris montana Benth.)"
 ]
}

 The dataset includes 257 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040938-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15080378
title: Near-duplicate build failure detection analysis files and Oodikone dataset

description: Near-duplicate build failure detection analysis files and Oodikone dataset

All of the outputs, intermediate output files, as well as the scripts used for data analysis are included in this package.

Before running any of the scripts, please create a virtual environment using Python 3.11 and install the python libraries in requirements.txt

Here's an example using conda.

 

Create a new conda environment with Python 3.11

conda create -n oodikone python=3.11

Activate the newly created environment

conda activate oodikone

Navigate to the directory containing the replication package

cd [Directory of the replication package]

Install the required dependencies from requirements.txt

pip install -r requirements.txt

The folder paths for the current implementation assume the replication package directory as the root folder.  If you run into errors, most likely they are related to incorrect folder paths, so please check that the folder paths are correct.

The original raw dataset of log files are in the folder "logs_all_original_input".

The OOVD filtered log files are in the folder "logs_all_OOVD_input". 

If you wish to perform OOVD filtering separately, please navigate to the folder "oovd_filtering" and run "create_oovd_filtering.ipynb".

Note that this is unnecessary, as all of the OOVD outputs are already in the folder "logs_all_OOVD_input". 

For Top-K results, please run config_analyzer_top_K.ipynb

For Top-R results, please run config_analyzer_top_R.ipynb
DOI: 10.5281/zenodo.15080379
title: Near-duplicate build failure detection analysis files and Oodikone dataset

description: Near-duplicate build failure detection analysis files and Oodikone dataset

All of the outputs, intermediate output files, as well as the scripts used for data analysis are included in this package.

Before running any of the scripts, please create a virtual environment using Python 3.11 and install the python libraries in requirements.txt

Here's an example using conda.

 

Create a new conda environment with Python 3.11

conda create -n oodikone python=3.11

Activate the newly created environment

conda activate oodikone

Navigate to the directory containing the replication package

cd [Directory of the replication package]

Install the required dependencies from requirements.txt

pip install -r requirements.txt

The folder paths for the current implementation assume the replication package directory as the root folder.  If you run into errors, most likely they are related to incorrect folder paths, so please check that the folder paths are correct.

The original raw dataset of log files are in the folder "logs_all_original_input".

The OOVD filtered log files are in the folder "logs_all_OOVD_input". 

If you wish to perform OOVD filtering separately, please navigate to the folder "oovd_filtering" and run "create_oovd_filtering.ipynb".

Note that this is unnecessary, as all of the OOVD outputs are already in the folder "logs_all_OOVD_input". 

For Top-K results, please run config_analyzer_top_K.ipynb

For Top-R results, please run config_analyzer_top_R.ipynb
DOI: 10.5517/ccdc.csd.cc2lbl4p
title: CCDC 2393452: Experimental Crystal Structure Determination

description: Related Article: Guangpeng Yan|2024|CSD Communication|||
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: 10.5066/p14psvbv
title: Digital Database of a 3D Geological Model of the Denver Basin

description: This digital database release contains elevation surfaces on 24 surfaces representing the tops of geological formations in the Denver Basin. These surfaces were exported as raster data from Leapfrog software, in which the model was constructed. Inputs to the model include well top data compiled from state agencies, formation contacts extracted from the Stage Geological Map Compilation, and structural elevation contours from Colorado and Wyoming state agencies and USGS water studies. These data are not included in this release. However, the sources are documented in the included DataSources table to allow retrieval of source data, should it be desired by the user. Other Non-Spatial tables include a Description of Model Units, describing the geology of each formation included as a unit in the model, a Glossary of terms, and a GeoMaterialDict table with terms common to GeMS-formatted databases. The EntityAndAttribute_DataDictionary_DenverBasin.csv file provides a listing of all outputs included in this release. DenverBasinInputSummaryTable.csv documents settings used to build the model (boundary filter, snapping, data sources, etc.) Three faults were used in the construction of the model. The Rocky Mountain Front reverse fault cuts the model from north to south. The Hartville Fault terminates against the Rocky Mountain Front in Wyoming. Finally, the Laramie Fault of Wyoming forms the edge of the study area in the northwest of the model. These faults are included as grids of points in point feature classes. The Denver Basin is a sedimentary basin primarily located in northeastern Colorado, with portions in Wyoming, Nebraska, and Kansas. The basin is bounded by the Hartville Uplift in Wyoming, the Chadron and Cambridge Arches of Nebraska, the Las Animas Arch in Colorado and Kansas, and the Apishapa Uplift in Colorado. The fault-bounded Rocky Mountain Front forms the western boundary of the model. Thrust faulting and sediment loading associated with the Rocky Mountain Front created the asymmetric shape of the Denver Basin, with a foredeep centered near Boulder, Colorado. Broad changes in geology can be seen in this Denver Basin model, from a Paleozoic-dominated carbonate platform near the Las Animas Arch transitioning to Penn-Perm clastics shed off the Ancestral Rocky Mountains. Other geologic features of interest visible within the model include Red Rocks Amphitheater in Morrison, CO, Garden of the Gods Park near Colorado Springs, and an interpretation of the Ralston Dike and associated Table Mountain volcanics near Golden, Colorado.
DOI: 10.5281/zenodo.14845750
title: Coralysis Vignettes SCE Objects

description: This repository contains SingleCellExperiment (SCE) objects used for running Coralysis vignettes. The purpose of this repository is to facilitate the distribution of medium-to-large-sized SCE objects compatible with Coralysis, allowing users to reproduce various vignettes. All datasets shared through this repository were originally made public by their respective authors, who are properly acknowledged herein, with links to the original publications.

File description: 



pbmc_10Xassays.rds: Two 10X PBMCs 3' assays, V1 and V2, downsampled to 2K cells (1K per assay). Both datasets have been downloaded from 10X website (see source below). The PBMC dataset V1 corresponds to sample pbmc6k and V2 pbmc8k. Cells were annotated using the annotations provided by Korsunsky et al., 2019 (PMID:31740819; Source Data Figure 4 file).





V1: pbmc6k




V2: pbmc8k




covid_Wilk_et_al.rds: Single-cell RNA sequencing atlas of peripheral blood mononuclear cells (PBMCs) from healthy versus COVID-19 infected donors published by Wilk et al., 2020 (PMID:32514174). In addition, the object provides the cell type annotations given by Välikangas et al., 2022 (PMID:35991542). The original dataset has been downsampled to 21K cells (1.5K cells per Donor_full sample; from 44,721 cells) to increase the speed and reduce the computational resources required to reproduce the vignette.
DOI: 10.5281/zenodo.14845751
title: Coralysis Vignettes SCE Objects

description: This repository contains SingleCellExperiment (SCE) objects used for running Coralysis vignettes. The purpose of this repository is to facilitate the distribution of medium-to-large-sized SCE objects compatible with Coralysis, allowing users to reproduce various vignettes. All datasets shared through this repository were originally made public by their respective authors, who are properly acknowledged herein, with links to the original publications.

File description: 



pbmc_10Xassays.rds: Two 10X PBMCs 3' assays, V1 and V2, downsampled to 2K cells (1K per assay). Both datasets have been downloaded from 10X website (see source below). The PBMC dataset V1 corresponds to sample pbmc6k and V2 pbmc8k. Cells were annotated using the annotations provided by Korsunsky et al., 2019 (PMID:31740819; Source Data Figure 4 file).

covid_Wilk_et_al.rds: Single-cell RNA sequencing atlas of peripheral blood mononuclear cells (PBMCs) from healthy versus COVID-19 infected donors published by Wilk et al., 2020 (PMID:32514174). In addition, the object provides the cell type annotations given by Välikangas et al., 2022 (PMID:35991542).
DOI: 10.5281/zenodo.14871436
title: Coralysis Vignettes SCE Objects

description: This repository contains SingleCellExperiment (SCE) objects used for running Coralysis vignettes. The purpose of this repository is to facilitate the distribution of medium-to-large-sized SCE objects compatible with Coralysis, allowing users to reproduce various vignettes. All datasets shared through this repository were originally made public by their respective authors, who are properly acknowledged herein, with links to the original publications.

File description: 



pbmc_10Xassays.rds: Two 10X PBMCs 3' assays, V1 and V2, downsampled to 2K cells (1K per assay). Both datasets have been downloaded from 10X website (see source below). The PBMC dataset V1 corresponds to sample pbmc6k and V2 pbmc8k. Cells were annotated using the annotations provided by Korsunsky et al., 2019 (PMID:31740819; Source Data Figure 4 file).





V1: pbmc6k




V2: pbmc8k




covid_Wilk_et_al.rds: Single-cell RNA sequencing atlas of peripheral blood mononuclear cells (PBMCs) from healthy versus COVID-19 infected donors published by Wilk et al., 2020 (PMID:32514174). In addition, the object provides the cell type annotations given by Välikangas et al., 2022 (PMID:35991542). The original dataset has been downsampled to 21K cells (1.5K cells per Donor_full sample; from 44,721 cells) to increase the speed and reduce the computational resources required to reproduce the vignette.
DOI: 10.15468/dl.uyqtuy
title: Occurrence Download

description: A dataset containing 447 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((59.79002 40.89019,45.53955 69.9907,0.30979 71.02419,-23.85405 44.53065,-13.94068 33.49789,46.15913 19.69792,59.79002 40.89019))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mulinia lateralis (Say, 1822)"
 ]
}

 The dataset includes 447 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000208-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4579ba
title: Occurrence Download

description: A dataset containing 8869 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heteropoda venatoria (Linnaeus, 1767)"
 ]
}

 The dataset includes 8869 records from 109 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000209-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e69wx2
title: Occurrence Download

description: A dataset containing 347420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Material sample, Fossil)",
 "TaxonKey is Megaptera novaeangliae (Borowski, 1781)"
 ]
}

 The dataset includes 347420 records from 562 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000203-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c22yp8
title: Occurrence Download

description: A dataset containing 12315286 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is South Africa",
 "DatasetKey is Southern African Bird Atlas Project 2",
 "Year 2015-2024"
 ]
}

 The dataset includes 12315286 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000204-250214102907787/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14871399
title: Conductivities and excitations of a holographic flavour brane Weyl semimetal --- data release

description: This dataset consists of .csv files containing all of the data used to make the plots appearing in the article Conductivities and excitations of a holographic flavour brane Weyl semimetal. There is also a tex file containing PGFplots code to reproduce the plots.
DOI: 10.5281/zenodo.14871400
title: Conductivities and excitations of a holographic flavour brane Weyl semimetal --- data release

description: This dataset consists of .csv files containing all of the data used to make the plots appearing in the article Conductivities and excitations of a holographic flavour brane Weyl semimetal. There is also a tex file containing PGFplots code to reproduce the plots.
DOI: 10.25345/c5k35ms1z
title: MassIVE MSV000097114 - Stress Granules Underlie Acute Myeloid Leukemia Stem Cell Survival and Stress Adaptation
title: Here we interrogate high throughput in vivo assignments of RNA regulators as dependencies in acutemyeloid leukemia (AML) and the leukemic stem cells (LSCs) that drive the disease to identify that the proteins that comprise dynamic, stress-adaptive ribonucleoproteins known as stress granules(SG) are enriched among the factors essential for LSC-driven leukemic propagation. Focusing on core SG nucleator G3BP1 we dissect the role of SGs in human AML. We further unveil an AML-specific G3BP1 SG proteome to show that consolidated control of innate immune signaling, and apoptosis repression is executed through regional binding specificity ofG3BP1 to highly structured 3' UTRs and cooperation with the RNA helicase UPF1 to mediate transcript decay in SGs.
DOI: 10.5281/zenodo.14782453
title: Benchmarking machine learning approaches in Industrial Punching: A multi-modal Dataset

description: A dataset derived from multiple sensors recorded during an industrial punching process is introduced here. The primary objective of this dataset is to facilitate insights into tool wear dynamics and the correlation of multiple parameters within the punching operation. The data consists of acoustic emission and cutting force signals capturing the tool wear throughout the punch’s life. Additionally, data related to the thickness of the strip material has been recorded. Contrary to previous data records, each manufactured cutting surface is visually documented through images within the process. The Dataset therefore renders multi-modal. In recent studies, the collected data is often not publicly available, which results in a lack of universal benchmark.

This dataset aims to establish a basis for developing machine learning approaches in the domains of stamping, punching and blanking. Consequently, it aims to contribute to the refinement of tool wear prediction models and the optimization of strategies within industrial punching operations. This paper shows the data accusation methods used for the dataset and the structure of the data. Furthermore, use cases of the data are shown.

 

For Information about the data collection, go to: Benchmarking machine learning approaches in Industrial Punching: A multi-modal DatasetFor the Structure of the data, go to: Datasheet for DatasetSupporting Repository for data handling: https://github.com/LepusMaximus/MultiModPunchFunding: The Authors gratefully acknowledge financial support from the interdisciplinary technology network Efficient Production Technology (EffPro), co-funded by the European Union and the Free State of Bavaria from the European Fund for regional development (ERDF) and supported by the University of Applied Sciences Kempten and the Bavarian Ministry of Science and Art.
DOI: 10.5281/zenodo.14782454
title: Benchmarking machine learning approaches in Industrial Punching: A multi-modal Dataset

description: A dataset derived from multiple sensors recorded during an industrial punching process is introduced here. The primary objective of this dataset is to facilitate insights into tool wear dynamics and the correlation of multiple parameters within the punching operation. The data consists of acoustic emission and cutting force signals capturing the tool wear throughout the punch’s life. Additionally, data related to the thickness of the strip material has been recorded. Contrary to previous data records, each manufactured cutting surface is visually documented through images within the process. The Dataset therefore renders multi-modal. In recent studies, the collected data is often not publicly available, which results in a lack of universal benchmark.

This dataset aims to establish a basis for developing machine learning approaches in the domains of stamping, punching and blanking. Consequently, it aims to contribute to the refinement of tool wear prediction models and the optimization of strategies within industrial punching operations. This paper shows the data accusation methods used for the dataset and the structure of the data. Furthermore, use cases of the data are shown.

 

For Information about the data collection, go to: Benchmarking machine learning approaches in Industrial Punching: A multi-modal DatasetFor the Structure of the data, go to: Datasheet for DatasetSupporting Repository for data handling: https://github.com/LepusMaximus/MultiModPunchFunding: The Authors gratefully acknowledge financial support from the interdisciplinary technology network Efficient Production Technology (EffPro), co-funded by the European Union and the Free State of Bavaria from the European Fund for regional development (ERDF) and supported by the University of Applied Sciences Kempten and the Bavarian Ministry of Science and Art.