cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3923649
title: Codes and datasets associated with the paper "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows"

description: Here, you will find some of the codes and datasets utilized in the article: "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows". (https://arxiv.org/abs/2001.02255) The direct numerical simulations were performed using the HERCULES code (https://github.com/friedenhe/HERCULES). For all the simulations, the bulk Reynolds number is kept at 20,000. The bulk Richardson number is varied from 0.1 to 0.5. The zip files Re20000RiXX.zip contains the various turbulence statistics. Re20000Ri10: Ri_b = 0.1 Re20000Ri20: Ri_b = 0.2 Re20000Ri30: Ri_b = 0.3 Re20000Ri40: Ri_b = 0.4 Re20000Ri50: Ri_b = 0.5 Every dat file (within the zip archives) is in ascii format and has 10 rows and 286 columns. Each row corresponds to normalized time (ranging from 1 to 10). Each column represents vertical model levels.
DOI: 10.5281/zenodo.3923648
title: Codes and datasets associated with the paper "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows"

description: Here, you will find some of the codes and datasets utilized in the article: "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows". (https://arxiv.org/abs/2001.02255) The direct numerical simulations were performed using the HERCULES code (https://github.com/friedenhe/HERCULES). For all the simulations, the bulk Reynolds number is kept at 20,000. The bulk Richardson number is varied from 0.1 to 0.5. The zip files Re20000RiXX.zip contains the various turbulence statistics. Re20000Ri10: Ri_b = 0.1 Re20000Ri20: Ri_b = 0.2 Re20000Ri30: Ri_b = 0.3 Re20000Ri40: Ri_b = 0.4 Re20000Ri50: Ri_b = 0.5 Every dat file (within the zip archives) is in ascii format and has 10 rows and 286 columns. Each row corresponds to normalized time (ranging from 1 to 10). Each column represents vertical model levels.
DOI: 10.34644/laudatio-qynxbxmb7carcq9cp7by
title: Deutsch Diachron Digital - Referenzkorpus Altdeutsch Version 1.0
DOI: 10.7910/dvn/6r1kca/wmk7io
title: Appendix 1b STaRT-RWE template appendices.tab

description: Appendix 1b
DOI: 10.7910/dvn/6r1kca/hzbre6
title: Appendix 1a STaRT template tables and figure.docx

description: Appendix 1a (word)
DOI: 10.7910/dvn/6r1kca/pwdzvb
title: STaRT-RWE Example 1a Comparative Effectiveness Tables and Figure.tab

description: STaRT-RWE Example 1a
DOI: 10.7910/dvn/6r1kca/zzhdmy
title: STaRT-RWE Example 4 SCRI Danish Data.tab

description: STaRT-RWE Example 4
DOI: 10.7910/dvn/6r1kca/5c7zir
title: Appendix 1a STaRT-RWE template tables and figure.tab

description: Appendix 1a (xlsx)
DOI: 10.7910/dvn/6r1kca/ogpde8
title: STaRT-RWE Example 1b Comparative Effectiveness Appendices.tab

description: STaRT-RWE Example 1b
DOI: 10.7910/dvn/6r1kca/amqtnb
title: Appendix 2 Summary of focus group.docx

description: Appendix 2
DOI: 10.7910/dvn/6r1kca/yap4ey
title: STaRT-RWE Example 3 Safety in Pregnancy.xlsx

description: STaRT-RWE Example 3
DOI: 10.7910/dvn/6r1kca/pb2qtk
title: STaRT-RWE manuscript_2020_06-25.docx

description: Manuscript
DOI: 10.5281/zenodo.583422
title: Dataset: Analysis of timing variability in human movements by aligning parameter curves in time

description: Supplementary Data for <em><strong>Analysis of timing variability in human movements by aligning parameter curves in time</strong></em> article Dataset associated with the following publication:<br> Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time. ------------------------------------------------------------------------------- The data files are structured in the following way:<br> (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file). (2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.
DOI: 10.5281/zenodo.601473
title: Dataset: Analysis of timing variability in human movements by aligning parameter curves in time

description: Supplementary Data for <em><strong>Analysis of timing variability in human movements by aligning parameter curves in time</strong></em> article Dataset associated with the following publication:<br> Maurer, L. K., Maurer, H., & Müller, H. (2017). Analysis of timing variability in human movements by aligning parameter curves in time. ------------------------------------------------------------------------------- The data files are structured in the following way:<br> (1) Basic subject information (age, sex) can be found in the file subject_data.txt (tabulator separated text file). (2) The folder parameter_curves contains the angle trajectories of all trials structured in blocks of 50 trials (sometimes less than 50 because of data cleaning procedures deleating corrupted data and trials in which participants released accidentally [with zero velocity]). Each participant performed five practice days with four blocks of 50 trials, i.e. 20 blocks. File names contain subject (1,...,14), day (1,...,5), and block (1,...,4) information. Within the tabulator separated text files each column contains the angle trajectory of one trial consisting of 1000 values (sampled with 1000 Hz). Index 600 is the moment when participants released the virtual ball.
DOI: 10.21227/0288-5561
title: Vibration measurement on SAG mill drive motor

description: Vibration measurement on SAG mill drive motor for Energy harvesting or predictive maintenance
DOI: 10.21227/y8xc-cn93
title: Vibration measurement on SAG mill drive motor

description: Vibration measurement on SAG mill drive motor for Energy harvesting or predictive maintenance
DOI: 10.5281/zenodo.3923917
title: LeiKo

description: LeiKo is a comparable corpus of German easy-to-read news texts. This freely available resource is systematically compiled and linguistically annotated for linguistic and computational linguistic research. LeiKo consists of 216 news and newspaper texts (approx. 56,600 tokens) and their meta data structured in four subcorpora according to the websites they were published on. All texts are tokenized, lemmatized, part-of-speech tagged and dependency parsed and can be queried in ANNIS (Krause/Zeldes 2016). A core corpus of 40 texts is manually corrected.<br> <br> Version 0.9 contains only the core corpus with lemma and pos annotations and can be queried here: https://corpora.uni-hamburg.de/hzsk/de/hzsk_access/annis/leiko<br> <br> Version 1.0 comprises all 216 texts and not only lemma and pos annotations, but also syntactic annotations and metadata. The corpus is provided in the annis format, which can be directly imported into ANNIS Kickstarter. Version 1.1 is identical to version 1.0, but in addition to the annis files contains all corpus texts in the conll format. In version 1.3, some parts in the tazleicht subcorpus were added that were missing due to a mistake in the Python script written to gather the texts from the websites. In addition, the syntactic segmentation for lists was revised and the URL for each text was added to the set of meta data.<br> <br> Further versions with additional manual annotation levels will follow. If you use the corpus, please cite: Jablotschkin, Sarah / Heike Zinsmeister (2020): „LeiKo: A corpus of easy-to-read German“. Poster presentation at the Computational Linguistics Poster Session in the course of the 42nd annual conference of the Deutsche Gesellschaft für Sprachwissenschaft (DGfS) in Hamburg. (The version of the poster you find in the download section was corrected for typos.)
description: If you use the corpus, please cite: Jablotschkin, Sarah / Heike Zinsmeister (2020): "LeiKo: A corpus of easy-to-read German". Poster presentation at the Computational Linguistics Poster Session in the course of the 42nd annual conference of the Deutsche Gesellschaft für Sprachwissenschaft (DGfS) in Hamburg. (The version of the poster you find in the download section was corrected for typos.)
DOI: 10.5281/zenodo.3923935
title: When female leaders believe that men make better leaders: Empowerment in community-based water management in rural Namibia

description: The code in this repository can be used to reproduce the figures and regression analysis in Stata (some packages need to be installed via SSC) but the data file can of course be used with other programs like R or SPSS. "female_leaders.do" includes the Stata code "female_leaders.dta" includes the data from the behavioral task and surveys data in Stata format (.dta) & "female_leaders.xlsx" includes these data in Excel format (.xlsx)
DOI: 10.5281/zenodo.3923866
title: Bias Away - Background sequence repositories

description: This repository contains all Background Sequences available for the following modules of our BiasAway web interface: Mononucleotide distribution matched (subcommand g in the command-line tool). Mononucleotide distribution within a sliding window (subcommand c in the command-line tool). For every organism, we provide background sequences with the following lengths: 100bp, 250bp, 500bp, 750bp and 1000bp. The genome assemblies used were: Homo sapiens: hg38. Mus musculus: mm10. Rattus norvegicus: Rnor_6.0. Arabidopsis thaliana: TAIR10. Danio rerio: GRCz11. Drosophila melanogaster: dm6. Caenorhabditis elegans: WBcel235. Saccharomyces cerevisiae: R64-1-1. Schizosaccharomyces pombe: ASM294v2. The code and necessary documentation to generate them can be found at our BitBucket repository. The documentation for the BiasAway tool can be found at readthedocs.
DOI: 10.5281/zenodo.3923865
title: Bias Away - Background sequence repositories

description: This repository contains all Background Sequences available for the following modules of our BiasAway web interface: Mononucleotide distribution matched (subcommand g in the command-line tool). Mononucleotide distribution within a sliding window (subcommand c in the command-line tool). For every organism, we provide background sequences with the following lengths: 100bp, 250bp, 500bp, 750bp and 1000bp. The genome assemblies used were: Homo sapiens: hg38. Mus musculus: mm10. Rattus norvegicus: Rnor_6.0. Arabidopsis thaliana: TAIR10. Danio rerio: GRCz11. Drosophila melanogaster: dm6. Caenorhabditis elegans: WBcel235. Saccharomyces cerevisiae: R64-1-1. Schizosaccharomyces pombe: ASM294v2. The code and necessary documentation to generate them can be found at our BitBucket repository. The documentation for the BiasAway tool can be found at readthedocs.
DOI: 10.5281/zenodo.3923969
title: Dataset: Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning

description: Supplementary Data for <em><strong>Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning</strong></em><em><strong> </strong></em>article Dataset associated with the following publication: Maurer, L. K., Joch, M., Hegele, M., Maurer, H. & Müller, H. (2019). Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning. <em>Journal of Vision</em>, <em>19(4):18</em>, 1-13. https://doi.org/10.1167/19.4.18
DOI: 10.5281/zenodo.3923968
title: Dataset: Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning

description: Supplementary Data for <em><strong>Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning</strong></em><em><strong> </strong></em>article Dataset associated with the following publication: Maurer, L. K., Joch, M., Hegele, M., Maurer, H. & Müller, H. (2019). Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning. <em>Journal of Vision</em>, <em>19(4):18</em>, 1-13. https://doi.org/10.1167/19.4.18
DOI: 10.34644/laudatio-xsmdbxmb7carcq9ct4s5
title: RIDGES Herbology Version 7.0
DOI: 10.25387/g3.12472121
title: Supplemental Material for Peñaloza et al., 2020

description: Figure S1 - Distribution of SNPs of the 65K SNP array on the Nile tilapia genome. <br>Figure S2 - Pairwise scatter plots of the first six principal components from the PCA of the genome-wide SNP data obtained from the 65K SNP array. <br>Figure 3 - Fraction of the variance accounted for by the first 40 PCs.Table S1 - Details of the seven previously identified sex-associated markers included on the 65K Nile tilapia SNP array.File S1 - Genome position and probes for all SNPs included on the 65K SNP array. <br>
DOI: 10.25387/g3.12472121.v2
title: Supplemental Material for Peñaloza et al., 2020

description: Figure S1 - Distribution of SNPs of the 65K SNP array on the Nile tilapia genome. <br>Figure S2 - Pairwise scatter plots of the first six principal components from the PCA of the genome-wide SNP data obtained from the 65K SNP array. <br>Figure 3 - Fraction of the variance accounted for by the first 40 PCs.Table S1 - Details of the seven previously identified sex-associated markers included on the 65K Nile tilapia SNP array.File S1 - Genome position and probes for all SNPs included on the 65K SNP array. <br>
DOI: 10.25387/g3.12472121.v1
title: Supplemental Material for Peñaloza et al., 2020

description: Two files have been uploaded, one that corresponds to the supplementary figures/tables that are part of a manuscript that has already been accepted for publication. The other file is an excel file (File_S1.CSV) containing genetic data associated to the same manuscript.
DOI: 10.26302/sshade/experiment_dt_20170222_002
title: Y K edge XAS transmission of synthetic yttrium oxide Y2O3 at ambient conditions
DOI: 10.5281/zenodo.3923916
title: Four-area test network

description: Test network consisting of four areas used in reliability studies at SINTEF Energy Research, see README.md for details.
DOI: 10.5281/zenodo.3491915
title: Four-area test network

description: Test network consisting of four areas used in reliability studies at SINTEF Energy Research, see README.md for details.
DOI: 10.34644/laudatio-0coabxmb7carcq9cecqj
title: RIDGES Herbology Version 8.0
DOI: 10.7910/dvn/kwsryo/truvja
title: Taubman Poll Codebook Fall 2019.docx

description: :unav
DOI: 10.7910/dvn/kwsryo/zzvapf
title: taubman_poll_F2019.tab

description: :unav
DOI: 10.5281/zenodo.3924097
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Benjamin Price, https://orcid.org/0000-0001-5497-4087. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.34644/laudatio-mcnubxmb7carcq9cnr_n
title: Deutsch Diachron Digital - Referenzkorpus Altdeutsch Version 1.1
DOI: 10.5281/zenodo.3368425
title: Dataset S2 - NGS-based S. aureus typing and outbreak analysis in clinical microbiology laboratories: lessons learned from a Swiss-wide proficiency test

description: Supplemental material of article "NGS-based S. aureus typing and outbreak analysis in clinical microbiology laboratories: lessons learned from a Swiss-wide proficiency test" by D. Dylus et al. Please refer to Methods therein. Assemblies and SNP calls for increment 3.
DOI: 10.5281/zenodo.3923070
title: Life Sciences dataset used in INFORE project, part 2

description: Life Sciences dataset used in INFORE project, part 2 The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction. The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different examples of parameters combinations of our use case that correspond to the different panels of Figure 4 in Documentation folder. This figure comes from the paper in the same folder.<br> You can find a broad discussion of our use case in the Biological Use Case Documentation file. Also,<br> The results of the cell simulations can be found in example_XXX/run0/outputs. The results of the microenvironment simulations can be found in example_XXX/run0/microutputs.<br> Details on how these files are built can be found in Biological Use Case output format file (which is a snippet of the broad documentation file that I detached for your convenience). Briefly: each time step defined, the software writes an output and microutput file. For instance, ecm_t00030.txt correspond to time step 30. Each line of these files corresponds to a cell or microenvironment entity (oxygen, TNF, etc). Columns are defined by the first row for output folder. For the microutputs, the first three columns correspond to spatial coordinates and the fourth to the value of the density. The examples are:<br> - example_spheroid_TNF_nopulse: corresponds to Figure 4 A.<br> - example_spheroid_TNF_onepulse: corresponds to Figure 4 C.<br> - example_spheroid_TNF_pulse150: corresponds to Figure 4 D left. This is the simulation outcome desired: proliferative cells die out with increasing number of pulses of TNF.<br> - example_spheroid_TNF_pulse600: corresponds to Figure 4 D right.<br> - example_spheroid_TNF_pulsecont: corresponds to Figure 4 B.<br> - example_cells_with_ECM_mutants: does NOT correspond to Figure 4. This is an example in which microutput folder is full of two entities: oxygen and ECM. Also, in this example you can find a folder (ECM_mut) with the kind of visualisation that we perform to showcase results.<br> - example_spheroid_TNF_pulsecont_oxy: 21 simulations with slightly different oxygen tolerance conditions using as a base the simulation with one continuous pulse (Figure 4 B from the presentation).<br> The only difference among parameters file is the "oxygen_necrotic" value, which controls the threshold above which cells commit to necrosis due to lack of oxygen. In the original simulation this value was zero and the maximum available oxygen is 40 fg/µm^3. Here, we have studied the parameter value from 0 to 40 in steps of 5.
DOI: 10.5281/zenodo.3923069
title: Life Sciences dataset used in INFORE project, part 2

description: Life Sciences dataset used in INFORE project, part 2 The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction. The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different examples of parameters combinations of our use case that correspond to the different panels of Figure 4 in Documentation folder. This figure comes from the paper in the same folder.<br> You can find a broad discussion of our use case in the Biological Use Case Documentation file. Also,<br> The results of the cell simulations can be found in example_XXX/run0/outputs. The results of the microenvironment simulations can be found in example_XXX/run0/microutputs.<br> Details on how these files are built can be found in Biological Use Case output format file (which is a snippet of the broad documentation file that I detached for your convenience). Briefly: each time step defined, the software writes an output and microutput file. For instance, ecm_t00030.txt correspond to time step 30. Each line of these files corresponds to a cell or microenvironment entity (oxygen, TNF, etc). Columns are defined by the first row for output folder. For the microutputs, the first three columns correspond to spatial coordinates and the fourth to the value of the density. The examples are:<br> - example_spheroid_TNF_nopulse: corresponds to Figure 4 A.<br> - example_spheroid_TNF_onepulse: corresponds to Figure 4 C.<br> - example_spheroid_TNF_pulse150: corresponds to Figure 4 D left. This is the simulation outcome desired: proliferative cells die out with increasing number of pulses of TNF.<br> - example_spheroid_TNF_pulse600: corresponds to Figure 4 D right.<br> - example_spheroid_TNF_pulsecont: corresponds to Figure 4 B.<br> - example_cells_with_ECM_mutants: does NOT correspond to Figure 4. This is an example in which microutput folder is full of two entities: oxygen and ECM. Also, in this example you can find a folder (ECM_mut) with the kind of visualisation that we perform to showcase results.<br> - example_spheroid_TNF_pulsecont_oxy: 21 simulations with slightly different oxygen tolerance conditions using as a base the simulation with one continuous pulse (Figure 4 B from the presentation).<br> The only difference among parameters file is the "oxygen_necrotic" value, which controls the threshold above which cells commit to necrosis due to lack of oxygen. In the original simulation this value was zero and the maximum available oxygen is 40 fg/µm^3. Here, we have studied the parameter value from 0 to 40 in steps of 5.
DOI: 10.5281/zenodo.3924110
title: Dataset S2 - NGS-based S. aureus typing and outbreak analysis in clinical microbiology laboratories: lessons learned from a Swiss-wide proficiency test

description: Supplemental material of article "NGS-based S. aureus typing and outbreak analysis in clinical microbiology laboratories: lessons learned from a Swiss-wide proficiency test" by D. Dylus et al. Please refer to Methods therein. Assemblies and SNP calls for increment 3, including associated pseudonymised epidemiological information.
DOI: 10.5281/zenodo.3368424
title: Dataset S2 - NGS-based S. aureus typing and outbreak analysis in clinical microbiology laboratories: lessons learned from a Swiss-wide proficiency test

description: Supplemental material of article "NGS-based S. aureus typing and outbreak analysis in clinical microbiology laboratories: lessons learned from a Swiss-wide proficiency test" by D. Dylus et al. Please refer to Methods therein. Assemblies and SNP calls for increment 3, including associated pseudonymised epidemiological information.
DOI: 10.21227/ytaz-4a55
title: Defective photonic bandgap crystals using Finite Difference Time Domain (FDTD)

description: This is a dataset of Finite Difference Time Domain (FDTD) simulation results of 13 defective crystals and one non-defective crystal. There are 4 fields in the dataset, namely: Real, Img, Int, and Attribute. The header real shows a real part of the simulated result, img shows the imaginary part, int gives the intensity all in superimposed form. Attribute denotes the label of a crystal simulated. The label 0 is for the simulated crystal, which is non-defective. Other 13 labels, from crystal 1 to crystal 13 are assigned to the 13 different crystals whose simulations are studied.