cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/2xwcbr
title: d011.gpkg

description: :unav
DOI: 10.57745/zkfqby
title: d001.gpkg

description: :unav
DOI: 10.57745/7q6phk
title: d039.gpkg

description: :unav
DOI: 10.57745/pwkqi8
title: d015.gpkg

description: :unav
DOI: 10.57745/lplgh3
title: d049.gpkg

description: :unav
DOI: 10.57745/4dvbzf
title: d037.gpkg

description: :unav
DOI: 10.57745/5xfonb
title: d023.gpkg

description: :unav
DOI: 10.57745/npwks8
title: d041.gpkg

description: :unav
DOI: 10.57745/cfvuwo
title: d021.gpkg

description: :unav
DOI: 10.57745/h2swsy
title: d027.gpkg

description: :unav
DOI: 10.57745/aj0mer
title: d042.gpkg

description: :unav
DOI: 10.57745/qk2dp7
title: d044.gpkg

description: :unav
DOI: 10.57745/5uefr7
title: d033.gpkg

description: :unav
DOI: 10.57745/da9qqc
title: d031.gpkg

description: :unav
DOI: 10.57745/uxhglc
title: d022.gpkg

description: :unav
DOI: 10.57745/ehr9fb
title: d048.gpkg

description: :unav
DOI: 10.57745/pfm6wl
title: d025.gpkg

description: :unav
DOI: 10.57745/ikuiep
title: d043.gpkg

description: :unav
DOI: 10.57745/0xkjbe
title: d029.gpkg

description: :unav
DOI: 10.57745/a0rcay
title: d034.gpkg

description: :unav
DOI: 10.57745/2hjfau
title: d045.gpkg

description: :unav
DOI: 10.57745/x3tcaw
title: d038.gpkg

description: :unav
DOI: 10.57745/742mx2
title: d035.gpkg

description: :unav
DOI: 10.57745/rllley
title: d040.gpkg

description: :unav
DOI: 10.57745/xnngnk
title: d026.gpkg

description: :unav
DOI: 10.57745/ormv0f
title: d036.gpkg

description: :unav
DOI: 10.57745/sj6dg2
title: d024.gpkg

description: :unav
DOI: 10.57745/tlaejq
title: d046.gpkg

description: :unav
DOI: 10.57745/sa2loe
title: d047.gpkg

description: :unav
DOI: 10.57745/pgcvkv
title: d032.gpkg

description: :unav
DOI: 10.57745/z4qtx6
title: d028.gpkg

description: :unav
DOI: 10.57745/by4tm2
title: d030.gpkg

description: :unav
DOI: 10.57745/q0naks
title: d089.gpkg

description: :unav
DOI: 10.57745/a9kdjk
title: d090.gpkg

description: :unav
DOI: 10.57745/nihinr
title: d087.gpkg

description: :unav
DOI: 10.57745/yv3nln
title: d059.gpkg

description: :unav
DOI: 10.57745/tfusmq
title: d052.gpkg

description: :unav
DOI: 10.57745/cyxh1k
title: d081.gpkg

description: :unav
DOI: 10.57745/uzxhhn
title: d080.gpkg

description: :unav
DOI: 10.57745/a3f4hh
title: d075.gpkg

description: :unav
DOI: 10.57745/p1kj6g
title: d053.gpkg

description: :unav
DOI: 10.57745/9ws2b0
title: d083.gpkg

description: :unav
DOI: 10.57745/n2rc6q
title: d062.gpkg

description: :unav
DOI: 10.57745/tnzv4j
title: d057.gpkg

description: :unav
DOI: 10.57745/fvlbis
title: d066.gpkg

description: :unav
DOI: 10.57745/svuhzq
title: d077.gpkg

description: :unav
DOI: 10.57745/c3zbwa
title: d071.gpkg

description: :unav
DOI: 10.57745/keuhve
title: d055.gpkg

description: :unav
DOI: 10.57745/cfoijk
title: d060.gpkg

description: :unav
DOI: 10.57745/ac8sol
title: d069.gpkg

description: :unav
DOI: 10.57745/86dbjx
title: d074.gpkg

description: :unav
DOI: 10.6084/m9.figshare.23628426
title: simulation_data_supplementary_material.zip

description: The data is stored in documents with the following coding: <br> [topology][population size]_[dynamics]_[mutation scenario].csv topology values = {comp, circ, star} population size values = {10, 20, 30, 40, 50} dynamics = {BDB, DBD, DBB, BDD, LB, LD} mutation scenario = {slow, fast} <br> Each line in the document provides information about the free parameters chosen and the simulation results obtained in a 12-dimensional vector with the following entries: <br> 1) population size 2) staying propensity of mutant 3) staying propensity of resident 4) movement cost 5) type of mutant ({-1, 1}, i.e. {defector, cooperator}) 6) type of resident ({-1, 1}, i.e. {defector, cooperator}) 7) sensitivity 8) cooperating cost 9) cooperating reward 10) exploration phase length 11) fixation time 12) fixation probability
DOI: 10.57745/pjk5g9
title: d088.gpkg

description: :unav
DOI: 10.6084/m9.figshare.23628426.v2
title: simulation_data_supplementary_material.zip

description: The data is stored in documents with the following coding: <br> [topology][population size]_[dynamics]_[mutation scenario].csv topology values = {comp, circ, star} population size values = {10, 20, 30, 40, 50} dynamics = {BDB, DBD, DBB, BDD, LB, LD} mutation scenario = {slow, fast} <br> Each line in the document provides information about the free parameters chosen and the simulation results obtained in a 12-dimensional vector with the following entries: <br> 1) population size 2) staying propensity of mutant 3) staying propensity of resident 4) movement cost 5) type of mutant ({-1, 1}, i.e. {defector, cooperator}) 6) type of resident ({-1, 1}, i.e. {defector, cooperator}) 7) sensitivity 8) cooperating cost 9) cooperating reward 10) exploration phase length 11) fixation time 12) fixation probability
DOI: 10.57745/hhwv9d
title: d093.gpkg

description: :unav
DOI: 10.6084/m9.figshare.23628426.v1
title: simulation_data_supplementary_material.zip

description: The data is stored in documents with the following coding: <br> [topology][population size]_[dynamics]_[mutation scenario].csv topology values = {comp, circ, star} population size values = {10, 20, 30, 40, 50} dynamics = {BDB, DBD, DBB, BDD, LB, LD} mutation scenario = {slow, fast} <br> Each line in the document provides information about the free parameters chosen and the simulation results obtained in a 12-dimensional vector with the following entries: <br> 1) population size 2) staying propensity of mutant 3) staying propensity of resident 4) movement cost 5) type of mutant ({-1, 1}, i.e. {defector, cooperator}) 6) type of resident ({-1, 1}, i.e. {defector, cooperator}) 7) sensitivity 8) cooperating cost 9) cooperating reward 10) exploration phase length 11) fixation time 12) fixation probability
DOI: 10.57745/9pra7r
title: d078.gpkg

description: :unav
DOI: 10.57745/h0x8x8
title: d095.gpkg

description: :unav
DOI: 10.57745/dsegys
title: d073.gpkg

description: :unav
DOI: 10.57745/mooxxc
title: d082.gpkg

description: :unav
DOI: 10.57745/wzxagv
title: d064.gpkg

description: :unav
DOI: 10.57745/ihco7l
title: d065.gpkg

description: :unav
DOI: 10.57745/l88ywn
title: d061.gpkg

description: :unav
DOI: 10.57745/iluvqj
title: d070.gpkg

description: :unav
DOI: 10.57745/u9oslj
title: d063.gpkg

description: :unav
DOI: 10.57745/szpjjk
title: d076.gpkg

description: :unav
DOI: 10.57745/ztt8aa
title: d085.gpkg

description: :unav
DOI: 10.57745/qbteil
title: d068.gpkg

description: :unav
DOI: 10.57745/2ffld0
title: d091.gpkg

description: :unav
DOI: 10.57745/4xlrbk
title: d050.gpkg

description: :unav
DOI: 10.57745/uh2vnj
title: d079.gpkg

description: :unav
DOI: 10.57745/ubl4ho
title: d056.gpkg

description: :unav
DOI: 10.57745/4nazdf
title: d058.gpkg

description: :unav
DOI: 10.57745/6ynuu5
title: d054.gpkg

description: :unav
DOI: 10.57745/qgfaca
title: d094.gpkg

description: :unav
DOI: 10.57745/jmabap
title: d072.gpkg

description: :unav
DOI: 10.57745/kn2idi
title: d067.gpkg

description: :unav
DOI: 10.57745/g2vall
title: d092.gpkg

description: :unav
DOI: 10.57745/wadx4k
title: d086.gpkg

description: :unav
DOI: 10.57745/2axsbr
title: d051.gpkg

description: :unav
DOI: 10.57745/jzwtkh
title: d084.gpkg

description: :unav
DOI: 10.5281/zenodo.7806264
title: Feature selection on microbial profiles of CRC samples with chopin2

description: This Zenodo entry contains the result of the feature selection algorithm implemented through a backward variable elimination strategy and in chopin2 applied on MetaPhlAn3 microbial profiles of a public dataset of metagenomic stool samples collected from patients affected by the colorectal cancer (CRC) as well as from healthy individuals. Microbial profiles have been extracted through the curatedMetagenomicData package for R under the IDs <em>ThomasAM_2018a</em>, <em>ThomasAM_2018b</em>, and <em>ThomasAM_2019_a</em>. The feature selection algorithm is implemented as a backward variable elimination method, and it makes use of the vector-symbolic architecture described in Cumbo F 2020. Deposited data is described below: <em>datasets.tar.gz</em>: it contains the datasets used as input of <em>chopin2</em> as the result of merging the three datasets with relative abundances mentioned above, also stratified by age and sex (with prefix RA). The same datasets have been also binarized (with prefix BIN); <em>hd-models.tar.gz</em>: it contains the output of the feature selection performed with <em>chopin2</em> on the datasets with both relative abundance and binary profiles (RA and BIN); <em>ml-models.tar.gz</em>: it contains the result of the feature selection produced with classical wrapper-based techniques (i.e., Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, and Extreme Gradient Boosting) in addition to a Python 3.8 script to reproduce the results. Please note that the datasets <em>RA__ThomasAM__species.csv</em> and <em>BIN__ThomasAM__species.csv</em> are also included into the <em>datasets.tar.gz</em> archive.
description: {"references": ["Cumbo, Fabio at al. (2020). \"A brain-inspired hyperdimensional computing approach for classifying massive DNA methylation data of cancer\". MDPI Algorithms 2020"]}
DOI: 10.5517/ccdc.csd.cc27vkc1
title: CCDC 2081103: Experimental Crystal Structure Determination

description: Related Article: Yu-Bai Wei, Dong Luo, Xiao Xiong, Yong-Liang Huang, Mo Xie, Weigang Lu, Dan Li|2022|Chemical Science|13|4813|doi:10.1039/D2SC00188H
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.cc27vkf3
title: CCDC 2081105: Experimental Crystal Structure Determination

description: Related Article: Yu-Bai Wei, Dong Luo, Xiao Xiong, Yong-Liang Huang, Mo Xie, Weigang Lu, Dan Li|2022|Chemical Science|13|4813|doi:10.1039/D2SC00188H
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.cc2fgxys
title: CCDC 2248676: Experimental Crystal Structure Determination

description: Related Article: Eleanor Dodd, Simon J. Coles, Terry L. Threlfall, Philip Adler|2023|CSD Communication|||
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.cc2fgy40
title: CCDC 2248682: Experimental Crystal Structure Determination

description: Related Article: Saskia Braun, Philipp Kollenz, Uwe H. F. Bunz|2023|CSD Communication|||
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.cc2fh6tz
title: CCDC 2248951: Experimental Crystal Structure Determination

description: Related Article: Rachel Mata|2023|CSD Communication|||
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.cc2fh1tt
title: CCDC 2248796: Experimental Crystal Structure Determination

description: Related Article: Rui Chen|2023|CSD Communication|||
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.cc2fh1ww
title: CCDC 2248798: Experimental Crystal Structure Determination

description: Related Article: Aleksander Vasilyev|2023|CSD Communication|||
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.cc2fhg9q
title: CCDC 2249183: Experimental Crystal Structure Determination

description: Related Article: Bang-Heng Lyu, Ze-Yu Ruan, Wen Cui, Si-Guo Wu, Zhao-Ping Ni, Ming-Liang Tong|2023|Inorg.Chem.Front.|10|3577|doi:10.1039/D3QI00644A
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.cc2fb9lp
title: CCDC 2244232: Experimental Crystal Structure Determination

description: Related Article: Daria Budzikur-Maciąg, Vasyl Kinzhybalo, Katarzyna Ślepokura|2023|CrystEngComm|25|3826|doi:10.1039/D3CE00186E
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.cc2fb9mq
title: CCDC 2244233: Experimental Crystal Structure Determination

description: Related Article: Daria Budzikur-Maciąg, Vasyl Kinzhybalo, Katarzyna Ślepokura|2023|CrystEngComm|25|3826|doi:10.1039/D3CE00186E
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.cc2fb9nr
title: CCDC 2244234: Experimental Crystal Structure Determination

description: Related Article: Daria Budzikur-Maciąg, Vasyl Kinzhybalo, Katarzyna Ślepokura|2023|CrystEngComm|25|3826|doi:10.1039/D3CE00186E
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.cc2fb9ps
title: CCDC 2244235: Experimental Crystal Structure Determination

description: Related Article: Daria Budzikur-Maciąg, Vasyl Kinzhybalo, Katarzyna Ślepokura|2023|CrystEngComm|25|3826|doi:10.1039/D3CE00186E
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: 10.6084/m9.figshare.23628585
title: Mario Corsi Data

description: The data refer to a research titled: The sport after the COVID-19 pandemic. A study on adolescents attitudes, behaviors, identiity and social support in physical activity.
DOI: 10.6084/m9.figshare.23628585.v1
title: Mario Corsi Data

description: The data refer to a research titled: The sport after the COVID-19 pandemic. A study on adolescents attitudes, behaviors, identiity and social support in physical activity.
DOI: 10.5281/zenodo.8065169
title: Soil moisture sensor network, design, location attributes and soil properties, Hainich, Germany, project AquaDiva

description: This dataset contains information of the small scale highly resolved soil moisture measurement network that is part of the of the AquaDiva Critical Zone exploratory, Hainich National Park, Germany. The dataset contains information on soil measurement locations, as well as attributes to the location, the design type (random locations vs transects), as well as locations attributes like distance to the next tree and soil properties. Measurement design was first introduced by Metzger et al., (2017), and used in Fischer et al., 2023. See there for more information. <strong>References</strong> Fischer-Bedtke, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics – empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023. Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckmüller, J., Küsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783–3795, https://doi.org/10.1002/hyp.11274, 2017.
description: Data were collected by project B02 of the Collaborative Research Centre AquaDiva of the Friedrich Schiller University Jena, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1076 – Project Number 218627073
description: {"references": ["Metzger, J. C., Wutzler, T., Dalla Valle, N., Filipzik, J., Grauer, C., Lehmann, R., Roggenbuck, M., Schelhorn, D., Weckm\u00fcller, J., K\u00fcsel, K., Totsche, K. U., Trumbore, S., and Hildebrandt, A.: Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties, Hydrological Processes, 31, 3783\u20133795, https://doi.org/10.1002/hyp.11274, 2017.", "Fischer, C., Metzger, J. C., Demir, G., Wutzler, T., and Hildebrandt, A.: Throughfall spatial patterns translate into spatial patterns of soil moisture dynamics \u2013 empirical evidence, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-2022-418, 2023."]}
DOI: 10.6084/m9.figshare.23628423
title: Simulation Data

description: The data is stored in documents with the following coding: [topology][population size]_[dynamics]_[mutation scenario].csv <br> topology values = {comp, circ, star} population size values = {10, 20, 30, 40, 50} dynamics = {BDB, DBD, DBB, BDD, LB, LD} mutation scenario = {slow, fast} <br> Each line in the document provides information about the free parameters chosen and the simulation results obtained in a 12-dimensional vector with the following entries: <br> 1) population size 2) staying propensity of mutant 3) staying propensity of resident 4) movement cost 5) type of mutant ({-1, 1}, i.e. {defector, cooperator}) 6) type of resident ({-1, 1}, i.e. {defector, cooperator}) 7) sensitivity 8) cooperating cost 9) cooperating reward 10) exploration phase length 11) fixation time 12) fixation probability
DOI: 10.6084/m9.figshare.23628423.v2
title: Simulation Data

description: The data is stored in documents with the following coding: [topology][population size]_[dynamics]_[mutation scenario].csv <br> topology values = {comp, circ, star} population size values = {10, 20, 30, 40, 50} dynamics = {BDB, DBD, DBB, BDD, LB, LD} mutation scenario = {slow, fast} <br> Each line in the document provides information about the free parameters chosen and the simulation results obtained in a 12-dimensional vector with the following entries: <br> 1) population size 2) staying propensity of mutant 3) staying propensity of resident 4) movement cost 5) type of mutant ({-1, 1}, i.e. {defector, cooperator}) 6) type of resident ({-1, 1}, i.e. {defector, cooperator}) 7) sensitivity 8) cooperating cost 9) cooperating reward 10) exploration phase length 11) fixation time 12) fixation probability
DOI: 10.6084/m9.figshare.23628423.v1
title: simulation_data_for_PEB.zip

description: The data is stored in documents with the following coding: [topology][population size]_[dynamics]_[mutation scenario].csv <br> topology values = {comp, circ, star} population size values = {10, 20, 30, 40, 50} dynamics = {BDB, DBD, DBB, BDD, LB, LD} mutation scenario = {slow, fast} <br> Each line in the document provides information about the free parameters chosen and the simulation results obtained in a 12-dimensional vector with the following entries: <br> 1) population size 2) staying propensity of mutant 3) staying propensity of resident 4) movement cost 5) type of mutant ({-1, 1}, i.e. {defector, cooperator}) 6) type of resident ({-1, 1}, i.e. {defector, cooperator}) 7) sensitivity 8) cooperating cost 9) cooperating reward 10) exploration phase length 11) fixation time 12) fixation probability
DOI: 10.6084/m9.figshare.23628471
title: Placenta PE, single cell RNAseq cellranger files (raw data). Admati, Skarbianskis et al.

description: Raw data from scRNAseq experiments on human placenta from preeclampsia and matched control. The experiment were done using 10Xgenomics V3 (nextGem) technology. Each sample have three files as in normal outpt from cellranger pipeline, samplename_matrix.mtx, samplename_barcodes.tsv, samplename_features.tsv.