cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14886952
title: Code regarding the article "Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network"

description: This is the Python code used to achieve the results published in the article "Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network", which was posted as a preprint on arXiv (https://doi.org/10.48550/arXiv.2411.17272) and is currently under review at the journal Advanced Optical Materials.

The files whose name starts with "AWGpattern_", were employed to generate the input time series used to drive the AWG and thus the optical modulator. 

The files whose name starts with "parse_data", were used to parse the raw data acquired with the oscilloscope to extract the machine learning samples.

The files whose name starts with "ML_", contains the employed machine learning pipeline for the different cases and tasks described in the article. These are supported by the code in "functions_1.py".

The files whose name starts with "ResultsAnalysis_", summarize, elaborate and plot the ML results.

Additional plots were generated using the files "Article_waveform_examples.py" and "Article_plots_and_results.py".
DOI: 10.5281/zenodo.14886953
title: Code regarding the article "Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network"

description: This is the Python code used to achieve the results published in the article "Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network", which was posted as a preprint on arXiv (https://doi.org/10.48550/arXiv.2411.17272) and is currently under review at the journal Advanced Optical Materials.

The files whose name starts with "AWGpattern_", were employed to generate the input time series used to drive the AWG and thus the optical modulator. 

The files whose name starts with "parse_data", were used to parse the raw data acquired with the oscilloscope to extract the machine learning samples.

The files whose name starts with "ML_", contains the employed machine learning pipeline for the different cases and tasks described in the article. These are supported by the code in "functions_1.py".

The files whose name starts with "ResultsAnalysis_", summarize, elaborate and plot the ML results.

Additional plots were generated using the files "Article_waveform_examples.py" and "Article_plots_and_results.py".
DOI: 10.6084/m9.figshare.5722711
title: A public data set of overground and treadmill walking kinematics and kinetics of healthy individuals

description: The data
set comprises raw and processed (both in c3d and txt formats) pelvis and lower
extremity kinematics and kinetics signals of 42 healthy volunteers (24 young
adults and 18 older adults) walking in both overground and treadmill in a range
of gait speeds. A file of metadata (xls), including anthropometrics and
information related to each file in the data set is also provided. In addition,
a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program
are also provided.

The data
were collected using a three-dimensional (3D) motion-capture system, force
platforms and an instrumented treadmill while the subjects walked barefoot over
a range of gait speeds.
DOI: 10.15468/dl.77qme7
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Glycyrrhiza inflata Batalin"
 ]
}

 The dataset includes 86 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000135-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ffwg8g
title: Occurrence Download

description: A dataset containing 124 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["3182746"],"matchCase":false},{"type":"in","key":"LICENSE","values":["CC0 1.0","CC-BY 4.0"],"matchCase":false},{"type":"equals","key":"MEDIA_TYPE","value":"StillImage","matchCase":false}]}

 The dataset includes 124 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000138-250218110819086/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.28436138
title: R Script and Data for Propagules, disturbance lower invasibility - revision

description: We manipulated soil disturbance and propagule pressure in three habitat types (aspen forest, shrub vegetation, prairie grassland) and examined their impact on the germination success of three pairs of phylogenetically similar native and non-native plant species (<i>Bromus ciliatus</i>/<i>B</i>. <i>inermis</i>, <i>Elymus trachycaulus</i>/<i>Agropyron cristatum</i>, <i>Poa secunda</i>/<i>P</i>. <i>pratensis</i>) for three months after seed addition.
DOI: 10.15468/dl.xakd83
title: Occurrence Download

description: A dataset containing 9309 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["5391845"],"matchCase":false},{"type":"in","key":"LICENSE","values":["CC0 1.0","CC-BY 4.0"],"matchCase":false},{"type":"equals","key":"MEDIA_TYPE","value":"StillImage","matchCase":false}]}

 The dataset includes 9309 records from 132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000136-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5bxgk
title: Occurrence Download

description: A dataset containing 586 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["3683063"],"matchCase":false},{"type":"in","key":"LICENSE","values":["CC0 1.0","CC-BY 4.0"],"matchCase":false},{"type":"equals","key":"MEDIA_TYPE","value":"StillImage","matchCase":false}]}

 The dataset includes 586 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000137-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/4538
title: Kurdish_NameEntityRecognition
DOI: 10.5281/zenodo.14066535
title: User Modeling in MDE - Data Sheet and Scripts

description: This replication package contains both the filled out excel template and the used scripts to filter the results at an abstract/title level for ScienceDirect and SpringerLink. For ScienceDirect: 



Perform the search on the ScienceDirect page and download the results.

ScienceDirect proposes a .bib file download. Transform this .bib file into a .ris file using any available online tools for such a conversion.

Execute the script 'sciencedirect_filter_script.py' that will ingest a ScienceDirect.ris file and produce a file called 'output_filtered_included.ris' file.


For SpringerLink:



Perform the search on SpringerLink and download the resulting .csv file. and name it SearchResults.csv.

The downloaded file does not contain the abstracts, that is why the 'springerlink_abstract_scrape.py' script that will perform a scraping to fetch the abstracts. Execute 'python springerlink_abstract_scrape.py SearchResults.py output.csv'. This will start the scraping and save the results in output.csv.

Perform a manual check if some abstracts are missing. Simply text search the .csv file for 'ABSTRACT NOT FOUND ERROR' and manually enter the missing results.

Now use the second script by executing 'python springerlink_filter_script.py' and it will generate a file called 'outputfinalkeyword.ris'


 

The data extraction excel file contains all the relevant information concerning the data extraction. This includes the reviewed papers, a summary of the numbers, the RQs and metadata.
DOI: 10.5281/zenodo.14066534
title: User Modeling in MDE - Data Sheet and Scripts

description: This replication package contains both the filled out excel template and the used scripts to filter the results at an abstract/title level for ScienceDirect and SpringerLink. For ScienceDirect: 



Perform the search on the ScienceDirect page and download the results.

ScienceDirect proposes a .bib file download. Transform this .bib file into a .ris file using any available online tools for such a conversion.

Execute the script 'sciencedirect_filter_script.py' that will ingest a ScienceDirect.ris file and produce a file called 'output_filtered_included.ris' file.


For SpringerLink:



Perform the search on SpringerLink and download the resulting .csv file. and name it SearchResults.csv.

The downloaded file does not contain the abstracts, that is why the 'springerlink_abstract_scrape.py' script that will perform a scraping to fetch the abstracts. Execute 'python springerlink_abstract_scrape.py SearchResults.py output.csv'. This will start the scraping and save the results in output.csv. Note that you will need to manually change the name of the columns ['Item Title', 'Authors', 'Publication Year', 'Publication Title', 'URL'] to ['title', 'author', 'year', 'publication', 'link']

Perform a manual check if some abstracts are missing. Simply text search the .csv file for 'ABSTRACT NOT FOUND ERROR' and manually enter the missing results.

Now use the second script by executing 'python springerlink_filter_script.py' and it will generate a file called 'outputfinalkeyword.ris'


 

The data extraction excel file contains all the relevant information concerning the data extraction. This includes the reviewed papers, a summary of the numbers, the RQs and metadata.
DOI: 10.5281/zenodo.14887422
title: User Modeling in MDE - Data Sheet and Scripts

description: This replication package contains both the filled out excel template and the used scripts to filter the results at an abstract/title level for ScienceDirect and SpringerLink. For ScienceDirect: 



Perform the search on the ScienceDirect page and download the results.

ScienceDirect proposes a .bib file download. Transform this .bib file into a .ris file using any available online tools for such a conversion.

Execute the script 'sciencedirect_filter_script.py' that will ingest a ScienceDirect.ris file and produce a file called 'output_filtered_included.ris' file.


For SpringerLink:



Perform the search on SpringerLink and download the resulting .csv file. and name it SearchResults.csv.

The downloaded file does not contain the abstracts, that is why the 'springerlink_abstract_scrape.py' script that will perform a scraping to fetch the abstracts. Execute 'python springerlink_abstract_scrape.py SearchResults.py output.csv'. This will start the scraping and save the results in output.csv. Note that you will need to manually change the name of the columns ['Item Title', 'Authors', 'Publication Year', 'Publication Title', 'URL'] to ['title', 'author', 'year', 'publication', 'link']

Perform a manual check if some abstracts are missing. Simply text search the .csv file for 'ABSTRACT NOT FOUND ERROR' and manually enter the missing results.

Now use the second script by executing 'python springerlink_filter_script.py' and it will generate a file called 'outputfinalkeyword.ris'


 

The data extraction excel file contains all the relevant information concerning the data extraction. This includes the reviewed papers, a summary of the numbers, the RQs and metadata.
DOI: 10.4121/154f01fc-9c6a-4da5-9c64-505fd195b6bf
title: Data underlying the publication: Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development

description: The present dataset is designed to be used in training and testing of AI algorithms for the detection of failures in space camera sensors. It contains simulated images generated from the camera sensor of a sample vehicle for Small Solar System Bodies exploration. The images depict various simulated faults as described in the related publication, and are accompanied by labelling masks, which can be used for both classification and segmentation tasks.The dataset is intended to support the development of AI applications that can be implemented onboard to improve the reliability of vision-based navigation systems. We encourage the research community to utilise this dataset and enhance it through further advancements.
DOI: 10.4121/154f01fc-9c6a-4da5-9c64-505fd195b6bf.v1
title: Data underlying the publication: Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development

description: The present dataset is designed to be used in training and testing of AI algorithms for the detection of failures in space camera sensors. It contains simulated images generated from the camera sensor of a sample vehicle for Small Solar System Bodies exploration. The images depict various simulated faults as described in the related publication, and are accompanied by labelling masks, which can be used for both classification and segmentation tasks.The dataset is intended to support the development of AI applications that can be implemented onboard to improve the reliability of vision-based navigation systems. We encourage the research community to utilise this dataset and enhance it through further advancements.
DOI: 10.4121/154f01fc-9c6a-4da5-9c64-505fd195b6bf.v2
title: Data underlying the publication: Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development

description: The present dataset is designed to be used in training and testing of AI algorithms for the detection of failures in space camera sensors. It contains simulated images generated from the camera sensor of a sample vehicle for Small Solar System Bodies exploration. The images depict various simulated faults as described in the related publication, and are accompanied by labelling masks, which can be used for both classification and segmentation tasks.The dataset is intended to support the development of AI applications that can be implemented onboard to improve the reliability of vision-based navigation systems. We encourage the research community to utilise this dataset and enhance it through further advancements.
DOI: 10.6084/m9.figshare.28436030
title: raisd-ai_evaluation_dataset_low_intensity_recombination_hotspot_merge_2K

description: Dataset of low intensity recombination hotspot (1000 simulations) merged with a mild bottleneck (1000 simulations) used for the evaluation of RAiSD-AI software.The commands to generate recombination hotspot MS files are:train/neutral.ms: ./msHOT/msHOT 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 1 -v 1 950 1050 224911 5763 23421train/selsweep.ms: command: ./../mbs/mbs 128 -t 0.02 -r 0.02 -s 100000 50000 -f 1 1000 ./../mbsFiles/traj -h ./../mbsFiles/rechot_47500_52500_2.txttest/neutral.ms: ./msHOT/msHOT 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 1 -v 1 950 1050 247112 3587 13815test/selsweep.ms: command: ./mbs/mbs 128 -t 0.02 -r 0.02 -s 100000 50000 -f 1 1000 ./mbsFiles/traj -h ./mbsFiles/rechot_47500_52500_2.txtThe commands to generate mild bottleneck MS files are:train/neutral.ms: ./msdir/ms 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 17316 60926 22395train/selsweep.ms: ./msseldir/mssel 128 1000 0 128 ./trajectory_files/trajectory1.txt 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 17316 60926 22395test/neutral.ms: ./msdir/ms 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 13380 19597 12002test/selsweep.ms: ./msseldir/mssel 128 1000 0 128 ./trajectory_files/trajectory1.txt 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 13380 19597 12002<br>
DOI: 10.6084/m9.figshare.28436030.v1
title: raisd-ai_evaluation_dataset_low_intensity_recombination_hotspot_merge_2K

description: Dataset of low intensity recombination hotspot (1000 simulations) merged with a mild bottleneck (1000 simulations) used for the evaluation of RAiSD-AI software.The commands to generate recombination hotspot MS files are:train/neutral.ms: ./msHOT/msHOT 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 1 -v 1 950 1050 224911 5763 23421train/selsweep.ms: command: ./../mbs/mbs 128 -t 0.02 -r 0.02 -s 100000 50000 -f 1 1000 ./../mbsFiles/traj -h ./../mbsFiles/rechot_47500_52500_2.txttest/neutral.ms: ./msHOT/msHOT 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 1 -v 1 950 1050 247112 3587 13815test/selsweep.ms: command: ./mbs/mbs 128 -t 0.02 -r 0.02 -s 100000 50000 -f 1 1000 ./mbsFiles/traj -h ./mbsFiles/rechot_47500_52500_2.txtThe commands to generate mild bottleneck MS files are:train/neutral.ms: ./msdir/ms 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 17316 60926 22395train/selsweep.ms: ./msseldir/mssel 128 1000 0 128 ./trajectory_files/trajectory1.txt 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 17316 60926 22395test/neutral.ms: ./msdir/ms 128 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 13380 19597 12002test/selsweep.ms: ./msseldir/mssel 128 1000 0 128 ./trajectory_files/trajectory1.txt 1000 -t 2000 -r 2000 2000 -eN .10000000000000000000 0.5 -eN .10040000000000000000 13380 19597 12002<br>
DOI: 10.15468/dl.gkep3s
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["3801770"],"matchCase":false},{"type":"in","key":"LICENSE","values":["CC0 1.0","CC-BY 4.0"],"matchCase":false},{"type":"equals","key":"MEDIA_TYPE","value":"StillImage","matchCase":false}]}

 The dataset includes 15 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000142-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vdc9tg
title: Occurrence Download

description: A dataset containing 462 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "TaxonKey is Merops viridis Linnaeus, 1758"
 ]
}

 The dataset includes 462 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000143-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fsymxr
title: Occurrence Download

description: A dataset containing 15874 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["9150405"],"matchCase":false},{"type":"in","key":"LICENSE","values":["CC0 1.0","CC-BY 4.0"],"matchCase":false},{"type":"equals","key":"MEDIA_TYPE","value":"StillImage","matchCase":false}]}

 The dataset includes 15874 records from 148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000140-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ddgejw
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["11668641"],"matchCase":false},{"type":"in","key":"LICENSE","values":["CC0 1.0","CC-BY 4.0"],"matchCase":false},{"type":"equals","key":"MEDIA_TYPE","value":"StillImage","matchCase":false}]}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000141-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fds8td
title: Occurrence Download

description: A dataset containing 31476 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Observation)",
 "Continent is Europe",
 "Country is Germany",
 "PublishingOrg is one of (bb646dff-a905-4403-a49b-6d378c2cf0d9, c8d737e0-2ff8-42e8-b8fc-6b805d26fc5f, 28eb1a3f-1c15-4a95-931a-4af90ecb574d)",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 31476 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000139-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sytsrb
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Altiphylax Yeriomtschenko & Szczerbak, 1984",
 "HasCoordinate is true"
 ]
}

 The dataset includes 45 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000145-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e209285
title: Replication Material for COEP The Causal Effect of Economic Freedom on Female Employment & Education

description: While there are decades of evidence that economically free economies grow faster and are more productive than less free ones, there is less knowledge about the effect of economic freedom on groups that have traditionally been disadvantaged. I study the causal effects of large and sustained jumps in economic freedom on women’s labor force participation and primary school enrollment. I find that these jumps have a positive and statistically significant effect in both cases--economic freedom is good for women’s labor force opportunities and female education.
DOI: 10.3886/e209285v2-168281
title: COEP Replication Package for "The Causal Effect of Economic Freedom on Female Employment & Education"

description: While there are decades of evidence that economically free economies grow faster and are more productive than less free ones, there is less knowledge about the effect of economic freedom on groups that have traditionally been disadvantaged. I study the causal effects of large and sustained jumps in economic freedom on women’s labor force participation and primary school enrollment. I find that these jumps have a positive and statistically significant effect in both cases--economic freedom is good for women’s labor force opportunities and female education.
DOI: 10.5281/zenodo.14886708
title: Dry Simulation Trajectories for 10.26434/chemrxiv-2024-nd20j-v2

description: Set of simulation trajectories without solvent, which were used for the data analysis in 10.26434/chemrxiv-2024-nd20j-v2
DOI: 10.5281/zenodo.14886709
title: Dry Simulation Trajectories for 10.26434/chemrxiv-2024-nd20j-v2

description: Set of simulation trajectories without solvent, which were used for the data analysis in 10.26434/chemrxiv-2024-nd20j-v2
DOI: 10.15468/dl.txkvf2
title: Occurrence Download

description: A dataset containing 9936 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Arthrobacter Conn & Dimmick, 1947"
 ]
}

 The dataset includes 9936 records from 333 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020066-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.