cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7274346
title: La diffusione del culto di San Terenziano in Italia

description: Questo dataset contiene la posizione geografica e brevi informazioni sulle località e luoghi di culto dove è attestato San Terenziano. È un complemento a quanto pubblicato su https://steko.iosa.it/2022/06/san-terenziano/ per consentire a chiunque di elaborare i dati e aggiornarli. La colonna <em>fonte</em> fa riferimento alla fonte utilizzata per individuare l'attestazione. In particolare: - <em>elenco</em> indica che la fonte è costituita dall'elenco pubblicato su www.encyclocapranica.it, oggi non più disponibile ma riportato nell'articolo;<br> - <em>beweb</em> si riferisce al portale BeWeb;<br> - <em>liguriavincoli</em> si riferisce al portale Liguria Vincoli.
DOI: 10.5281/zenodo.7262562
title: Dataset for Weather drives variation in COVID-19 transmission and detection

description: This dataset contains the input data for the panel regression and SEIR analyses for the paper "Weather drives variation in COVID-19 transmission and detection", as well as the results of the SEIR calibrations. Under the `inputs` directory, the `panel_all.csv` data provides the compiled data for the core analyses. For information on COVID cases, we use daily reports from the John Hopkins University's COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) (Dong et al. 2020). We complement these data with a variety of online sources on confirmed cases at the subnational level. For information on weather, we use ERA 5 reanalysis (Hersbach et al. 2018). The data were downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store in January 2021. The governance data (in `inputs/governance`) is drawn from https://info.worldbank.org/governance/wgi/ for the year 2019. The mapping is done using GADM 3.6, simplified to reduce its size, and stored in `inputs/gadm36_levels_simple`. The result data in the `results` directory represents calibrations of the Bayesian SEIR model: - Files of the form `epimodel-meta-0314...-nodel.csv` include parameter estimates both as initially calibrated (group = "Raw") and after the meta-analysis (group = "Combined"). Paramater estimates are represented by their mean, standard deviation, and five quantiles. Different files of this form reflect different assumptions: (1) `-noweather` exclude weather effects; `-noprior` exclude panel regression priors, and `-full3` include weather and priors; (2) `-all` were estimated using all observations; `-mobile` were estimated using only region/observations with mobility data; (3) `-nobs` include meta-analyzed data weighted by the number of observations; `-pop` are weighted by population; and `-region` are weighted by region. - The `global-0314.RData` file contains calibrations treating the whole world as one region. - The `pairwise.csv` and `pairwise-all.csv` compare data from regions that have valid no-prior (suffix 1) and no-weather (suffix 2) projections. `pairwise.csv` has summary statistics while `pairwise-all.csv` includes every region. Acknowledgements:<br> <br> The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.<br> <br> Bibliography:<br> <br> Dong E, Du H, Gardner L (2020): An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1<br> <br> Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed in January 2021), 10.24381/cds.adbb2d47
DOI: 10.5281/zenodo.7274345
title: La diffusione del culto di San Terenziano in Italia

description: Questo dataset contiene la posizione geografica e brevi informazioni sulle località e luoghi di culto dove è attestato San Terenziano. È un complemento a quanto pubblicato su https://steko.iosa.it/2022/06/san-terenziano/ per consentire a chiunque di elaborare i dati e aggiornarli. La colonna <em>fonte</em> fa riferimento alla fonte utilizzata per individuare l'attestazione. In particolare: - <em>elenco</em> indica che la fonte è costituita dall'elenco pubblicato su www.encyclocapranica.it, oggi non più disponibile ma riportato nell'articolo;<br> - <em>beweb</em> si riferisce al portale BeWeb;<br> - <em>liguriavincoli</em> si riferisce al portale Liguria Vincoli.
DOI: 10.5281/zenodo.7262561
title: Dataset for Weather drives variation in COVID-19 transmission and detection

description: This dataset contains the input data for the panel regression and SEIR analyses for the paper "Weather drives variation in COVID-19 transmission and detection", as well as the results of the SEIR calibrations. Under the `inputs` directory, the `panel_all.csv` data provides the compiled data for the core analyses. For information on COVID cases, we use daily reports from the John Hopkins University's COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) (Dong et al. 2020). We complement these data with a variety of online sources on confirmed cases at the subnational level. For information on weather, we use ERA 5 reanalysis (Hersbach et al. 2018). The data were downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store in January 2021. The governance data (in `inputs/governance`) is drawn from https://info.worldbank.org/governance/wgi/ for the year 2019. The mapping is done using GADM 3.6, simplified to reduce its size, and stored in `inputs/gadm36_levels_simple`. The result data in the `results` directory represents calibrations of the Bayesian SEIR model: - Files of the form `epimodel-meta-0314...-nodel.csv` include parameter estimates both as initially calibrated (group = "Raw") and after the meta-analysis (group = "Combined"). Paramater estimates are represented by their mean, standard deviation, and five quantiles. Different files of this form reflect different assumptions: (1) `-noweather` exclude weather effects; `-noprior` exclude panel regression priors, and `-full3` include weather and priors; (2) `-all` were estimated using all observations; `-mobile` were estimated using only region/observations with mobility data; (3) `-nobs` include meta-analyzed data weighted by the number of observations; `-pop` are weighted by population; and `-region` are weighted by region. - The `global-0314.RData` file contains calibrations treating the whole world as one region. - The `pairwise.csv` and `pairwise-all.csv` compare data from regions that have valid no-prior (suffix 1) and no-weather (suffix 2) projections. `pairwise.csv` has summary statistics while `pairwise-all.csv` includes every region. Acknowledgements:<br> <br> The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.<br> <br> Bibliography:<br> <br> Dong E, Du H, Gardner L (2020): An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1<br> <br> Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed in January 2021), 10.24381/cds.adbb2d47
DOI: 10.3886/e141461v1-130351
title: Code for: The Health Effects of Prison

description: This paper studies the health effects of Swedish prison reforms that held sentences constant but increased the share of time inmates had to serve. The increased time served did not harm post-release health, and actually reduced mortality risk. We find especially large decreases in mortality for offenders not previously incarcerated, younger offenders, and those more attached to the labor market. Risk of suicide and circulatory death fell for inmates with mental health problems and older inmates, respectively. In-prison health care utilization and program participation increases with time served, suggesting healthcare treatment and services as the key mechanism for mortality declines.<br>
DOI: 10.6084/m9.figshare.20576610.v4
title: SUPPLEMENTS

description: This supplement contains <em>Supplementary figure S1 Risk of Bias of Included Studies, </em><em>Supplementary figure S2-S7: Heterogeneity of outcome measures, </em><em>Supplementary figure S8-S20: The Trial Sequential Analysis of the primary outcomes, </em>Table S1. Search Strategy: PubMed, Table S2. Search Strategy: Embase, Table S3. Search Strategy: Cochrane Library, Table S4. Search Strategy: Web of Science, Table S5. Search Strategy: the Chinese Scientific Journal database (VIP), Table S6. Search Strategy: Wanfang database, Table S7. Search Strategy: China National Knowledge Infrastructure (CNKI), Table S8. Search Strategy: SinoMed, <em>Supplementary table S9: Summary of All Included Studies, </em><em>Supplementary table S10: More details about the product information of 7 TCMIs, </em><em>Supplementary table S11: Specific intervention methods of the study, </em><em>Supplementary table S12: Comparisons of the fit of consistency and inconsistency models using deviance information criteria (DIC), </em><em>Supplementary table S14: League table for treatment comparisons in sensitivity analyses by excluding studies that were not within 14 to 30 days of treatment, </em><em>Supplementary table S15: League table for treatment comparisons in sensitivity analyses by excluding studies that were high risk bias, </em><em>Supplementary table S16: The PRISMA checklist of this meta-analysis.</em>
DOI: 10.34876/p00r-9328
title: Longitudinal documentation of pathogens prevalence and loads assessed during the Pneumocystis spp. study in pigs

description: Data underlying the article "Pneumocystis spp. in pigs: a longitudinal quantitative study and co-infections assessment in Austrian farms"
DOI: 10.5281/zenodo.7274369
title: BCdatabaser - its.embryophyta.none.2022-11-02.zip

description: Parameters: --marker-search-string ITS OR ITS2 OR ITS1 OR internal transcribed spacer OR 18s OR 5.8s OR ribulose OR rbcl OR trnl OR trnh OR tRNA-leu OR leucine OR leucine-trna OR trnl-trnf --taxonomic-range Embryophyta --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5281/zenodo.7274368
title: BCdatabaser - its.embryophyta.none.2022-11-02.zip

description: Parameters: --marker-search-string ITS OR ITS2 OR ITS1 OR internal transcribed spacer OR 18s OR 5.8s OR ribulose OR rbcl OR trnl OR trnh OR tRNA-leu OR leucine OR leucine-trna OR trnl-trnf --taxonomic-range Embryophyta --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5281/zenodo.7274373
title: BCdatabaser - its.charophyceae.ITS_rbcL_matK_trnL.2022-11-02.zip

description: Parameters: --marker-search-string ITS OR ITS2 OR ITS1 OR internal transcribed spacer OR 5.8S OR 18S OR rbcL OR ribulose OR matK OR maturaseK OR trnl OR trnh OR trna-leu OR trn-his OR leucine-trna OR trnl-trnf OR trnt-trnl --taxonomic-range Charophyceae --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5281/zenodo.7274372
title: BCdatabaser - its.charophyceae.ITS_rbcL_matK_trnL.2022-11-02.zip

description: Parameters: --marker-search-string ITS OR ITS2 OR ITS1 OR internal transcribed spacer OR 5.8S OR 18S OR rbcL OR ribulose OR matK OR maturaseK OR trnl OR trnh OR trna-leu OR trn-his OR leucine-trna OR trnl-trnf OR trnt-trnl --taxonomic-range Charophyceae --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5517/ccdc.csd.cc26zqxv
title: CCDC 2055328: Experimental Crystal Structure Determination

description: Related Article: A.I. Mohammed, M.M. Bhadbhade, R.W. Read|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|629|doi:10.1107/S205322962200897X
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.cc25hwvf
title: CCDC 2011275: Experimental Crystal Structure Determination

description: Related Article: Y.-Y. An, L.-J. Su, X. Zhao, J.-Z. Yan|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|612|doi:10.1107/S2053229622009445
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.cc253w4b
title: CCDC 1999721: Experimental Crystal Structure Determination

description: Related Article: Y.-Y. An, L.-J. Su, X. Zhao, J.-Z. Yan|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|612|doi:10.1107/S2053229622009445
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.cc2d5s4j
title: CCDC 2210087: Experimental Crystal Structure Determination

description: Related Article: T. Blockhaus, K. Sünkel|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|578|doi:10.1107/S205322962200955X
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.cc2d5s5k
title: CCDC 2210088: Experimental Crystal Structure Determination

description: Related Article: T. Blockhaus, K. Sünkel|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|578|doi:10.1107/S205322962200955X
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.cc2d5s6l
title: CCDC 2210089: Experimental Crystal Structure Determination

description: Related Article: T. Blockhaus, K. Sünkel|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|578|doi:10.1107/S205322962200955X
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.7274371
title: PERTEMUAN 6_03 NOVEMBER 2022_Pelunasan PPh dalam Tahun Berjalan_PERPAJAKAN_UNIVERSITAS WIJAYA PUTRA_JAM 8.00-10.00

description: <strong>Capaian Pembelajaran Mata Kulah (CPMK)</strong> Mahasiswa mampu menjelaskan Gambaran Umum Pelunasan PPh Dalam Tahun Berjalan <strong>Gambaran Umum Pelunasan PPh Dalam Tahun Berjalan:</strong> Pelunasaan melalui pemotong an/ pemungutan oleh pihak lain (PPh Pasal 21/26, PPh Pasal 22, PPh Pasal 23/26, PPh Pasal 24, PPh Final Pasal 4 ayat 2 dan lain-lain)
description: PERTEMUAN 6_03 NOVEMBER 2022_Pelunasan PPh dalam Tahun Berjalan_PERPAJAKAN_UNIVERSITAS WIJAYA PUTRA_JAM 8.00-10.00
DOI: 10.5281/zenodo.7274370
title: PERTEMUAN 6_03 NOVEMBER 2022_Pelunasan PPh dalam Tahun Berjalan_PERPAJAKAN_UNIVERSITAS WIJAYA PUTRA_JAM 8.00-10.00

description: <strong>Capaian Pembelajaran Mata Kulah (CPMK)</strong> Mahasiswa mampu menjelaskan Gambaran Umum Pelunasan PPh Dalam Tahun Berjalan <strong>Gambaran Umum Pelunasan PPh Dalam Tahun Berjalan:</strong> Pelunasaan melalui pemotong an/ pemungutan oleh pihak lain (PPh Pasal 21/26, PPh Pasal 22, PPh Pasal 23/26, PPh Pasal 24, PPh Final Pasal 4 ayat 2 dan lain-lain)
description: PERTEMUAN 6_03 NOVEMBER 2022_Pelunasan PPh dalam Tahun Berjalan_PERPAJAKAN_UNIVERSITAS WIJAYA PUTRA_JAM 8.00-10.00
DOI: 10.5281/zenodo.7274378
title: Dataset of automated earthquake detections in the Hikurangi Margin (New Zealand) during the 2014 Slow Slip Event

description: The dataset provided in this repository contains: - Yarce_REST_events.dat: Contains information of 854 events, with event id, origin time (epoch time), latitude, longitude, depth, number of P arrivals, number of S arrivals, total number of arrivals, local magnitude. - Yarce_REST_arrivals.data: Contains the arrival data information of the 854 events in Yarce_REST_events_v2.csv: event id, station, phase (P or S), arrival time.
DOI: 10.5281/zenodo.7274379
title: Dataset of automated earthquake detections in the Hikurangi Margin (New Zealand) during the 2014 Slow Slip Event

description: The dataset provided in this repository contains: - Yarce_REST_events.dat: Contains information of 854 events, with event id, origin time (epoch time), latitude, longitude, depth, number of P arrivals, number of S arrivals, total number of arrivals, local magnitude, weight, count. Note: last two columns (weight and count) contains the data to build figure 8 - Yarce_REST_arrivals.data: Contains the arrival data information of the 854 events in Yarce_REST_events_v2.csv: event id, station, phase (P or S), arrival time
DOI: 10.5281/zenodo.7274399
title: Dataset of automated earthquake detections in the Hikurangi Margin (New Zealand) during the 2014 Slow Slip Event

description: The dataset provided in this repository contains: - Yarce_REST_events.dat: Contains information of 854 events, with event id, origin time (epoch time), latitude, longitude, depth, number of P arrivals, number of S arrivals, total number of arrivals, local magnitude. - Yarce_REST_arrivals.data: Contains the arrival data information of the 854 events in Yarce_REST_events_v2.csv: event id, station, phase (P or S), arrival time.