cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25318/3310059301-fra
title: Profils des entreprises du Canada rural, postes de dépenses des moyennes entreprises constituées en société selon l'industrie et l'indicateur d'emplacement; calculs basés sur le revenu total

description: Cette table contient les dépenses totales, coût de ventes (frais direct), salaires et avantages sociaux, achats et matériaux et sous-traitance, inventaire d'ouverture, inventaire de fermeture, dépenses d'exploitation (frais indirects), traitements et commissions, amortissement et tarissement, réparations et entretien, services publics et téléphone, loyer/location, intérêts et frais bancaires, honoraires professionnels et commerciaux, publicité et promotion, livraison, assurances, autres dépenses indirectes, ratio bénéfice net ou perte nette. Entreprises constituées en société uniquement. Les valeurs sont des moyennes en dollar courant sauf indication contraire.
DOI: 10.5281/zenodo.7604025
title: CamaraLab/ConDecon : Tutorial Data

description: <strong>ConDecon</strong> is a clustering-independent deconvolution method for estimating cell abundances in bulk tissues using single-cell RNA-seq data. The aim of ConDecon is to infer a probability distribution across a reference single-cell dataset that represents the likelihood for each cell in the reference data to be present in the query bulk tissue. ConDecon enables previously elusive analyses of dynamic cellular processes in bulk tissues and represents an increase in functionality and phenotypic resolution with respect to current methods for gene expression deconvolution. We anticipate that these features will improve our understanding of tissue cell composition by facilitating the inference of cell state abundances within complex bulk tissues, particularly in the context of evolving systems like development and disease progression. In the ConDecon GitHub repository, we demonstrate ConDecon's utility by applying it to transcriptomic data (ConDecon_B_cell_Tutorial), spatial transcriptomic data (ConDecon_Spatial_RNA_Tutorial), and chromatin accessibility data (ConDecon_ATAC_Tutorial). These datasets were downloaded from open-source data repositories (referenced below) and re-processed by the Camara lab. For convenience, the re-processed data associated with these tutorials has been uploaded below. Aubin, R. G., Montelongo, J., Hu, R., Camara, P. G. <em>Clustering-independent estimation of cell abundances in bulk tissue using single-cell RNA-seq data</em>. <strong>Biorxiv</strong> (2023).
description: {"references": ["The Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590\u2013595 (2020). https://doi.org/10.1038/s41586-020-2496-1", "Liu, Chang, et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Developmental Cell 57.10 (2022): https://doi.org/10.1016/j.devcel.2022.04.009.", "Bravo Gonz\u00e1lez-Blas, C., Minnoye, L., Papasokrati, D. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat Methods 16, 397\u2013400 (2019). https://doi.org/10.1038/s41592-019-0367-1"]}
DOI: 10.5281/zenodo.7604026
title: CamaraLab/ConDecon : Tutorial Data

description: <strong>ConDecon</strong> is a clustering-independent deconvolution method for estimating cell abundances in bulk tissues using single-cell RNA-seq data. The aim of ConDecon is to infer a probability distribution across a reference single-cell dataset that represents the likelihood for each cell in the reference data to be present in the query bulk tissue. ConDecon enables previously elusive analyses of dynamic cellular processes in bulk tissues and represents an increase in functionality and phenotypic resolution with respect to current methods for gene expression deconvolution. We anticipate that these features will improve our understanding of tissue cell composition by facilitating the inference of cell state abundances within complex bulk tissues, particularly in the context of evolving systems like development and disease progression. In the ConDecon GitHub repository, we demonstrate ConDecon's utility by applying it to transcriptomic data (ConDecon_B_cell_Tutorial), spatial transcriptomic data (ConDecon_Spatial_RNA_Tutorial), and chromatin accessibility data (ConDecon_ATAC_Tutorial). These datasets were downloaded from open-source data repositories (referenced below) and re-processed by the Camara lab. For convenience, the re-processed data associated with these tutorials has been uploaded below. Aubin, R. G., Montelongo, J., Hu, R., Camara, P. G. <em>Clustering-independent estimation of cell abundances in bulk tissue using single-cell RNA-seq data</em>. <strong>Biorxiv</strong> (2023).
description: {"references": ["The Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590\u2013595 (2020). https://doi.org/10.1038/s41586-020-2496-1", "Liu, Chang, et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Developmental Cell 57.10 (2022): https://doi.org/10.1016/j.devcel.2022.04.009.", "Bravo Gonz\u00e1lez-Blas, C., Minnoye, L., Papasokrati, D. et al. cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat Methods 16, 397\u2013400 (2019). https://doi.org/10.1038/s41592-019-0367-1"]}
DOI: 10.5281/zenodo.7629053
title: Dimerization of melanocortin 4 receptor controls puberty onset and body size polymorphism

description: Example dataset and scripts used in the analysis of PIE-based FRET-FLIM data of the Mc4r receptor.
DOI: 10.5281/zenodo.7629052
title: Dimerization of melanocortin 4 receptor controls puberty onset and body size polymorphism

description: Example dataset and scripts used in the analysis of PIE-based FRET-FLIM data of the Mc4r receptor.
DOI: 10.18709/perscido.2023.02.ds382
title: Championship Value Prediction 1 Public Traces

description: The following traces are 135 "public" execution traces that were generated for the Championship Value Prediction 1 (https://www.microarch.org/cvp1/cvp1online/contestants.html) that took place with the IEEE/ACM International Symposium on Computer Architecture (ISCA) 2018. There traces contain instructions executed by ARMv8 workloads of interest to CPU design. The traces only contain partial information and are anonymized, in the sense that the program from which a given trace was generated is not available. All traces used in CVP-1 were released to the public domain after CVP-1.

This release contains :
- ./public_traces : 135 traces containing 30M instructions, known as the "public traces" as this is what was released to CVP-1 contestants to develop their model

Additional information about how to use the traces and the trace format is provided in the README.
DOI: 10.25625/1tjvme/qgiryi
title: woche06.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 6
DOI: 10.25625/1tjvme/dzcsc6
title: woche02_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 2
DOI: 10.25625/1tjvme/t94hoo
title: woche02.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 2
DOI: 10.25625/1tjvme/nu0lr3
title: woche04_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 4
DOI: 10.25625/1tjvme/uv3qju
title: woche11.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 11, no new pseudonyms in this week
DOI: 10.25625/1tjvme/ga53gz
title: woche08_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 8
DOI: 10.25625/1tjvme/yuh0me
title: woche06_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 6
DOI: 10.25625/1tjvme/xverg8
title: woche10_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 10
DOI: 10.25625/1tjvme/3g2dag
title: woche07.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 7
DOI: 10.25625/1tjvme/2vvb8q
title: woche03_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 3
DOI: 10.25625/1tjvme/dmgpxb
title: woche12_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 12
DOI: 10.25625/1tjvme/gjmqix
title: algos_nach_tag.dat

description: Algo-counter data from 10/19/2020 to 02/22/2021
DOI: 10.25625/1tjvme/c37upg
title: gesamt_cceval.tab

description: Full/matched dataset of weekly Coding Class evaluation in winter semester 2019/20
DOI: 10.25625/1tjvme/5spbm3
title: Eingeladene_Tutoren_Feedback2020.md

description: Results from group interview with selected student tutors on the basis of the results of the written interviews in winter semester 2019/20
DOI: 10.25625/1tjvme/qsfmyx
title: woche13.csv

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 13
DOI: 10.25625/1tjvme/bff46c
title: woche05.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 5
DOI: 10.25625/1tjvme/ukup2o
title: woche09_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 9
DOI: 10.25625/1tjvme/irqaqu
title: woche08.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 8
DOI: 10.25625/1tjvme/0axij9
title: woche13_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 13
DOI: 10.25625/1tjvme/vvijwo
title: Tutorenfeedback2021.md

description: Results from written interview with student tutors in winter semester 2020/21
DOI: 10.25625/1tjvme/hrpgd4
title: Tutoren-Feedback 2022.md

description: Results from written interview with student tutors in winter semester 2021/22
DOI: 10.25625/1tjvme/oslfit
title: woche09.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 9
DOI: 10.25625/1tjvme/taugyz
title: woche07_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 7
DOI: 10.25625/1tjvme/wqaijy
title: woche01.csv

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 1
DOI: 10.25625/1tjvme/hvuzbl
title: woche10.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 10
DOI: 10.25625/1tjvme/pkcbua
title: Tutorenfeedback2020.md

description: Results from written interview with student tutors in winter semester 2019/20
DOI: 10.25625/1tjvme/f4wewb
title: woche03.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 3
DOI: 10.25625/1tjvme/ptoajm
title: aud22zwischen.csv

description: Dataset from midterm evaluation winter semester 2021/22
DOI: 10.25625/1tjvme/ui2rnh
title: woche12.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 12
DOI: 10.25625/1tjvme/xn6nkq
title: woche04.tab

description: Dataset of weekly Coding Class evaluation in winter semester 2019/20 - Week 4
DOI: 10.25625/1tjvme/vtrh1w
title: woche05_neue_pseudonyme.tab

description: New pseudonyms in weekly Coding Class evaluation in winter semester 2019/20 - Week 5
DOI: 10.25625/1tjvme/jaym4b
title: evaluation_2019.pdf

description: Evaluation results at the end of winter semester 2018/19 for comparison to new concept
DOI: 10.25625/1tjvme/efyh0r
title: AuD_gesamteval_2021.csv

description: Dataset from evaluation at the end of winter semester 2020/21 including items concerning gamification
DOI: 10.25625/1tjvme/lhbnif
title: aud20.csv

description: Dataset from evaluation at the end of winter semester 2019/20 (first year with new concept - Coding Classes and Milestones)
DOI: 10.25625/1tjvme/ir6uvq
title: aud22.csv

description: Dataset from evaluation at the end of winter semester 2021/22 including items concerning gamification
DOI: 10.25625/1tjvme/ehrsop
title: quexmlpdf_578635_de.pdf

description: pdf of survey for evaluation at the end of winter semester 2021/22
DOI: 10.25625/1tjvme/bvjc1z
title: survey202122.csv

description: questions, answers and codes for evaluation at the end of winter semester 2021/22
DOI: 10.25625/1tjvme/3pgjgt
title: survey201920.csv

description: questions, answers and codes for evaluation at the end of winter semester 2019/20
DOI: 10.25625/1tjvme/ychcx3
title: survey202021.csv

description: questions, answers and codes for evaluation at the end of winter semester 2020/21
DOI: 10.25625/1tjvme/6cyyx9
title: quexmlpdf_2021_de.pdf

description: pdf of survey for evaluation at the end of winter semester 2020/21
DOI: 10.25625/1tjvme/qtofgc
title: quexmlpdf_2020_de.pdf

description: pdf of survey for evaluation at the end of winter semester 2019/20
DOI: 10.25625/1tjvme
title: Data from module evaluations (students and tutors) from 2019 to 2022 including Gamification data and Algo-counter data

description: Data for publications "Heterogenitätsbewusste Digitale Gestaltung Eines Einführungsmoduls in Der Informatik" and "Cooperative Gamification in a Computer Science Introductory Module", referenced in chapter 5. Additional data used for chapter 6. Module evaluation data at the end of the semester: - Winter semester 2018/19 - from StudIP - Winter semester 2019/20 - from LimeSurvey - Winter semester 2020/21 - from LimeSurvey - Winter semester 2021/22 - from LimeSurvey Weekly evaluation data for Coding Classes for winter semester 2019/20 Written interview data with student tutors: - Winter semester 2019/20 - Winter semester 2020/21 - Winter semester 2021/22 Group interview protocol with selected student tutor on the basis of written interview from winter semester 2019/20 Algo-counter data from 10/19/2020 to 02/22/2021 (winter semester 2020/21)
DOI: 10.6084/m9.figshare.21823455.v14
title: The ReptileEvolution.com Project

description: Taxon exclusion is the number one problem currently vexing paleontology. The search for pterosaur, turtle, bat, snake, whale, reptile and tetrapod ancestors continues to unnecessarily befuddle paleontologists who continue to work from cherry-picked, textbook and traditional taxon lists that overlook and omit pertinent taxa. That’s why the large reptile tree (LRT), the nearly completely resolved cladogram at the heart of www.ReptileEvolution.com, includes 2212 taxa. Larger taxon numbers minimize taxon exclusion issues. Results indicate the simple act of adding taxa resolves all phylogenetic issues.
DOI: 10.18709/perscido.2023.02.ds384
title: Championship Value Prediction 1 Secret Traces

description: The following traces are 2013 "secret" execution traces that were generated for the Championship Value Prediction 1 (https://www.microarch.org/cvp1/cvp1online/contestants.html) that took place with the IEEE/ACM International Symposium on Computer Architecture (ISCA) 2018. There traces contain instructions executed by ARMv8 workloads of interest to CPU design. The traces only contain partial information and are anonymized, in the sense that the program from which a given trace was generated is not available. All traces used in CVP-1 were released to the public domain after CVP-1.

This release contains :
- ./secret_traces : 2013 traces containing 100M instructions, known as the "secret traces" as this is what contestant code was evaluated on during CVP-1

Additional information about how to use the traces and the trace format is provided in the README.
DOI: 10.17037/data.00003188
title: Data for: Coverage and effectiveness of conditional cash transfer for people with drug resistant tuberculosis in Zimbabwe

description: A dataset containing participant information collected during a study to assess the coverage and effectiveness of conditional cash transfers for people on drug resistant tuberculosis in Zimbabwe. Data may be requested for the purpose of ethically-approved research and verification purposes, subject to the signing of a data transfer agreement that outlines conditions of confidentiality. Further de-identification may be necessary to reduce the accuracy of values, based upon the dataset measures that are requested.
DOI: 10.5281/zenodo.7627033
title: Tonatiuh Dataset

description: To perform a detailed study of the Kansa asymmetric collocation method, applied to heat transfer problems, we have designed, constructed, stored and labeled a database, which we have named Tonatiuh. This database is composed of four tables, which describe 303, 337 solutions obtained of solving the models Steady-state heat conduction and Steady-state advection-diffusion for different values of the input physical parameters (P) and numerical hyperparameters (HP).
DOI: 10.5281/zenodo.7627032
title: Tonatiuh Dataset

description: To perform a detailed study of the Kansa asymmetric collocation method, applied to heat transfer problems, we have designed, constructed, stored and labeled a database, which we have named Tonatiuh. This database is composed of four tables, which describe 303, 337 solutions obtained of solving the models Steady-state heat conduction and Steady-state advection-diffusion for different values of the input physical parameters (P) and numerical hyperparameters (HP).
DOI: 10.5281/zenodo.7624677
title: Modular Differential Evolution: Supplementary Material

description: This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. The following files are included: - Modde.zip: A zip-file of the version of the modular differential evolution code used throughout the paper. This is a static version of the github repository (not linked now to preserve anonymity). This can be installed using pip; installation is needed for the data collection script - Irace_files: A zip-file containing all relevant settings and code for running irace to tune on BBOB, which can be processed into the set of elites (irace_elites_modde.csv). - *_runner: Python files which runs each elite and each common + single module DE on the required instances. Resulting IOH-files are included in the 'raw_data' zipfile - R files: code which takes the raw IOH data and turns it into the relevant csv-files which are used to create the figures - Visualization*: notebooks which are used to generate the figures from the papers
DOI: 10.5281/zenodo.7624676
title: Modular Differential Evolution: Supplementary Material

description: This repository contains the code and data for reproducibility of the paper 'Modular Differential Evolution'. The following files are included: - Modde.zip: A zip-file of the version of the modular differential evolution code used throughout the paper. This is a static version of the github repository (not linked now to preserve anonymity). This can be installed using pip; installation is needed for the data collection script - Irace_files: A zip-file containing all relevant settings and code for running irace to tune on BBOB, which can be processed into the set of elites (irace_elites_modde.csv). - *_runner: Python files which runs each elite and each common + single module DE on the required instances. Resulting IOH-files are included in the 'raw_data' zipfile - R files: code which takes the raw IOH data and turns it into the relevant csv-files which are used to create the figures - Visualization*: notebooks which are used to generate the figures from the papers
DOI: 10.15124/d9e75138-5055-4ed7-a7ae-6188136ec8eb
title: Synergy of UV light and heat in peptide degradation

description: A set of raw experimental data for publication "Synergy of UV light and heat in peptide degradation" in Journal of Photochemistry & Photobiology, A: Chemistry
DOI: 10.6084/m9.figshare.22068830
title: Heterogeneous fibroblasts contribute to fibrotic scar formation after spinal cord injury

description: Rhesus macaques underwent transection SCI (8 mm) and were sacrificed at 7 dpi, 14dpi, 28 dpi, and 6 mpi. Single-nucleus RNA-seq was performed on tissues dissected from the lesion edge, while scRNA-seq was performed on tissuesfrom the lesion core.
DOI: 10.6084/m9.figshare.22068830.v1
title: Heterogeneous fibroblasts contribute to fibrotic scar formation after spinal cord injury

description: Rhesus macaques underwent transection SCI (8 mm) and were sacrificed at 7 dpi, 14dpi, 28 dpi, and 6 mpi. Single-nucleus RNA-seq was performed on tissues dissected from the lesion edge, while scRNA-seq was performed on tissuesfrom the lesion core.
DOI: 10.6084/m9.figshare.22069034
title: PRISMA_2020_checklist.pdf

description: Kostares E, Kostare G, Kostares M, Kantzanou M (2023) Prevalence of carpal tunnel syndrome among dentists: a systematic review and meta-analysis. PRISMA 2020 checklist. figshare. Dataset.
DOI: 10.6084/m9.figshare.19575148
title: HMM reference database for prediction of O-antigen serogroup in V. cholerae genomes

description: To predict the antigenic serogroup, we screened the assemblies against a custom reference database using Hamburger (Williams, 2021). Details of reference sequences and loci are supplied in the 'serogrouping_reference_sequence.xlsx' spreadsheet. In brief, a database was constructed by selecting flanking and marker genes for the operon encoding the <em>V. cholerae</em> O-antigen, with representative genes for both O1 and non-O1 serogroups included. Gene sequences were individually aligned using Clustal Omega (version 1.2.4), prior to HMM construction with HMMER (version 3.2.1) and concatenation of the HMM alignments. Assemblies were screened against this database using Hamburger (version 836a77c) to identify the operon, and genetic structure was compared across the assemblies and references to designate serogroups.
DOI: 10.6084/m9.figshare.22069103
title: Database 1.xlsx

description: Databases used for the study entitled: "One-Leg Stance Postural Sway Is Not Benefited by Bicycle Motocross Practice in Elite Riders".
DOI: 10.6084/m9.figshare.22069895
title: Birder Code

description: Simulated Dataset to run code.
DOI: 10.21943/auas.5432761
title: interviews beacon

description: interview met consument over ervaring met beacons in de byAMFI-Statement store
DOI: 10.21943/auas.5432761.v1
title: interviews beacon

description: interview met consument over ervaring met beacons in de byAMFI-Statement store
DOI: 10.5517/ccdc.csd.cc2b8tbs
title: CCDC 2153425: Experimental Crystal Structure Determination

description: Related Article: Tuncay Yeşilkaynak, Fatma Nur Özkömeç, Mustafa Çeşme, Ruken Esra Demirdöğen, Canan Vejselova Sezer, Hatice Mehtap Kutlu, Fatih Mehmet Emen|2023|J.Mol.Struct.|1280|135086|doi:10.1016/j.molstruc.2023.135086
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.cc2bjkfw
title: CCDC 2160868: Experimental Crystal Structure Determination

description: Related Article: Biprajit Paul, Surajit Das, Indranil Chatterjee|2023|Org.Lett.|25|653|doi:10.1021/acs.orglett.2c04261
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.