cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23563770.v1
title: <strong>SARS-CoV-2 variants identified by real time PCR in the AUNA healthcare network, Peru</strong>

description: SARS-CoV-2 variants detected from September to December (39 to 52 epidemiological weeks) in 2021 and from January to May (1 to 22 epidemiological weeks) in 2022 in Auna healthcare network from Peru determined by the identification of S-gene mutation real-time PCR.
DOI: 10.6084/m9.figshare.23563770
title: <strong>SARS-CoV-2 variants identified by real time PCR in the AUNA healthcare network, Peru</strong>

description: SARS-CoV-2 variants detected from September to December (39 to 52 epidemiological weeks) in 2021 and from January to May (1 to 22 epidemiological weeks) in 2022 in Auna healthcare network from Peru determined by the identification of S-gene mutation real-time PCR.
DOI: 10.5281/zenodo.8071547
title: DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America

description: This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here. <strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. <em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. <em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.
DOI: 10.5281/zenodo.8071548
title: DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America

description: This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here. <strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. <em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. <em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.
DOI: 10.34740/kaggle/dsv/5998708
title: Balochi Language to English Translation Dataset
DOI: 10.6084/m9.figshare.23563782
title: Research data for the kind reviewers of JNT by considering the tetration bases which are congruent to 5 modulo 20.

description: Research data for the kind reviewers of JNT about the smallest perfect powers of degree c=3,4,5,...,32, congruent to 5 modulo 20, having a constant congruence speed of c (please, note that this result has not been included in the submitted manuscript, since the original problem has been proven by using the LTE Lemma anda taking into account the bases congruent to 1 modulo 20).
DOI: 10.18120/xkaj-vg55
title: JNE_pseudoHFO_2022
DOI: 10.6084/m9.figshare.23563614
title: Sequence and annotation files from "Complete genome of the toxic mold <em>Aspergillus pseudotamarii</em> isolate NRRL 25517 reveals genomic instability of the aflatoxin biosynthesis cluster"

description: <br> Aspergillus_pseudotamarii_NRRL25517.fasta - multi-fasta file of scaffolds (chromosomes) and mitochondrial genome Aspergillus_pseudotamarii_NRRL25517.gff - nuclear genome annotation in GFF3 format
DOI: 10.3886/e183523v1
title: Bynum 1-Year Standard Method for identifying Alzheimer’s Disease and Related Dementias (ADRD) in Medicare Claims data

description: Here, you will find resources to use the Bynum-Standard 1-Year Algorithm including a README file that accompanies SAS and Stata scripts for the 1-Year Standard Method for identifying Alzheimer’s Disease and Related Dementias (ADRD) in Medicare Claims data. <br> <br>There are seven script files (plus a parameters file for SAS [parm.sas]) for both SAS and Stata. The files are numbered in the order in which they should be run; the five “1” files may be run in any order.<br><br>The full algorithm requires access to a single year of Medicare Claims data for (1) MedPAR, (2) Home Health Agency (HHA) Claims File, (3) Hospice Claims File, (4) Carrier Claims and Line Files, and (5) Hospital Outpatient File (HOF) Claims and Revenue Files. All Medicare Claims files are expected to be in SAS format (.sas7bdat).<br>For each data source, the script will output three files*:<br><br>Diagnosis-level file: Lists individual ADRD diagnoses for each beneficiary for a given visit. This file allows researchers to identify which ICD-9-CM or ICD-10-CM codes are used in the claims data.Service Date-level file: Aggregated from the Diagnosis-level file, this file includes all beneficiaries with an ADRD diagnosis by Service Date (date of a claim with at least one ADRD diagnosis).Beneficiary-level file: Aggregated from the Service Date-level file, this file includes all beneficiaries with at least one* ADRD diagnosis at any point in the year within a specific file<br>* The algorithm combines the Carrier and HOF files at the Service Date-level. The final combined Carrier and HOF Beneficiary-level file includes those with at least two (2) claims that are seven (7) or more days apart.<br>​<br>A final combined file is created by merging all Beneficiary-level files. This file is used to identify beneficiaries with ADRD and can be merged onto other files by the Beneficiary ID (BENE_ID).<br><br><br>​With appreciation & acknowledgement to colleagues at the NIA IMPACT Collaboratory for their involvement in development & validation of the Bynum-Standard 1-Year Algorithm:
description: NA
description: Response Rates: NA
description: Presence of Common Scales: NA
description: Medicare beneficiaries with Traditional Medicare/FFS (continuous enrollment in parts A and B for one (1) calendar year)
description: Medicare Population, age 65+ in the U.S.A. Smallest Geographic Unit: NA
DOI: 10.3886/e183523v2
title: Bynum 1-Year Standard Method for identifying Alzheimer’s Disease and Related Dementias (ADRD) in Medicare Claims data

description: Here, you will find resources to use the Bynum-Standard 1-Year Algorithm including a README file that accompanies SAS and Stata scripts for the 1-Year Standard Method for identifying Alzheimer’s Disease and Related Dementias (ADRD) in Medicare Claims data. <br> <br>There are seven script files (plus a parameters file for SAS [parm.sas]) for both SAS and Stata. The files are numbered in the order in which they should be run; the five “1” files may be run in any order.<br><br>The full algorithm requires access to a single year of Medicare Claims data for (1) MedPAR, (2) Home Health Agency (HHA) Claims File, (3) Hospice Claims File, (4) Carrier Claims and Line Files, and (5) Hospital Outpatient File (HOF) Claims and Revenue Files. All Medicare Claims files are expected to be in SAS format (.sas7bdat).<br>For each data source, the script will output three files*:<br><br>Diagnosis-level file: Lists individual ADRD diagnoses for each beneficiary for a given visit. This file allows researchers to identify which ICD-9-CM or ICD-10-CM codes are used in the claims data.Service Date-level file: Aggregated from the Diagnosis-level file, this file includes all beneficiaries with an ADRD diagnosis by Service Date (date of a claim with at least one ADRD diagnosis).Beneficiary-level file: Aggregated from the Service Date-level file, this file includes all beneficiaries with at least one* ADRD diagnosis at any point in the year within a specific file<br>* The algorithm combines the Carrier and HOF files at the Service Date-level. The final combined Carrier and HOF Beneficiary-level file includes those with at least two (2) claims that are seven (7) or more days apart.<br>​<br>A final combined file is created by merging all Beneficiary-level files. This file is used to identify beneficiaries with ADRD and can be merged onto other files by the Beneficiary ID (BENE_ID).<br><br><br>With appreciation & acknowledgement to colleagues from a grant funded by the NIA for their involvement in development & validation of the Bynum-Standard 1-Year Algorithm<br>
description: NA
description: Response Rates: NA
description: Presence of Common Scales: NA
description: Medicare beneficiaries with Traditional Medicare/FFS (continuous enrollment in parts A and B for one (1) calendar year)
description: Medicare Population, age 65+ in the U.S.A. Smallest Geographic Unit: NA
DOI: 10.6084/m9.figshare.23563878.v1
title: Trait and Environmental Metadata

description: metadata describing variables in other datasets
DOI: 10.6084/m9.figshare.23563878
title: Trait and Environmental Metadata

description: metadata describing variables in other datasets
DOI: 10.5281/zenodo.8071871
title: enteromius_viviparus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8071870
title: enteromius_viviparus_zanfona_1.fasta.gz

description: .
DOI: 10.25345/c5bg2hm4d
title: MassIVE MSV000092240 - Characterization of O-glycopeptides with 193 nm Ultraviolet Photodissociation
title: Dataset containing raw spectrum files, mzMXL files, and Byonic search data connected to the work by Helms et al. concerning characterization of O-glycopeptides with ultraviolet photodissociation.
DOI: 10.6084/m9.figshare.23563881
title: Trait and Environmental Data

description: leaf and root trait data as well as environmental data collected from sites where seedlings were collected
DOI: 10.6084/m9.figshare.23563881.v1
title: Trait and Environmental Data

description: leaf and root trait data as well as environmental data collected from sites where seedlings were collected
DOI: 10.5281/zenodo.8071883
title: enteromius_treurensis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8071882
title: enteromius_treurensis_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.23563884
title: DDPCR SARSCOV2 VARIANT DETECTION IN POOLED SAMPLES

description: Datasets for publishing in INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES <br>
DOI: 10.6084/m9.figshare.23563884.v1
title: DDPCR SARSCOV2 VARIANT DETECTION IN POOLED SAMPLES

description: Datasets for publishing in INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES <br>
DOI: 10.6084/m9.figshare.23557149.v1
title: Relatório de informação para leigos - roteiro para Pitch

description: Relatório com informações para elaboração do Pitch
DOI: 10.6084/m9.figshare.23557149
title: Relatório de informação para leigos - roteiro para Pitch

description: Relatório com informações para elaboração do Pitch
DOI: 10.5066/p9fhp6xx
title: Estimated crop irrigation water use withdrawals in Sacramento Valley Groundwater Basin, Arizona for 2022

description: Observations of irrigated agricultural land within the Sacramento Valley Groundwater Basin in Arizona. Crops were verified in situ once in 2022 on May 31st; based on digitized field boundaries. Field boundaries were digitized from U.S. Department of Agriculture, National Agricultural Imagery Program County Mosaic 2021 imagery for Arizona and supplemented with Landsat and Sentinel2 imagery collections accessed via the Sentinel Hub, Sentinel Playground (https://apps.sentinel-hub.com/sentinel-playground). Satellite images were also used to identify the length of the growing season and crop condition. Water withdrawals were calculated using the modified Blaney-Criddle model of calculating consumptive use (U.S. Bureau of Reclamation, 1992 appendix A) using crop coefficients from Doorenbos and Pruitt (1975), the number of acres with active crops, crop condition, and irrigation system efficiency. The withdrawal equation was modified from "Water withdrawals for irrigation, municipal, mining, thermoelectric-power, and drainage uses in Arizona outside of active management areas, 1991-2000" (Tadayon, 2005) to account for variations in water application. Data are available as a shapefile for visualization or as a stand-alone csv table for attributes only. Bureau of Reclamation, 1992, Plan of study and methods manual for Colorado River system consumptive uses and losses report 1985–1990: Bureau of Reclamation, Appendix A, 23 p. Doorenbos, J. and Pruitt, W.O.,1975, Guidelines for predicting crop water requirements: Irrigation and drainage paper FAO No. 24, p195, https://www.nrc.gov/docs/ML1821/ML18215A282.pdf. Tadayon, Saeid, 2005, Water withdrawals for irrigation, municipal, mining, thermoelectric-power, and drainage uses in Arizona outside of Active Management Areas, 1991–2000: U.S. Geological Survey Scientific Investigations Report 2004-5293, 28 p.
DOI: 10.5281/zenodo.8071929
title: enteromius_argenteus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8071928
title: enteromius_argenteus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7346709
title: Microsatellite data for Gekko hokouensis and G. yakuensis from southern Kyushu and Tanegashima Island, Japan

description: <strong>README_file.txt</strong> This file explains the contents of the dataset. <strong>Microsatellite_data_for_Gekko_hokouensis_and_G._yakuensis.csv</strong> This file is in STRUCTURE format and contains the raw data of allele lengths collected from 330 individuals using microsatellite genotyping. The first line lists the names of 16 microsatellite loci. The first column lists sample IDs, and the second column lists the names of sampling sites. Two alleles for each sample are listed in two consecutive columns in the subsequent columns. Missing data is represented with -9. These data include genotypic data for 20 pure <em>G. hokouensis</em> and 20 pure <em>G. yakuensis</em> used in Okamoto et al. (2020).
DOI: 10.5281/zenodo.7346708
title: Microsatellite data for Gekko hokouensis and G. yakuensis from southern Kyushu and Tanegashima Island, Japan

description: <strong>README_file.txt</strong> This file explains the contents of the dataset. <strong>Microsatellite_data_for_Gekko_hokouensis_and_G._yakuensis.csv</strong> This file is in STRUCTURE format and contains the raw data of allele lengths collected from 330 individuals using microsatellite genotyping. The first line lists the names of 16 microsatellite loci. The first column lists sample IDs, and the second column lists the names of sampling sites. Two alleles for each sample are listed in two consecutive columns in the subsequent columns. Missing data is represented with -9. These data include genotypic data for 20 pure <em>G. hokouensis</em> and 20 pure <em>G. yakuensis</em> used in Okamoto et al. (2020).
DOI: 10.6084/m9.figshare.23501959
title: Ebola virus inclusion bodies are liquid organelles whose formation is facilitated by nucleoprotein oligomerization

description: Viral RNA synthesis of several non-segmented, negative-sense RNA viruses (NNSVs) takes place in inclusion bodies (IBs) that show properties of liquid organelles, which are formed by liquid–liquid phase separation of scaffold proteins. It is believed that this is driven by intrinsically disordered regions (IDRs) and/or multiple copies of interaction domains, which for NNSVs are usually located in their nucleo – and phosphoproteins. In contrast to other NNSVs, the Ebola virus (EBOV) nucleoprotein NP alone is sufficient to form IBs without the need for a phosphoprotein, and to facilitate the recruitment of other viral proteins into these structures. While it has been proposed that also EBOV IBs are liquid organelles, this has so far not been formally demonstrated. Here we used a combination of live cell microscopy, fluorescence recovery after photobleaching assays, and mutagenesis approaches together with reverse genetics-based generation of recombinant viruses to study the formation of EBOV IBs. Our results demonstrate that EBOV IBs are indeed liquid organelles, and that oligomerization but not IDRs of the EBOV nucleoprotein plays a key role in their formation. Additionally, VP35 (often considered the phosphoprotein-equivalent of EBOV) is not essential for IB formation, but alters their liquid behaviour. These findings define the molecular mechanism for the formation of EBOV IBs, which play a central role in the life cycle of this deadly virus.
DOI: 10.6084/m9.figshare.23501959.v2
title: Ebola virus inclusion bodies are liquid organelles whose formation is facilitated by nucleoprotein oligomerization

description: Viral RNA synthesis of several non-segmented, negative-sense RNA viruses (NNSVs) takes place in inclusion bodies (IBs) that show properties of liquid organelles, which are formed by liquid–liquid phase separation of scaffold proteins. It is believed that this is driven by intrinsically disordered regions (IDRs) and/or multiple copies of interaction domains, which for NNSVs are usually located in their nucleo – and phosphoproteins. In contrast to other NNSVs, the Ebola virus (EBOV) nucleoprotein NP alone is sufficient to form IBs without the need for a phosphoprotein, and to facilitate the recruitment of other viral proteins into these structures. While it has been proposed that also EBOV IBs are liquid organelles, this has so far not been formally demonstrated. Here we used a combination of live cell microscopy, fluorescence recovery after photobleaching assays, and mutagenesis approaches together with reverse genetics-based generation of recombinant viruses to study the formation of EBOV IBs. Our results demonstrate that EBOV IBs are indeed liquid organelles, and that oligomerization but not IDRs of the EBOV nucleoprotein plays a key role in their formation. Additionally, VP35 (often considered the phosphoprotein-equivalent of EBOV) is not essential for IB formation, but alters their liquid behaviour. These findings define the molecular mechanism for the formation of EBOV IBs, which play a central role in the life cycle of this deadly virus.
DOI: 10.5517/ccdc.csd.cc2290gw
title: CCDC 1915287: Experimental Crystal Structure Determination

description: Related Article: Changpeng Chen, Zhijun Wang, Sha Wang, Liang Xu, Xiaoming Zeng|2023|Org.Lett.|25|4241|doi:10.1021/acs.orglett.3c01156
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.cc2290hx
title: CCDC 1915288: Experimental Crystal Structure Determination

description: Related Article: Changpeng Chen, Zhijun Wang, Sha Wang, Liang Xu, Xiaoming Zeng|2023|Org.Lett.|25|4241|doi:10.1021/acs.orglett.3c01156
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.cc25t76g
title: CCDC 2020245: Experimental Crystal Structure Determination

description: Related Article: Changpeng Chen, Zhijun Wang, Sha Wang, Liang Xu, Xiaoming Zeng|2023|Org.Lett.|25|4241|doi:10.1021/acs.orglett.3c01156
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.cc27fkt2
title: CCDC 2068624: Experimental Crystal Structure Determination

description: Related Article: Yao Zhao, Shuangqiang Wang, Huihui Shao, Xuyong Tan, Rizhi Chen, Teck-Peng Loh, Jianrong Steve Zhou, Xiaojin Wu|2023|Org.Lett.|25|4258|doi:10.1021/acs.orglett.3c01214
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.cc24lt5r
title: CCDC 1984284: Experimental Crystal Structure Determination

description: Related Article: Lixin Liu, Qi An, Yu Duan, Yunan Zhang, Liang Chang, Yingli Liu, Haiting Zhang|2023|Journal of Drug Delivery Science and Technology|86|104634|doi:10.1016/j.jddst.2023.104634
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.cc26zdyk
title: CCDC 2055019: Experimental Crystal Structure Determination

description: Related Article: A.H. Udaya Kumar, K.J. Pampa, Nanishankar V. Harohally, Dipankar Das, Biswa Nath Ghosh, N.K. Lokanath|2023|Mater.Chem.Phys.|306|128031|doi:10.1016/j.matchemphys.2023.128031
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.cc28qp6x
title: CCDC 2107169: Experimental Crystal Structure Determination

description: Related Article: Yao Zhao, Shuangqiang Wang, Huihui Shao, Xuyong Tan, Rizhi Chen, Teck-Peng Loh, Jianrong Steve Zhou, Xiaojin Wu|2023|Org.Lett.|25|4258|doi:10.1021/acs.orglett.3c01214
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.