cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6670198
title: Supplemental Material for a Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches

description: Bug benchmarks are used in development and evaluation of debugging approaches. Quantitative performance comparison of different debugging approaches is only possible when they have been evaluated on the same dataset or benchmark. However, benchmarks are often specialized towards usage for certain debugging approaches in their contained data, metrics, and artifacts. Such benchmarks can not be easily used on debugging approaches outside their scope as such approach may rely on specific data such as bug reports or code metrics not included in the dataset. Furthermore, benchmarks vary in their size w.r.t. the number of subject programs and the size of the individual subject programs. For these reasons, we have performed a systematic literature review where we have identified 73 benchmarks that can be used to evaluate debugging approaches. We compare the different benchmarks with respect to their size and the provided information such as bug reports, contained test cases, and other code metrics. Furthermore, we have investigated how well the benchmarks realize the FAIR guiding principles. This comparison is intended to help researchers to quickly identify all suitable benchmarks for evaluating their specific debugging approaches. More information can be found in the publication: Thomas Hirsch and Birgit Hofer: "A Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches", Journal of Systems and Software, in press, 2022.
DOI: 10.5281/zenodo.6411379
title: Supplemental Material for a Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches

description: Bug benchmarks are used in development and evaluation of debugging approaches. Quantitative performance comparison of different debugging approaches is only possible when they have been evaluated on the same dataset or benchmark. However, benchmarks are often specialized towards usage for certain debugging approaches in their contained data, metrics, and artifacts. Such benchmarks can not be easily used on debugging approaches outside their scope as such approach may rely on specific data such as bug reports or code metrics not included in the dataset. Furthermore, benchmarks vary in their size w.r.t. the number of subject programs and the size of the individual subject programs. For these reasons, we have performed a systematic literature review where we have identified 73 benchmarks that can be used to evaluate debugging approaches. We compare the different benchmarks with respect to their size and the provided information such as bug reports, contained test cases, and other code metrics. Furthermore, we have investigated how well the benchmarks realize the FAIR guiding principles. This comparison is intended to help researchers to quickly identify all suitable benchmarks for evaluating their specific debugging approaches. More information can be found in the publication: Thomas Hirsch and Birgit Hofer: "A Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches", Journal of Systems and Software, in press, 2022.
DOI: 10.5519/qd.nsanmpuv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (38 records)
DOI: 10.6084/m9.figshare.19455572
title: Review Protocol_Prescriptive Process Monitoring: Quo Vadis?

description: The review protocol and the file with retrieved studies for the paper Prescriptive Process Monitoring: Quo Vadis?
DOI: 10.6084/m9.figshare.19455572.v1
title: Review Protocol_Prescriptive Process Monitoring: Quo Vadis?

description: The review protocol and the file with retrieved studies for the paper Prescriptive Process Monitoring: Quo Vadis?
DOI: 10.5281/zenodo.6670165
title: MERRIN: MEtabolic Regulation Rule INference from time series data (Docker image and notebooks)

description: This record contains notebooks and Docker image for reproducing the results of the paper "MERRIN: MEtabolic Regulation Rule INference from time series data" published as part of the ECCB 2022 conference. Notebooks can be executed interactively within the Docker image <code>bioasp/merrin:v1</code> which extends the CoLoMoTo Docker version <code>2021-02-01.</code> Also see https://github.com/bioasp/merrin-covert The Docker image can be executed as follows: <pre><code class="language-bash">docker pull bioasp/merrin:v1 docker run -it --rm -p 8888:8888 bioasp/merrin:v1 </code></pre> then point your browser to http://127.0.0.1:8888. The image can be imported using the command <code>docker load</code> with the image file provided in this record: <pre><code>docker load -i image.tar.gz</code></pre> or with the <code>donodo</code> command available at https://github.com/pauleve/donodo: <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.6670165</code></pre>
DOI: 10.5281/zenodo.6670164
title: MERRIN: MEtabolic Regulation Rule INference from time series data (Docker image and notebooks)

description: This record contains notebooks and Docker image for reproducing the results of the paper "MERRIN: MEtabolic Regulation Rule INference from time series data" published as part of the ECCB 2022 conference. Notebooks can be executed interactively within the Docker image <code>bioasp/merrin:v1</code> which extends the CoLoMoTo Docker version <code>2021-02-01.</code> Also see https://github.com/bioasp/merrin-covert The Docker image can be executed as follows: <pre><code class="language-bash">docker pull bioasp/merrin:v1 docker run -it --rm -p 8888:8888 bioasp/merrin:v1 </code></pre> then point your browser to http://127.0.0.1:8888. The image can be imported using the command <code>docker load</code> with the image file provided in this record: <pre><code>docker load -i image.tar.gz</code></pre> or with the <code>donodo</code> command available at https://github.com/pauleve/donodo: <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.6670165</code></pre>
DOI: 10.5281/zenodo.6670236
title: Myrmophylax atrothorax SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6670235
title: Myrmophylax atrothorax SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6660137
title: Dissertation Björn Klaes

description: Electronic appendix - Additional data of stalagmite MA1 and lacustrine cores STU1/STU3 (unpublished data)
DOI: 10.5281/zenodo.6660136
title: Dissertation Björn Klaes

description: Electronic appendix - Additional data of stalagmite MA1 and lacustrine cores STU1/STU3 (unpublished data)
DOI: 10.5281/zenodo.5903899
title: A study of techniques to management and control of lianas in Neotropical forests

description: The present dataset presents an update to data published in 2020 (http://dx.doi.org/DOI:10.5281/zenodo.4050477) with additional description related to the effect of lianas on tree and forest structure and diversity, and to contribute to improve decision making on forest restoration and management.
DOI: 10.5281/zenodo.6670260
title: Bathymetry 2015 - 1m - HBC Project

description: Digital Bathymetry data set., cell size 1 m×1m.
description: {"references": ["Di Stefano M, Mayer LA. An Automatic Procedure for the Quantitative Characterization of Submarine Bedforms. Geosciences. 2018; 8(1):28. https://doi.org/10.3390/geosciences8010028"]}
DOI: 10.5281/zenodo.5665376
title: Dataset to "Surface Properties of the Hydrogen–Titanium System"

description: Data of " Surface Properties of the Hydrogen−Titanium System" published in the Journal of Physical Chemistry C, 2021 DOI: 10.1021/acs.jpcc.1c08635
DOI: 10.5281/zenodo.5665375
title: Dataset to "Surface Properties of the Hydrogen–Titanium System"

description: Data of " Surface Properties of the Hydrogen−Titanium System" published in the Journal of Physical Chemistry C, 2021 DOI: 10.1021/acs.jpcc.1c08635
DOI: 10.34740/kaggle/dsv/3835373
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6655889
title: Association Rules and Semantic Relatedness (ARSR) - Evaluation Data

description: <em><strong>ARSR</strong></em> stands for "<strong><em>Association Rules and Semantic Relatedness</em></strong>",<br> a recommender system that combines association rule mining and semantic relatedness to create recommendations for form fields. ARSR was evaluated with focus on recommending values for fields of metadata forms.<br> The evaluation was performed with two sets of association rules (R1 and R2).<br> <strong>R1</strong> was primarily used to assess the recommendation performance.<br> <strong>R2</strong> served as an alternative and led to similar results. This dataset contains the raw data ("<strong>collected</strong>", JSON format), generated during the evaluation.<br> It includes i.a. input (populated fields and target field), expected output, and the top 40 generated recommendations for each test combination. In addition, the processed data ("<strong>analysed</strong>", CSV format) is provided.<br> It is based on the raw data and is used to calculate metrics and plot the results. The <strong>source code</strong> for ARSR and the evaluation is available on GitLab (gitlab.com). <em>Note:</em><br> To perform the analysis on the raw data (collected) yourself, make sure to follow the setup instruction in the evaluation repository first.<br> More specifically, install dependencies and unzip "data/cedar/test-instances.zip" so that URI mappings can be accessed.<br> Then follow the instructions provided by the README file in the raw data archives (e.g. collected-R1.zip).
DOI: 10.5281/zenodo.6655888
title: Association Rules and Semantic Relatedness (ARSR) - Evaluation Data

description: <em><strong>ARSR</strong></em> stands for "<strong><em>Association Rules and Semantic Relatedness</em></strong>",<br> a recommender system that combines association rule mining and semantic relatedness to create recommendations for form fields. ARSR was evaluated with focus on recommending values for fields of metadata forms.<br> The evaluation was performed with two sets of association rules (R1 and R2).<br> <strong>R1</strong> was primarily used to assess the recommendation performance.<br> <strong>R2</strong> served as an alternative and led to similar results. This dataset contains the raw data ("<strong>collected</strong>", JSON format), generated during the evaluation.<br> It includes i.a. input (populated fields and target field), expected output, and the top 40 generated recommendations for each test combination. In addition, the processed data ("<strong>analysed</strong>", CSV format) is provided.<br> It is based on the raw data and is used to calculate metrics and plot the results. The <strong>source code</strong> for ARSR and the evaluation is available on GitLab (gitlab.com). <em>Note:</em><br> To perform the analysis on the raw data (collected) yourself, make sure to follow the setup instruction in the evaluation repository first.<br> More specifically, install dependencies and unzip "data/cedar/test-instances.zip" so that URI mappings can be accessed.<br> Then follow the instructions provided by the README file in the raw data archives (e.g. collected-R1.zip).
DOI: 10.5281/zenodo.3822094
title: CellCognize: a neural network pipeline for cell type classification from flow cytometry data

description: Readme file content The files stored here contain the following material as supplementary and source data for the publication Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data Birge D. Özel Duygan1, Noushin Hadadi1, Ambrin Farizah Bab1, Markus Seyfried2, Jan R. van der Meer1 1 Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland<br> 2 Biotechnology Department, Firmenich SA, Geneva, Switzerland %%%%%%%<br> Flow cytometry data<br> %%%%%% FCM_files: .mat files with cleaned data as described in the supplementary methods section Ecoli_lakewater: raw FCM data (in .csv format) of E. coli cultures and E. coli cultures mixed to lakewater MIX_experiment_ACL_AJH_PVR: raw FCM data (in .csv format) of the synthetic three culture experiment with E. coli, A. johnsonii and P. veronii, as described in the main text and SI methods. PHE_OCT_enrichments: raw FCM data (in .csv format) of the phenol and 1-octanol enrichments and the 1-octanol isolates, as described in the main text and SI methods. %%%%%%%<br> Neural network data<br> %%%%%% NN_file_example: three ANN functions, to be used in conjunction with the SI methods section Supplementary_Methods.docx: Detailed description on the construction, usage and scripts for the ANN. To be used in conjunction with the Flow Cytometry data %%%%%%%<br> 16S sequencing data<br> %%%%%% raw fastq- files of the sample reads of the 1-octanol and phenol enrichments described in the paper, at t=0 and t=3d, each in triplicates, forward and reverse. Readme_16S_sequence_files.txt: sample description of the read files
description: Commercial use of the CellCognize pipeline is restricted under patent application EP-Patent Application 20 18 1896.0. For non-disclosure agreements, please contact Dr. Birge Özel: birgeozel@gmail.com
DOI: 10.5281/zenodo.3822093
title: CellCognize: a neural network pipeline for cell type classification from flow cytometry data

description: Readme file content The files stored here contain the following material as supplementary and source data for the publication Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data Birge D. Özel Duygan1, Noushin Hadadi1, Ambrin Farizah Bab1, Markus Seyfried2, Jan R. van der Meer1 1 Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland<br> 2 Biotechnology Department, Firmenich SA, Geneva, Switzerland %%%%%%%<br> Flow cytometry data<br> %%%%%% FCM_files: .mat files with cleaned data as described in the supplementary methods section Ecoli_lakewater: raw FCM data (in .csv format) of E. coli cultures and E. coli cultures mixed to lakewater MIX_experiment_ACL_AJH_PVR: raw FCM data (in .csv format) of the synthetic three culture experiment with E. coli, A. johnsonii and P. veronii, as described in the main text and SI methods. PHE_OCT_enrichments: raw FCM data (in .csv format) of the phenol and 1-octanol enrichments and the 1-octanol isolates, as described in the main text and SI methods. %%%%%%%<br> Neural network data<br> %%%%%% NN_file_example: three ANN functions, to be used in conjunction with the SI methods section Supplementary_Methods.docx: Detailed description on the construction, usage and scripts for the ANN. To be used in conjunction with the Flow Cytometry data %%%%%%%<br> 16S sequencing data<br> %%%%%% raw fastq- files of the sample reads of the 1-octanol and phenol enrichments described in the paper, at t=0 and t=3d, each in triplicates, forward and reverse. Readme_16S_sequence_files.txt: sample description of the read files
description: Commercial use of the CellCognize pipeline is restricted under patent application EP-Patent Application 20 18 1896.0. For non-disclosure agreements, please contact Dr. Birge Özel: birgeozel@gmail.com
DOI: 10.5281/zenodo.6670341
title: Xyliphius kryptos decontaminated gx

description: .
DOI: 10.5281/zenodo.6670340
title: Xyliphius kryptos decontaminated gx

description: .
DOI: 10.34740/kaggle/dsv/3835455
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.6670357
title: Adela reaumurella decontaminated gx

description: .
DOI: 10.5281/zenodo.6670356
title: Adela reaumurella decontaminated gx

description: .
DOI: 10.5517/ccdc.csd.cc2855wj
title: CCDC 2090357: Experimental Crystal Structure Determination

description: Related Article: Xiangtai Meng, Hehua Xu, Rui Liu, Yu Zheng, Shenlin Huang|2022|Green Chemistry|24|4754|doi:10.1039/D2GC01129H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc298bgd
title: CCDC 2123173: Experimental Crystal Structure Determination

description: Related Article: Xiangtai Meng, Hehua Xu, Rui Liu, Yu Zheng, Shenlin Huang|2022|Green Chemistry|24|4754|doi:10.1039/D2GC01129H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bj7mr
title: CCDC 2160564: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Gang Zhao, Jean'ne M. Shreeve|2022|Mat.Advs.|3|5012|doi:10.1039/D2MA00406B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bj7ns
title: CCDC 2160565: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Gang Zhao, Jean'ne M. Shreeve|2022|Mat.Advs.|3|5012|doi:10.1039/D2MA00406B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bj7pt
title: CCDC 2160566: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Gang Zhao, Jean'ne M. Shreeve|2022|Mat.Advs.|3|5012|doi:10.1039/D2MA00406B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bj7qv
title: CCDC 2160567: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Gang Zhao, Jean'ne M. Shreeve|2022|Mat.Advs.|3|5012|doi:10.1039/D2MA00406B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.6670384
title: Suomen sodissa 1939-1945 menehtyneet

description: Suomen sodissa 1939-1945 menehtyneiden tietokanta sisältää tietoja lähes 95 000 viime sotien aikana puolustusvoimien palveluksessa menehtyneestä . suomalaisista ja Suomessa palvelleista ulkomaalaisista. Tietokanta ei lähtökohtaisesti sisällä tietoja niistä sotavainajista, jotka eivät olleet puolustusvoimien palveluksessa. Näitä henkilöryhmiä ovat mm. partisaanien uhrit ja kotiseudun ilmapommituksissa menehtyneet siviilit. Denna databas innehåller information om nästan 95 000 personer - finländare och utlänningar - som omkom i försvarsmaktens tjänst i finska krigen 1939-1945. Databasen innehåller egentligen inte information om de krigsoffren, som inte var i militärtjänst, såsom offren för partisaner och civila, som avled i luftbombningar på sin hemort.
description: Menehtyneiden tietokannan tiedot löytyvät myös Sotasammosta. Data on lisäksi haettavissa rajapinnan kautta osoitteesta https://www.ldf.fi/dataset/warsa
DOI: 10.5281/zenodo.6670383
title: Suomen sodissa 1939-1945 menehtyneet

description: Suomen sodissa 1939-1945 menehtyneiden tietokanta sisältää tietoja lähes 95 000 viime sotien aikana puolustusvoimien palveluksessa menehtyneestä . suomalaisista ja Suomessa palvelleista ulkomaalaisista. Tietokanta ei lähtökohtaisesti sisällä tietoja niistä sotavainajista, jotka eivät olleet puolustusvoimien palveluksessa. Näitä henkilöryhmiä ovat mm. partisaanien uhrit ja kotiseudun ilmapommituksissa menehtyneet siviilit. Denna databas innehåller information om nästan 95 000 personer - finländare och utlänningar - som omkom i försvarsmaktens tjänst i finska krigen 1939-1945. Databasen innehåller egentligen inte information om de krigsoffren, som inte var i militärtjänst, såsom offren för partisaner och civila, som avled i luftbombningar på sin hemort.
description: Menehtyneiden tietokannan tiedot löytyvät myös Sotasammosta. Data on lisäksi haettavissa rajapinnan kautta osoitteesta https://www.ldf.fi/dataset/warsa
DOI: 10.5281/zenodo.6670419
title: Bolitophila cinerea decontaminated gx

description: .
DOI: 10.5281/zenodo.6670418
title: Bolitophila cinerea decontaminated gx

description: .
DOI: 10.5281/zenodo.6456256
title: Dataset for "Impact of C-S-H seeding on hydration and strength of slag blended cement"

description: The data come along with the publication "Impact of C-S-H seeding on hydration and strength of slag blended cement" submitted to Cement and Concrete Research. 2022 The file 00_mixdesign.csv contains the indexing and the labels, the same as Table 5 from the manuscript. Units of the values are not always specified in the dataset, but can be found in the manuscript. To understand the structure and labels of the dataset, please refer to the paper. April. 2022<br> Xuerun Li, Julien Bizzozero and Christoph Hesse<br> BASF Construction Additives GmbH, Dr-Albert-Frank-Strasse 32, 83308 Trostberg, Germany
DOI: 10.5281/zenodo.6456255
title: Dataset for "Impact of C-S-H seeding on hydration and strength of slag blended cement"

description: The data come along with the publication "Impact of C-S-H seeding on hydration and strength of slag blended cement" submitted to Cement and Concrete Research. 2022 The file 00_mixdesign.csv contains the indexing and the labels, the same as Table 5 from the manuscript. Units of the values are not always specified in the dataset, but can be found in the manuscript. To understand the structure and labels of the dataset, please refer to the paper. April. 2022<br> Xuerun Li, Julien Bizzozero and Christoph Hesse<br> BASF Construction Additives GmbH, Dr-Albert-Frank-Strasse 32, 83308 Trostberg, Germany
DOI: 10.5281/zenodo.6670432
title: Calvadosia cruxmelitensis decontaminated gx

description: .
DOI: 10.5281/zenodo.6670431
title: Calvadosia cruxmelitensis decontaminated gx

description: .
DOI: 10.6073/pasta/25d3f0db55e9df6f99fc3e9596433090
title: Relative percent cover of plant species for years 2013 2014 2016 2017 in LTER dry heath tundra experimental plots established in 1989, Arctic LTER Toolik, Field Station Alaska

description: Relative percent cover of plant species was measured in Arctic Long-Term Ecological Research (ARC-LTER) Dry Heath experimental plots. Treatments include Nitrogen Phosphorus (NP), and Control (CT), Nitrogen Phosphorus Unfenced (NFNP), Nitrogen Phosphorus Small Fenced (SFNP), Nitrogen Phosphorus Large Fenced (LFNP), Control (CT), Control Small Fenced (CTSF), and Control Large Fenced (LFCT).