cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9a46g20
title: Comparison of environmental flow recommendations for the Willamette Basin Sustainable Rivers Program, water years 2008-2022

description: The U.S. Army Corps of Engineers (USACE) and the Nature Conservancy developed the Sustainable Rivers Program (SRP) as a collaborative environmental flows program to identify, refine, and implement environmental strategies at select USACE dams. The SRP was introduced to the Willamette Basin, Oregon, in 2007 through a series of environmental flow workshops, which led to stakeholder generated environmental flow recommendations in the North Santiam, South Santiam, Santiam (Bach and others, 2013), McKenzie, South Fork McKenzie (Risley and others, 2010), and Middle Fork Willamette Rivers (Gregory and others, 2007, https://www.hec.usace.army.mil/sustainablerivers/). This data release is a compilation of the stakeholder-defined environmental flow recommendations paired with flow data at the corresponding U.S. Geological Survey (USGS) streamflow gaging stations for each water-year spanning 2008 through 2022.
DOI: 10.25625/98xxvv/fzhqca
title: pompa_checked.tab

description: :unav
DOI: 10.25625/98xxvv/ew3nuv
title: humusindo_checked.tab

description: :unav
DOI: 10.25625/98xxvv/rbe5xg
title: lubuk_checked.tab

description: :unav
DOI: 10.25625/98xxvv/efkkzy
title: kabau_checked.tab

description: :unav
DOI: 10.25625/98xxvv/l8ixnq
title: unja_checked.tab

description: :unav
DOI: 10.25625/98xxvv/bw03da
title: ptpnvi_office_checked.tab

description: :unav
DOI: 10.25625/98xxvv/isvwzc
title: reki_checked.tab

description: :unav
DOI: 10.25625/98xxvv/tbrbwm
title: bungku_checked.tab

description: :unav
DOI: 10.25625/98xxvv
title: Reference_Stations_Checked

description: see metadata
DOI: 10.3886/37e8-6f90
title: Benin Power Compact - Off-Grid Energy Access: Design Report
DOI: 10.25345/c5ks6jf6b
title: MassIVE MSV000092473 - A role of TGNap1 in transporting antimicrobial proteins
title: Dataset of apoplastic proteins indentified in Col-3 and tgnap1-2 mutant upon infection with mock or Pst DC3000. Samples were harvested at 24 hpi.
DOI: 10.5281/zenodo.8146261
title: Dataset for Phonological acquisition depends on the timing of speech sound

description: Dataset for the analysis to the manuscript: Phonological acquisition depends on the timing of<br> speech sounds: Deconvolution EEG modelling across the first five years
DOI: 10.5281/zenodo.8146260
title: Dataset for Phonological acquisition depends on the timing of speech sound

description: Dataset for the analysis to the manuscript: Phonological acquisition depends on the timing of<br> speech sounds: Deconvolution EEG modelling across the first five years
DOI: 10.5519/qd.h9aivanh
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (25 records)
DOI: 10.5281/zenodo.6573117
title: Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training

description: This repository contains supplementary materials for the following journal paper: Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> https://doi.org/10.1007/s10639-022-11093-6 Preprint available at: https://arxiv.org/abs/2307.08582 <strong>How to cite</strong> If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <strong>Attached content</strong> The files included in the ZIP archive are: `All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search. `Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator. `Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator. `Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review. `Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers. `Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.
description: This research was supported by ERDF project CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence (No. CZ.02.1.01/0.0/0.0/16_019/0000822).
DOI: 10.5281/zenodo.6573116
title: Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training

description: This repository contains supplementary materials for the following journal paper: Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> https://doi.org/10.1007/s10639-022-11093-6 Preprint available at: https://arxiv.org/abs/2307.08582 <strong>How to cite</strong> If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <strong>Attached content</strong> The files included in the ZIP archive are: `All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search. `Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator. `Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator. `Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review. `Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers. `Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.
description: This research was supported by ERDF project CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence (No. CZ.02.1.01/0.0/0.0/16_019/0000822).
DOI: 10.6073/pasta/08b41ba79ef7310315d1106d893e7f48
title: Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2020 (1 Oct 2019 - 30 Sept 2020)

description: The umbrella Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from fourteen sites along the mainstem of the UCFR, and three sites representing major tributaries to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from fourteen sites along the mainstem of the UCFR and three tributaries, collected in water year 2020 (1 Oct 2019 - 30 Sept 2020).
DOI: 10.5281/zenodo.4160585
title: Dataset: Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges

description: This repository contains supplementary materials for the following journal paper: Valdemar Švábenský, Pavel Čeleda, Jan Vykopal, Silvia Brišáková.<br> <em>Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges.</em><br> In Elsevier Computers & Security. 2020.<br> https://doi.org/10.1016/j.cose.2020.102154 Preprint available at: https://arxiv.org/abs/2101.01421 <strong>How to cite</strong> If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2020cybersecurity, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel and Vykopal, Jan and Bri\v{s}\'{a}kov\'{a}, Silvia}, title = {{Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges}}, journal = {Computers \& Security}, publisher = {Elsevier}, volume = {102}, year = {2020}, issn = {0167-4048}, url = {https://www.sciencedirect.com/science/article/pii/S0167404820304272}, doi = {10.1016/j.cose.2020.102154}, }</code></pre> <strong>Attached content</strong> See the README.md file inside the attached ZIP file for more details.
description: This research was supported by ERDF project CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence (No. CZ.02.1.01/0.0/0.0/16_019/0000822).
DOI: 10.5281/zenodo.4160584
title: Dataset: Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges

description: This repository contains supplementary materials for the following journal paper: Valdemar Švábenský, Pavel Čeleda, Jan Vykopal, Silvia Brišáková.<br> <em>Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges.</em><br> In Elsevier Computers & Security. 2020.<br> https://doi.org/10.1016/j.cose.2020.102154 Preprint available at: https://arxiv.org/abs/2101.01421 <strong>How to cite</strong> If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2020cybersecurity, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel and Vykopal, Jan and Bri\v{s}\'{a}kov\'{a}, Silvia}, title = {{Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges}}, journal = {Computers \& Security}, publisher = {Elsevier}, volume = {102}, year = {2020}, issn = {0167-4048}, url = {https://www.sciencedirect.com/science/article/pii/S0167404820304272}, doi = {10.1016/j.cose.2020.102154}, }</code></pre> <strong>Attached content</strong> See the README.md file inside the attached ZIP file for more details.
description: This research was supported by ERDF project CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence (No. CZ.02.1.01/0.0/0.0/16_019/0000822).
DOI: 10.5281/zenodo.8136017
title: Dataset: Shell Commands Used by Participants of Hands-on Cybersecurity Training

description: This repository contains supplementary materials for the following journal paper: Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda.<br> <em>Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training.</em><br> In Elsevier Data in Brief. 2021.<br> https://doi.org/10.1016/j.dib.2021.107398 <strong>How to cite</strong> If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }</code></pre> The data were collected using a logging toolset referenced here. <strong>Attached content</strong> <strong>Dataset (data.zip).</strong> The collected data are attached here on Zenodo. A copy is also available in this repository. <strong>Analytical tools (toolset.zip).</strong> To analyze the data, you can instantiate the toolset or this project for ELK. <strong>Version history</strong> Version 1 (https://zenodo.org/record/5137355) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published. Version 2 (https://zenodo.org/record/5517479) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix. Version 3 (https://zenodo.org/record/6670113) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset. The current Version 4 (https://zenodo.org/record/8136017) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.
description: This research was supported by ERDF project CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence (No. CZ.02.1.01/0.0/0.0/16_019/0000822).
DOI: 10.5281/zenodo.5137354
title: Dataset: Shell Commands Used by Participants of Hands-on Cybersecurity Training

description: This repository contains supplementary materials for the following journal paper: Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda.<br> <em>Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training.</em><br> In Elsevier Data in Brief. 2021.<br> https://doi.org/10.1016/j.dib.2021.107398 <strong>How to cite</strong> If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link). <pre><code>@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }</code></pre> The data were collected using a logging toolset referenced here. <strong>Attached content</strong> <strong>Dataset (data.zip).</strong> The collected data are attached here on Zenodo. A copy is also available in this repository. <strong>Analytical tools (toolset.zip).</strong> To analyze the data, you can instantiate the toolset or this project for ELK. <strong>Version history</strong> Version 1 (https://zenodo.org/record/5137355) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published. Version 2 (https://zenodo.org/record/5517479) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix. Version 3 (https://zenodo.org/record/6670113) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset. The current Version 4 (https://zenodo.org/record/8136017) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.
description: This research was supported by ERDF project CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence (No. CZ.02.1.01/0.0/0.0/16_019/0000822).
DOI: 10.6073/pasta/054618c291fce4b5a4c55c1413cf084d
title: Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2021 (1 Oct 2020 - 30 Sept 2021)

description: The umbrella Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from thirteen sites along the mainstem of the UCFR, and three sites representing major tributaries to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from thirteen sites along the mainstem of the UCFR and three tributaries, collected in water year 2021 (1 Oct 2020 - 30 Sept 2021).
DOI: 10.34740/kaggle/dsv/6157046
title: Inverter Load Analysis Dataset
DOI: 10.5281/zenodo.8092296
title: Gradients1_3_cobalt_concentrations

description: This dataset includes concentrations of dissolved cobalt determined by cathodic stripping voltammetry (method adapted from Saito and Moffett (2001)) from Gradients 3.0 (KM1906) surface samples and concentrations of cobalt binding ligands determined by LC-ICP-MS (method adapted from Bundy et al. (2018)) from Gradients 1.0 (KOK1606) surface samples. Samples were analyzed at the Bundy lab at University of Washington. Funding was provided by Simons Foundation.
DOI: 10.5281/zenodo.8092295
title: Gradients1_3_cobalt_concentrations

description: This dataset includes concentrations of dissolved cobalt determined by cathodic stripping voltammetry (method adapted from Saito and Moffett (2001)) from Gradients 3.0 (KM1906) surface samples and concentrations of cobalt binding ligands determined by LC-ICP-MS (method adapted from Bundy et al. (2018)) from Gradients 1.0 (KOK1606) surface samples. Samples were analyzed at the Bundy lab at University of Washington. Funding was provided by Simons Foundation.
DOI: 10.5281/zenodo.8157369
title: Gradients1_3_cobalt_concentrations

description: This dataset includes concentrations of dissolved cobalt determined by cathodic stripping voltammetry (method adapted from Saito and Moffett (2001)) from Gradients 3.0 (KM1906) surface samples and concentrations of cobalt binding ligands determined by LC-ICP-MS (method adapted from Bundy et al. (2018)) from Gradients 1.0 (KOK1606) surface samples. Samples were analyzed at the Bundy lab at University of Washington. Funding was provided by Simons Foundation.
DOI: 10.5281/zenodo.8161407
title: Dataset for "A critical role for CaMKII in behavioral timescale synaptic plasticity in hippocampal CA1 pyramidal neurons"

description: Dataset for "A critical role for CaMKII in behavioral timescale synaptic plasticity in hippocampal CA1 pyramidal neurons"
DOI: 10.5281/zenodo.8161406
title: Dataset for "A critical role for CaMKII in behavioral timescale synaptic plasticity in hippocampal CA1 pyramidal neurons"

description: Dataset for "A critical role for CaMKII in behavioral timescale synaptic plasticity in hippocampal CA1 pyramidal neurons"
DOI: 10.5281/zenodo.8112517
title: Data, scripts, and results from SYNTCOMP 2023

description: This artifact contains all data downloaded from the StarExec platform concerning the jobs that ran the LTL, LTLf, and parity-game tracks of the 2023 reactive synthesis competition (SYNTCOMP23).
DOI: 10.5281/zenodo.8112518
title: Data, scripts, and results from SYNTCOMP 2023

description: This artifact contains all data downloaded from the StarExec platform concerning the jobs that ran the LTL, LTLf, and parity-game tracks of the 2023 reactive synthesis competition (SYNTCOMP23).
DOI: 10.5281/zenodo.8161423
title: Data, scripts, and results from SYNTCOMP 2023

description: This artifact contains all data downloaded from the StarExec platform concerning the jobs that ran the LTL, LTLf, and parity-game tracks of the 2023 reactive synthesis competition (SYNTCOMP23).
DOI: 10.34740/kaggle/dsv/6157077
title: Inverter Load Analysis Dataset
DOI: 10.6084/m9.figshare.23705673.v1
title: Supplemental Tables

description: Table S1 Primers used for gene expression analysis in adipose tissue from dairy cows Table S2 antibodies used in flow cytometry to characterize macrophage phenotype in adipose tissues from dairy cows
DOI: 10.6084/m9.figshare.23705673.v2
title: Supplemental Tables. <strong>Lipolysis inhibition as a treatment of clinical ketosis in dairy cows: Enhancing adipose tissue metabolic and immune functions </strong>

description: Table S1 Primers used for gene expression analysis in adipose tissue from dairy cows Table S2 antibodies used in flow cytometry to characterize macrophage phenotype in adipose tissues from dairy cows
DOI: 10.34740/kaggle/dsv/6157097
title: Balochi Language to English Translation Dataset
DOI: 10.34740/kaggle/dsv/6157053
title: English Typed Alphabets and Numbers Dataset in CSV
DOI: 10.34740/kaggle/dsv/6157110
title: Inverter Load Analysis Dataset