cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10433193
title: Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion

description: This data includes the pre-processed dataset, along with a novel workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of machine learning and ionospheric physics science, as well as being useful to other researchers for diverse objectives. 

Acknowledgements:

The WALDO database (https://waldo.world, accessed on October 1, 2023) provides VLF data. It is run collaboratively by the University of Colorado Denver and the Georgia Institute of Technology, utilizing data gathered from Stanford University and those two institutions. It has been made possible by numerous grants from the Department of Defense, NASA, and the NSF.
DOI: 10.5281/zenodo.10433194
title: Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion

description: This data includes the pre-processed dataset, along with a novel workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of machine learning and ionospheric physics science, as well as being useful to other researchers for diverse objectives. 

Acknowledgements:

The WALDO database (https://waldo.world, accessed on October 1, 2023) provides VLF data. It is run collaboratively by the University of Colorado Denver and the Georgia Institute of Technology, utilizing data gathered from Stanford University and those two institutions. It has been made possible by numerous grants from the Department of Defense, NASA, and the NSF.
DOI: 10.5281/zenodo.10433206
title: NFFA-Europe|Pilot proporsal "Production and characterization of highly controlled silicon oxide nanoparticles for solid polymer electrolytes" (PID: 444).

description: XPS, IR, and QMS data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal PID 444
description: To open '.RAR' files, you can do so using WinRAR softwar, following the steps in the link, or by using the open-source software 7zip, following the steps in the link.
DOI: 10.5281/zenodo.10433207
title: NFFA-Europe|Pilot proporsal "Production and characterization of highly controlled silicon oxide nanoparticles for solid polymer electrolytes" (PID: 444).

description: XPS, IR, and QMS data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal PID 444
description: To open '.RAR' files, you can do so using WinRAR softwar, following the steps in the link, or by using the open-source software 7zip, following the steps in the link.
DOI: 10.34740/kaggle/dsv/7285844
title: Malware Detection in Network Traffic Data
DOI: 10.5281/zenodo.7085777
title: Magnetosheath Jets MMS1 (5/2015 - 6/2020) - Classified

description: README

This dataset contains the time and the class of magnetosheath jets observed by MMS 1 during 05/2015 – 06/2020. 

More information about the different classes can be found in the articles:



https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JA027754

https://www.frontiersin.org/articles/10.3389/fspas.2020.00024/full


While an extension of the classification process has been published in:



https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JA029269


INFO 

For the attached text file. The first column is the time of maximum dynamic pressure in UTC. The second column is the class of the jet:

Main Categories:11 = Quasi-parallel jet22 = Quasi-perpendicular jet  3 = Boundary jet5 = Encapsulated jet 

Secondary Categories:1 = Possibly Quasi-parallel jet2 = Possibly Quasi-parallel jet4 =  Possibly Boundary jet6 = Possibly Encapsulated jet7 = Close to Magnetopause or Bow Shock jet0 = Unclassified jet8 = Data gap jet

Burst availability is given in the third column as:

2 = Full burst availability1 = Partial burst availability0 = No burst availability

For more information, please contact the author (savvasraptis@gmail.com)
DOI: 10.5281/zenodo.7085778
title: Magnetosheath Jets MMS1 (5/2015 - 6/2020) - Classified

description: README

This dataset contains the time and the class of magnetosheath jets observed by MMS 1 during 05/2015 – 06/2020. 

More information about the different classes can be found in the articles:



https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JA027754

https://www.frontiersin.org/articles/10.3389/fspas.2020.00024/full


While an extension of the classification process has been published in:



https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JA029269


INFO 

For the attached text file. The first column is the time of maximum dynamic pressure in UTC. The second column is the class of the jet:

Main Categories:11 = Quasi-parallel jet22 = Quasi-perpendicular jet  3 = Boundary jet5 = Encapsulated jet 

Secondary Categories:1 = Possibly Quasi-parallel jet2 = Possibly Quasi-parallel jet4 =  Possibly Boundary jet6 = Possibly Encapsulated jet7 = Close to Magnetopause or Bow Shock jet0 = Unclassified jet8 = Data gap jet

Burst availability is given in the third column as:

2 = Full burst availability1 = Partial burst availability0 = No burst availability

For more information, please contact the author (savvasraptis@gmail.com)
DOI: 10.5281/zenodo.7299382
title: Supplementary files for the dingo Python library

description: To compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library with the fastest approach for sampling on metabolic networks, a set of 7 models with a ranging dimension was used (<em>ext_data</em>). Under the <em>simpl_transf_polytopes</em> the polytopes retrieved after the simplify() and transform() functions of the PolyRound library can be found. These polytopes where used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1,000. Under the <em>dingo_samples_on_simpl_transf_polytopes</em> the resulted samples from <em>dingo</em> can be found. Similarly, under the <em>polyrounded_polytopes </em>the polytopes retrieved after both the simplify() and transform() but also the round() functions of the PolyRound library can be found. These polytopes were used as input for the hopsy library that was compared with dingo, again, asking for an ESS of 1,000. Under the <em>hopsy_samples</em> folder the resulted samples from hopsy<em> </em>library, using a thinning of 100<em>d </em>;<em> </em>only in case of Recon3D a thinning of 200<em>d </em>was used as suggested from the authors.<br>
DOI: 10.5281/zenodo.7581683
title: Supplementary files for the dingo Python library

description: We compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension (<em>ext_data.zip</em>). The <em>simpl_transf_polytopes.zip</em> contains the polytopes retrieved after the simplify() and transform() functions of the PolyRound library. These polytopes were used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1000. Under the <em>dingo_samples_on_simpl_transf_polytopes.zip</em> the resulting samples from <em>dingo</em> can be found. Similarly, <em>polyrounded_polytopes.zip contains </em>the polytopes retrieved after applying simplify(), transform() and round() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking for an ESS of 1000. The <em>hopsy_samples.zip</em> folder contains the resulting samples from hopsy<em> </em>library, using a thinning of 100<em>d </em>;<em> </em>only in the case of Recon3D a thinning of 200<em>d </em>was used as suggested by the authors. Under the <em>hopsy_samples_ess_1000.zip </em>folder, we provide the <em>hopsy</em> samples with an ESS of 1000. Further, the <em>sars_samples.zip </em>file contains <em>dingo</em> samples from the solution space of the SARS-CoV-2 integrated model of Renz et <em>al</em> (2020) for the following cases: unbiased; where the zero vector has been used as the objective function of the model after maximising for the human biomass after maximising for the virus biomass objective function (VBOF) The following Python scripts to perform these experiments are included: <em>polyround_preproces.py </em>: runs the <em>PolyRound </em>functions and builds the simplified and transformed polytopes that <em>dingo </em>will use as well as the simplified, transformed and rounded polytopes <em>hopsy</em> uses <em>hopsy_on_polyrounded_polytopes.py </em>: performs sampling with <em>hopsy </em> <em>dingo_on_simpl_transf_polytopes.py </em>: performs sampling with <em>dingo </em> <em>binary_search.py</em> : a function to return the index in the chain where ESS becomes 1000 <em>compute_ess.py: </em>based on a model's <em>hopsy</em> samples (under the <em>hopsy_samples.zip </em>folder) it retrieves the samples with an ESS of 1000 and the corresponding required time for <em>hopsy </em>to build them. The script requires the total time of the <em>hopsy </em>experiment recorded in the model's corresponding <em>.txt </em>file (you can find this under the <em>hopsy_samples.zip) </em> A notebook is available for how the integrated model was sampled.
DOI: 10.5281/zenodo.10423335
title: Supplementary files for the dingo Python library

description: We compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension (ext_data.zip).

The simpl_transf_polytopes.zip contains the polytopes retrieved after the simplify() and transform() functions of the PolyRound library. These polytopes were used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1000. Under the dingo_samples_on_simpl_transf_polytopes.zip the resulting samples from dingo can be found using the MMCS algorithm. 

Similarly, polyrounded_polytopes.zip contains the polytopes retrieved after applying simplify(), transform() and round() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking for an ESS of 1000. The hopsy_samples.zip folder contains the resulting samples from hopsy library, using a thinning of 100d ; only in the case of Recon3D a thinning of 200d was used as suggested by the authors. Under the hopsy_samples_ess_1000.zip folder, we provide the hopsy samples with an ESS of 1000. Last, the samples produced using the efficient Billiard Walk implementation of dingo can be found under the BWRsamples.zip file. In this case, 20000 points were sampled for each model.

Further, the sars_samples.zip file contains dingo samples from the solution space of the SARS-CoV-2 integrated model of Renz et al (2020) for the following cases: 



unbiased; where the zero vector has been used as the objective function of the model

after maximising for the human biomass 

after maximising for the virus biomass objective function (VBOF)


The following Python scripts to perform these experiments are included:



polyround_preproces.py : runs the PolyRound functions and builds the simplified and transformed polytopes that dingo will use as well as the simplified, transformed and rounded polytopes hopsy uses

hopsy_on_polyrounded_polytopes.py : performs sampling with hopsy 

dingo_on_simpl_transf_polytopes.py : performs sampling with dingo 

run_bwr_exp.py computes samples using the efficient Billiard Walk of dingo

binary_search.py : a function to return the index in the chain where ESS becomes 1000

compute_ess.py: based on a model's hopsy samples (under the hopsy_samples.zip folder) it retrieves the samples with an ESS of 1000 and the corresponding required time for hopsy to build them. The script requires the total time of the hopsy experiment recorded in the model's corresponding .txt file (you can find this under the hopsy_samples.zip)

compute_ess_psrf_per_phase.py computes ESS and PSRF in specific indices which correspond to those when MMCS switches from a phase a to next one


A notebook is available for how the integrated model was sampled.
DOI: 10.5281/zenodo.6213650
title: Supplementary files for the dingo Python library

description: We compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of PolyRound (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension (ext_data.zip).

The simpl_transf_polytopes.zip contains the polytopes retrieved after the simplify() and transform() functions of the PolyRound library. These polytopes were used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1000. Under the dingo_samples_on_simpl_transf_polytopes.zip the resulting samples from dingo can be found using the MMCS algorithm. 

Similarly, polyrounded_polytopes.zip contains the polytopes retrieved after applying simplify(), transform() and round() functions of the PolyRound library. These polytopes were used as input for the hopsy library, again, asking for an ESS of 1000. The hopsy_samples.zip folder contains the resulting samples from hopsy library, using a thinning of 100d ; only in the case of Recon3D a thinning of 200d was used as suggested by the authors. Under the hopsy_samples_ess_1000.zip folder, we provide the hopsy samples with an ESS of 1000. Last, the samples produced using the efficient Billiard Walk implementation of dingo can be found under the BWRsamples.zip file. In this case, 20000 points were sampled for each model.

Further, the sars_samples.zip file contains dingo samples from the solution space of the SARS-CoV-2 integrated model of Renz et al (2020) for the following cases: 



unbiased; where the zero vector has been used as the objective function of the model

after maximising for the human biomass 

after maximising for the virus biomass objective function (VBOF)


The following Python scripts to perform these experiments are included:



polyround_preproces.py : runs the PolyRound functions and builds the simplified and transformed polytopes that dingo will use as well as the simplified, transformed and rounded polytopes hopsy uses

hopsy_on_polyrounded_polytopes.py : performs sampling with hopsy 

dingo_on_simpl_transf_polytopes.py : performs sampling with dingo 

run_bwr_exp.py computes samples using the efficient Billiard Walk of dingo

binary_search.py : a function to return the index in the chain where ESS becomes 1000

compute_ess.py: based on a model's hopsy samples (under the hopsy_samples.zip folder) it retrieves the samples with an ESS of 1000 and the corresponding required time for hopsy to build them. The script requires the total time of the hopsy experiment recorded in the model's corresponding .txt file (you can find this under the hopsy_samples.zip)

compute_ess_psrf_per_phase.py computes ESS and PSRF in specific indices which correspond to those when MMCS switches from a phase a to next one


A notebook is available for how the integrated model was sampled.
DOI: 10.5281/zenodo.6213651
title: Supplementary files for the dingo Python library

description: Under the <em>simpl_transf_polytopes.zip</em> the polytopes retrieved after the simplify() and transform() functions of the PolyRound library can be found. These polytopes where used as input for the dingo implementation of the MMCS algorithm. Similarly, under the <em>simpl_transf_rounded_polytopes.zip </em>the polytopes retrieved after both the simplify() and transform() but also the round() functions of the PolyRound library can be found. These polytopes were used as input for the hopsy library that was compared with dingo, asking for the PSRF returned by dingo. Samples for each species of both dingo and hopsy, can be found under the <em>hopsy_samples</em> and the <em>dingo_samples</em> correspondingly.
DOI: 10.5281/zenodo.6224816
title: Supplementary files for the dingo Python library

description: Under the <em>simpl_transf_polytopes.zip</em> the polytopes retrieved after the simplify() and transform() functions of the PolyRound library can be found. These polytopes where used as input for the dingo implementation of the MMCS algorithm. Similarly, under the <em>simpl_transf_rounded_polytopes.zip </em>the polytopes retrieved after both the simplify() and transform() but also the round() functions of the PolyRound library can be found. These polytopes were used as input for the hopsy library that was compared with dingo, asking for the PSRF returned by dingo. Under the <em>dingo_samples</em> the resulted samples from <em>dingo</em> can be found. Under the <em>hopsy_samples_dingo_psrf</em> folder the resulted samples from <em>hopsy </em>library, using the PSRF the <em>dingo </em>returned for each model and a thinning of 8d^2, can be found. Finally, the <em>hopsy_steady_states_dingo_psrf </em>folder contains their mapped points in the initial polytope. A file called <em>mode_psrf.csv </em>points out the PSRF used for each model.
DOI: 10.5281/zenodo.6226369
title: Supplementary files for the dingo Python library

description: Under the <em>simpl_transf_polytopes.zip</em> the polytopes retrieved after the simplify() and transform() functions of the PolyRound library can be found. These polytopes where used as input for the dingo implementation of the MMCS algorithm. Similarly, under the <em>simpl_transf_rounded_polytopes.zip </em>the polytopes retrieved after both the simplify() and transform() but also the round() functions of the PolyRound library can be found. These polytopes were used as input for the hopsy library that was compared with dingo, asking for the PSRF returned by dingo. Under the <em>dingo_samples</em> the resulted samples from <em>dingo</em> can be found. Under the <em>hopsy_samples_dingo_psrf</em> folder the resulted samples from <em>hopsy </em>library, using the PSRF the <em>dingo </em>returned for each model and a thinning of 8d^2, can be found. Finally, the <em>hopsy_steady_states_dingo_psrf </em>folder contains their mapped points in the initial polytope. A file called <em>mode_psrf.csv </em>points out the PSRF used for each model.
DOI: 10.5281/zenodo.7456968
title: Supplementary files for the dingo Python library

description: To compare the Multiphase Monte Carlo flux Sampling (MMCS) feature of the dingo library against the combined method of Polyround (for rounding) followed by hopsy (for sampling) on a set of 7 models with a ranging dimension was used (<em>ext_data.zip</em>). Under the <em>simpl_transf_polytopes.zip</em> the polytopes retrieved after the simplify() and transform() functions of the PolyRound library can be found. These polytopes where used as input for the dingo implementation of the MMCS algorithm asking for an ESS of 1,000. Under the <em>dingo_samples_on_simpl_transf_polytopes.zip</em> the resulted samples from <em>dingo</em> can be found. Similarly, under the <em>polyrounded_polytopes.zip </em>the polytopes retrieved after both the simplify() and transform() but also the round() functions of the PolyRound library can be found. These polytopes were used as input for the hopsy library that was compared with dingo, again, asking for an ESS of 1000. Under the <em>hopsy_samples</em> folder the resulted samples from hopsy<em> </em>library, using a thinning of 100<em>d </em>;<em> </em>only in case of Recon3D a thinning of 200<em>d </em>was used as suggested from the authors. Further, under the <em>sars_samples.zip </em>file, <em>dingo</em> samples from the solution space of the SARS-CoV-2 integrated model of Renz et <em>al</em> (2020) can be found for the following cases: unbiased; where the zero vector has been used as the objective function of the model after maximizing for the human biomass after maximizing for the virus biomass objective function (VBOF) The Python scripts to perform these experiments are included: <em>polyround_preproces.py </em>: runs the <em>polyround </em>functions and builds the simplified and transformed polytopes that <em>dingo </em>will use as well as the simplified, transformed and rounded polytopes <em>hopsy </em>will use <em>hopsy_on_polyrounded_polytopes.py </em>: performs sampling with <em>hopsy </em> <em>dingo_on_simpl_transf_polytopes.py </em>: performs sampling with <em>dingo </em> A notebook is available for how the integrated model was sampled.
DOI: 10.5281/zenodo.10120663
title: Tomography Data for: Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating

description: This is data collected at the 3-ID Hard X-ray Nanoprobe beamline. This repository supports the following research article: 
Data provided is the aligned dataset and reconstruction using a FISTA algorithm. Angles Collected  -90 to +45 at 1 degree steps. 
Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating
By Aaron Michelson.
DOI: 10.5281/zenodo.10120664
title: Tomography Data for: Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating

description: This is data collected at the 3-ID Hard X-ray Nanoprobe beamline. This repository supports the following research article: 
Data provided is the aligned dataset and reconstruction using a FISTA algorithm. Angles Collected  -90 to +45 at 1 degree steps. 
Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating
By Aaron Michelson.
DOI: 10.5281/zenodo.10433367
title: Database on composition and characteristics of biomodified kraft and sulfite pulps using enzyme complexes based on the ascomycete fungus Penicillium sp
DOI: 10.5281/zenodo.10345653
title: Database on composition and characteristics of biomodified kraft and sulfite pulps using enzyme complexes based on the ascomycete fungus Penicillium sp
DOI: 10.5281/zenodo.10345654
title: Database on composition and characteristics of biomodified kraft and sulfite pulps using enzyme complexes based on the ascomycete fungus Penicillium sp
DOI: 10.6084/m9.figshare.22823283
title: How do different childbirth experience scales predict childbirth-related posttraumatic stress symptoms and disorder?

description: Post-traumatic stress disorder (PTSD) after childbirth causes severe and lasting effects. Screening of childbirth experience may expedite early PTSD recognition. Systematic reviews have not provided consensus on how and when to measure childbirth experience and the clinical implications of such measurements. We aimed to identify a reliable and simple scale for screening the childbirth experience with minimum risk of missing PTSD. This cohort study evaluated the childbirth experience of 1527 unselected women with Wijma Delivery Experience Questionnaire (W-DEQ-B), Delivery Satisfaction Scale (DSS), and Visual Analogue Scale (VAS). VAS was measured first &lt;1 week (VAS1) and then, together with the other scales, a few months after childbirth (VAS2). The scales’ ability to identify PTSD (measured with Traumatic Event Scale) was evaluated and compared with receiver operating characteristic (ROC) analysis. Diagnostic accuracy and clinical usefulness were used to suggest cutoff values for scales. W-DEQ-B showed highest recognition of partial or full PTSD (area under the ROC curve 0.96 in W-DEQ-B, 0.92 in VAS2, 0.91 in DSS and 0.82 in VAS1). All included scales recognized partial or full PTSD well. Although W-DEQ-B performed best, VAS (measured twice) with cutoff value of 50 mm is most suitable for screening in clinical circumstances.