cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.73q5wx
title: Occurrence Download

description: A dataset containing 1114458 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((32.05603 25.45211,37.02039 25.45211,37.02039 29.92877,32.05603 29.92877,32.05603 25.45211))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 1114458 records from 152 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033645-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12699165
title: TIMCI Kenya pre-post dataset

description: This dataset contains data from the TIMCI (Tools for Integrated Management of Childhood Illness) quasi-experimental pre-post study in Kenya (NCT05065320), which was conducted by the University of Nairobi (UoN) in collaboration with the Swiss Tropical & Public Health Institute (Swiss TPH). Data were collected with ODK between September 13, 2021 and February 8, 2023 in 19 facilities.

To request access to this dataset:



Contact the TIMCI consortium directly

Clearly state your intended purpose for data analysis


When using the data, please ensure proper attribution
DOI: 10.5281/zenodo.10282699
title: Dataset and Code for Manuscript "Cell sorting based on pulse shapes from angle resolved detection of scattered light"

description: Dataset and code for the cell cycle analysis and cluster selection for sorting:



ReadMe file with explanations of the data set and analysis

Python scripts for converting the data and to reproduce the sort cluster selection

binary data files containing pulse shapes and wavelet transform coefficients

FSC data files containing the respective common flow cytometry parameters

text files with event indices that represent the gating
DOI: 10.6084/m9.figshare.26302156
title: Health Research evidence-raw Data.sav

description: This research intended to analyze the current usage of health research evidence in health planning, determinants, and readiness to use knowledge translation tools among planning teams in Tanzania. Specifically, the study aims to 1) analyze the current usage of health research evidence among planning team members at the regional and council levels, 2) analyze the capability for the use of health research evidence among planning team members at regional and council levels, 3) analyze the opportunities for the use of health research evidence among health planning members at regional and council levels, 4) to identify the motivations for the use of health research evidence among health planning team members at regional and council levels, and 5) to assess the readiness of the planning team members on the use of knowledge translation tools. The study employed an exploratory mixed-method study design. It was conducted in nine (9) regions and eighteen (18) Councils of Tanzania Mainland involving the health planning team members.
DOI: 10.6084/m9.figshare.26302156.v1
title: Health Research evidence-raw Data.sav

description: This research intended to analyze the current usage of health research evidence in health planning, determinants, and readiness to use knowledge translation tools among planning teams in Tanzania. Specifically, the study aims to 1) analyze the current usage of health research evidence among planning team members at the regional and council levels, 2) analyze the capability for the use of health research evidence among planning team members at regional and council levels, 3) analyze the opportunities for the use of health research evidence among health planning members at regional and council levels, 4) to identify the motivations for the use of health research evidence among health planning team members at regional and council levels, and 5) to assess the readiness of the planning team members on the use of knowledge translation tools. The study employed an exploratory mixed-method study design. It was conducted in nine (9) regions and eighteen (18) Councils of Tanzania Mainland involving the health planning team members.
DOI: 10.15468/dl.97mav9
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample)",
 "Country is India",
 "TaxonKey is Lissemys Smith, 1931"
 ]
}

 The dataset includes 174 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033653-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10854172
title: Replication package for article 'Parallel Program Analysis on Path Ranges'

description: This Replication package contains all the results for the article "Parallel Program Analysis on Path Ranges"


Abstract. Symbolic execution is a software verification technique symbolically running programs and thereby checking for bugs.  
Ranged symbolic execution  
performs symbolic execution on program parts, so called {\em path ranges}, in parallel.
Due to the parallelism, verification is accelerated and hence scales to larger programs.


In this paper, we discuss a generalization of ranged symbolic execution to arbitrary program analyses.
More specifically, we present a verification approach that splits programs into path ranges and
then runs arbitrary analyses on the ranges in parallel. Our approach in particular allows to run {\em different}
analyses on different program parts.
We have implemented this generalization on top of  the tool \textsc{CPAchecker} and evaluated it on programs from the SV-COMP benchmark. Our evaluation shows that verification can benefit from the parallelisation of the verification task,
but also needs a form of work stealing (between analysis) as to become efficient.
DOI: 10.5281/zenodo.8398988
title: Replication package for article 'Parallel Program Analysis on Path Ranges'

description: This Replication package contains all the results for the article "Parallel Program Analysis on Path Ranges"


Abstract. Symbolic execution is a software verification technique symbolically running programs and thereby checking for bugs.  
Ranged symbolic execution  
performs symbolic execution on program parts, so called {\em path ranges}, in parallel.
Due to the parallelism, verification is accelerated and hence scales to larger programs.


In this paper, we discuss a generalization of ranged symbolic execution to arbitrary program analyses.
More specifically, we present a verification approach that splits programs into path ranges and
then runs arbitrary analyses on the ranges in parallel. Our approach in particular allows to run {\em different}
analyses on different program parts.
We have implemented this generalization on top of  the tool \textsc{CPAchecker} and evaluated it on programs from the SV-COMP benchmark. Our evaluation shows that verification can benefit from the parallelisation of the verification task,
but also needs a form of work stealing (between analysis) as to become efficient.
DOI: 10.15468/dl.zsv926
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Panthera pardus (Linnaeus, 1758)"
 ]
}

 The dataset includes 32 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033655-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ega6w
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((25.99696 41.19382,26.07561 41.18965,26.07707 41.26562,26.00957 41.26689,25.99696 41.19382))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033656-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12799640
title: European Social Survey Data Round 1-11 merged
DOI: 10.5281/zenodo.12799641
title: European Social Survey Data Round 1-11 merged
DOI: 10.15468/dl.hxasen
title: Occurrence Download

description: A dataset listing the 5749 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Côte d’Ivoire",
 "TaxonKey is Insecta"
 ]
}

 The dataset's 5749 records were derived from 232 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033654-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11583/dtu.25562364.v1
title: Phase field data

description: This dataset comprises tailored phase field prediction data generated using an innovative automated workflow designed to offer insights into complex phenomena while minimizing computational expenses. The dataset aims to facilitate benchmarking of new algorithms in phase field prediction, emphasizing accessibility and utility for researchers. The data creation process is detailed, focusing on streamlining data collection and preparation. Validation of the dataset's effectiveness is conducted through a benchmark experiment utilizing U-Net regression, a widely adopted neural network architecture. Results showcase competitive performance of the U-Net model, akin to previous state-of-the-art methods. This dataset not only serves as a valuable resource for the phase field prediction community but also highlights the potential of U-Net regression, fostering further advancements in the field.The linked code can be found under https://github.com/laura-rieger/phasefield_benchmark and describes in detail how the dataset is to be used.<br>
DOI: 10.11583/dtu.26325274
title: Phase Field Raw Data

description: This dataset is the raw data for the dataset found at https://data.dtu.dk/articles/dataset/Phase_field_data/25562364
It comprises tailored phase field prediction data generated using an innovative automated workflow designed to offer insights into complex phenomena while minimizing computational expenses. The dataset aims to facilitate benchmarking of new algorithms in phase field prediction, emphasizing accessibility and utility for researchers. The data creation process is detailed, focusing on streamlining data collection and preparation. Validation of the dataset's effectiveness is conducted through a benchmark experiment utilizing U-Net regression, a widely adopted neural network architecture. Results showcase competitive performance of the U-Net model, akin to previous state-of-the-art methods. This dataset not only serves as a valuable resource for the phase field prediction community but also highlights the potential of U-Net regression, fostering further advancements in the field.
The linked code can be found under https://github.com/laura-rieger/phase_field_benchmark and describes in detail how the dataset is to be used.
DOI: 10.11583/dtu.26325274.v1
title: Phase Field Raw Data

description: This dataset is the raw data for the dataset found at https://data.dtu.dk/articles/dataset/Phase_field_data/25562364
It comprises tailored phase field prediction data generated using an innovative automated workflow designed to offer insights into complex phenomena while minimizing computational expenses. The dataset aims to facilitate benchmarking of new algorithms in phase field prediction, emphasizing accessibility and utility for researchers. The data creation process is detailed, focusing on streamlining data collection and preparation. Validation of the dataset's effectiveness is conducted through a benchmark experiment utilizing U-Net regression, a widely adopted neural network architecture. Results showcase competitive performance of the U-Net model, akin to previous state-of-the-art methods. This dataset not only serves as a valuable resource for the phase field prediction community but also highlights the potential of U-Net regression, fostering further advancements in the field.
The linked code can be found under https://github.com/laura-rieger/phase_field_benchmark and describes in detail how the dataset is to be used.
DOI: 10.5281/zenodo.12799652
title: Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."

description: Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
DOI: 10.5281/zenodo.12799653
title: Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."

description: Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
DOI: 10.5281/zenodo.10958661
title: Replication Package of the paper: "A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems"

description: # README## _Replication Package for A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems_

This package contains several files and a folder with both code and data used within the context of the study. 

## output_StaticAnalysis.csvThis file contains the results of the manual annotation aimed at verifying whether a static code analysis tool can be used to pinpoint the presence of SATD and, more specifically, DL-SATD. For each SATD instance, you can find a list of warnings impacting the area of the code where the SATD is, as well as the outcome of our manual annotation. 

## dependentsTensorflow.csvThis file contains the list of dependents of the TensorFlow DL framework together with the number of stars and the number of forks. 

## dependentsTorch.csvThis file contains the list of dependents of the PyTorch DL framework, together with the number of stars and the number of forks.

## 100Projects.csvThis file contains the list of 100 open-source Python projects importing at least one among Tensorflow or PyTorch that we have used as the initial set for our study.  

## SATDTensorFlow.csvThis file contains the list of SATD detected for the 50 Python open-source projects relying on TensorFlow. Each line contains the name of the project, the path to the file in which the SATD has been detected, the line in the file where the SATD comment starts, and the SATD comment. 

## SATDTorch.csvThis file contains the list of SATD detected for the 50 Python open-source projects relying on PyTorch. Each line contains the name of the project, the path to the file in which the SATD has been detected, the line in the file where the SATD comment starts, and the SATD comment.

## SampledSATD.csvThis file contains the list of the 443 SATD comments used to determine the DL-specific SATD Taxonomy. Each line contains the link to the SATD, followed by the SATD comment body, and two identifiers used to determine the context of the SATD comment (used to properly select the presence of static code analysis tools warnings).  

## FinalValidation.csvThis file contains the outcome of the manual validation of the 443 SATD in our sample. 

## MappingWithHumbatovaEtAl.xlsThis file contains the outcome of the mapping between our DL-specific SATD taxonomy and the taxonomy of DL-bugs by Humbatova et al. 

## extractSATDComments.pyThis file contains the source code used to analyze the 100 projects in our study. Specifically, for each project and each Python file within it, the script checks whether the file imports one of the two DL frameworks used in the context of the study. If this is the case, it extracts all comments within it and re-implements the KL-SATD to check whether the comment is a SATD candidate. 

## runAscat.pyThis file contains the source code used to run Prospector as an aggregator of static code analysis tools for Python. 

## CompleteStaticAnalysisWarningsThis directory contains the outputs of Prospector (one for each studied project, in JSON format)
DOI: 10.5281/zenodo.10958662
title: Replication Package of the paper: "A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems"

description: This is the replication package of the paper: "A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems".

It contains:

- List of TensorFlow and PyTorch dependents- List of projects initially mined- CSV with all SATD- List of sampled SATD- Spreadsheets with the various rounds (remove the maybe)- Spreadsheet with the final taxonomy- Outputs of the static analysis tools
DOI: 10.5281/zenodo.10964285
title: Replication Package of the paper: "A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems"

description: # README## _Replication Package for A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems_

This package contains several files and a folder with both code and data used within the context of the study. 

## output_StaticAnalysis.csvThis file contains the results of the manual annotation aimed at verifying whether a static code analysis tool can be used to pinpoint the presence of SATD and, more specifically, DL-SATD. For each SATD instance, you can find a list of warnings impacting the area of the code where the SATD is, as well as the outcome of our manual annotation. 

## dependentsTensorflow.csvThis file contains the list of dependents of the TensorFlow DL framework together with the number of stars and the number of forks. 

## dependentsTorch.csvThis file contains the list of dependents of the PyTorch DL framework, together with the number of stars and the number of forks.

## 100Projects.csvThis file contains the list of 100 open-source Python projects importing at least one among Tensorflow or PyTorch that we have used as the initial set for our study.  

## SATDTensorFlow.csvThis file contains the list of SATD detected for the 50 Python open-source projects relying on TensorFlow. Each line contains the name of the project, the path to the file in which the SATD has been detected, the line in the file where the SATD comment starts, and the SATD comment. 

## SATDTorch.csvThis file contains the list of SATD detected for the 50 Python open-source projects relying on PyTorch. Each line contains the name of the project, the path to the file in which the SATD has been detected, the line in the file where the SATD comment starts, and the SATD comment.

## SampledSATD.csvThis file contains the list of the 443 SATD comments used to determine the DL-specific SATD Taxonomy. Each line contains the link to the SATD, followed by the SATD comment body, and two identifiers used to determine the context of the SATD comment (used to properly select the presence of static code analysis tools warnings).  

## FinalValidation.csvThis file contains the outcome of the manual validation of the 443 SATD in our sample. 

## MappingWithHumbatovaEtAl.xlsThis file contains the outcome of the mapping between our DL-specific SATD taxonomy and the taxonomy of DL-bugs by Humbatova et al. 

## extractSATDComments.pyThis file contains the source code used to analyze the 100 projects in our study. Specifically, for each project and each Python file within it, the script checks whether the file imports one of the two DL frameworks used in the context of the study. If this is the case, it extracts all comments within it and re-implements the KL-SATD to check whether the comment is a SATD candidate. 

## runAscat.pyThis file contains the source code used to run Prospector as an aggregator of static code analysis tools for Python. 

## CompleteStaticAnalysisWarningsThis directory contains the outputs of Prospector (one for each studied project, in JSON format)
DOI: 10.48612/msupe/x3vg-pk8r-emmx
title: Диагностика развития умственных способностей и произвольности с помощью методик, созданных под руководством Л.А. Венгера (выборка стандартизации для бланковой версии)

description: В базе приводятся результаты измерения умственных (сенсорных и интеллектуальных) способностей и произвольности. Измерение проведено с помощью методик, разработанных под руководством Л.А. Венгера: 1) Методика 1 "Произвольность"; 2) Методика 2 "Эталоны"; 3) Методика 3 "Перцептивное моделирование"; 4) Методика 4 "Схематизация"; Методика 5 "Систематизация". Выборка: дети, посещавшие старшую дошкольную группу (5-6 лет, n = 223) и дети, посещавшие подготовительную к школе группу детского сада (6-7 лет, n = 268) из нескольких детских садов города Москва, городов Московской области и города Смоленск. Данные собирались с целью определения статистических норм для этих методик на современной выборке детей.

Ключевые слова: умственные способности; методики Л.А. Венгера; Произвольность; Эталоны; Перцептивное моделирование; Схематизация; Систематизация, нормы методики.
DOI: 10.34740/kaggle/dsv/9016408
title: Credit Risk Dataset
DOI: 10.5281/zenodo.12799660
title: Source data for: Direct retino-iridal projections and intrinsic iris contraction mediate the pupillary light reflex in early vertebrates (Communications Biology)
DOI: 10.5281/zenodo.12799661
title: Source data for: Direct retino-iridal projections and intrinsic iris contraction mediate the pupillary light reflex in early vertebrates (Communications Biology)
DOI: 10.34740/kaggle/dsv/9016422
title: Credit Risk Dataset
DOI: 10.15468/dl.gdcbs2
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-34.38526 35.35386,-20.56917 35.35386,-20.56917 40.88030,-34.38526 40.88030,-34.38526 35.35386))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dipturus intermedius (Parnell, 1837)"
 ]
}

 The dataset includes 76 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033664-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9016426
title: Credit Risk Dataset
DOI: 10.15468/dl.x8qhw6
title: Occurrence Download

description: A dataset containing 273002 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Royal Netherlands Institute for Sea Research (NIOZ) - Kom Fyke Mokbaai"
 ]
}

 The dataset includes 273002 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033659-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26355544.v1
title: Papers collected on the topic of Images and Deep Learning in Human and Urban Infrastructure Interactions

description: The files include:(1) The meanings of numbers in the fusion paper information file.(2) The extracted information of papers on data fusion after filtering.(3) The extracted information of papers without data fusion after filtering.The data and codes support the findings of our article entitled ‘Images and Deep Learning in Human and Urban Infrastructure Interactions’