cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35078/wf3qrm/yanw6v
title: Cachoeira Faz. B.jpg

description: :unav
DOI: 10.35078/wf3qrm/xee1fj
title: Cabeceira dos Lourenços A.jpg

description: :unav
DOI: 10.35078/wf3qrm/q9syes
title: Silvio Dias Lage B.jpg

description: :unav
DOI: 10.35078/wf3qrm/nm1zr4
title: Garcia B.jpg

description: :unav
DOI: 10.5281/zenodo.6468594
title: FUNCTIONAL ANALYSIS OF GENES FOR REPRODUCTIVE CHARACTERISTICS IN PIGS: FROM GWAS TO POST-GWAS

description: Reproductive characteristics, such as the number of teats, uniformity and litter size, are essential for animal breeding programs due to their importance to the production chain, as they influence the maternal ability of sows and can affect the number of weaned piglets. Our objective was to identify candidate genes associated with reproductive traits in swine, through a systematic review using GWAS data, and to build networks of biological processes and Gene-FTs networks from the identified genes in order to highlight the most candidate genes for litter size and number of teats. The systematic review considered peer-reviewed articles, using descriptors related to the characteristics evaluated, and selected based on eligibility criteria. Fourteen articles were selected and classified into groups for functional analysis of gene networks. 2077 candidate genes were identified from the systematic review. After combining with the list of genes associated with the structural variants present in the 5'UTR and/or coding region, 306 genes remained that were used to construct the networks of biological processes and FTs gene networks, highlighting processes associated with litter size. (eg: ionotropic glutamate receptor signaling pathway and blastocyte growth) and teats number (eg: growth hormone receptor, regulation of the BMP - Bone Morphogenetic Proteins signaling pathway and blood vessel proliferation). three more candidate genes for the litter size trait (GRID2, GRIK3 and PALB2) were prioritized. For the number of teat trait, six genes were highlighted as the most candidate genes (GHR, IFT80, FSTL3, SKOR1, SMURF1 and AKT3). Transcription factors associated with candidate genes were also identified for litter size (PALB2 and GRID2) and teat number (RIN, LTBP2 and COL6A6). Thus, from this study, it is suggested that the genes and TFs presented in this study play an important role in the characteristics studied, being important for genetic studies and animal breeding, because their regulatory genetic mechanisms are involved in the expression phenotypic characteristics of these traits, influencing the reproductive performance and swine productivity. The discovery of these genes may bring new considerations to the current knowledge of the genetic architecture of these traits, since the markers associated with these genes can be assigned higher weights in genomic selection and validated in specific populations.
DOI: 10.35078/wf3qrm/h3pn2y
title: Retiro A.jpg

description: :unav
DOI: 10.35078/wf3qrm/xrgs25
title: Flores B.jpg

description: :unav
DOI: 10.35078/wf3qrm/raar55
title: Tanque A.jpg

description: :unav
DOI: 10.35078/wf3qrm/i5e6z7
title: Lajes B.jpg

description: :unav
DOI: 10.35078/wf3qrm/jku1hc
title: Criminosa B.jpg

description: :unav
DOI: 10.35078/wf3qrm/91kffy
title: Granja Maria B.jpg

description: :unav
DOI: 10.35078/wf3qrm/ezjmny
title: Boa Vista I. B.jpg

description: :unav
DOI: 10.35078/wf3qrm/dbjnhy
title: Cassu B.jpg

description: :unav
DOI: 10.35078/wf3qrm/tgamng
title: Barras Dantas A.jpg

description: :unav
DOI: 10.35078/wf3qrm/cpzzrq
title: Domingos Morais B.jpg

description: :unav
DOI: 10.35078/wf3qrm/nflfca
title: José Anicio Rodrigues A.jpg

description: :unav
DOI: 10.35078/wf3qrm/c5gauo
title: Lajes A.jpg

description: :unav
DOI: 10.35078/wf3qrm/luqyuz
title: Boa Esperança A.jpg

description: :unav
DOI: 10.35078/wf3qrm/sgtwg9
title: Goiabal B.jpg

description: :unav
DOI: 10.35078/wf3qrm/krony0
title: Borba Gato A.jpg

description: :unav
DOI: 10.35078/wf3qrm/ewxke3
title: Córrego dos Américos A.jpg

description: :unav
DOI: 10.35078/wf3qrm/omiwzz
title: Lobos A.jpg

description: :unav
DOI: 10.35078/wf3qrm/uowcfb
title: Jacaré B.jpg

description: :unav
DOI: 10.35078/wf3qrm/jietwn
title: Barra do Grupiara A.jpg

description: :unav
DOI: 10.35078/wf3qrm/bmxbje
title: Barra do Sapé A.jpg

description: :unav
DOI: 10.35078/wf3qrm/hfpjl9
title: Sudário A.jpg

description: :unav
DOI: 10.35078/wf3qrm/ggwomn
title: Mendonça C.jpg

description: :unav
DOI: 10.35078/wf3qrm/xkg2ts
title: Edezio B.jpg

description: :unav
DOI: 10.35078/wf3qrm/mjyhic
title: Alves B.jpg

description: :unav
DOI: 10.35078/wf3qrm/y30dkn
title: DICIONARIO_KARDEX_PROGRAMA_CONTROLE_DOENÇA_CHAGAS.pdf

description: Dicionário para interpretação da ficha epidemiologica (Kardex)
DOI: 10.22003/xfel.eu-data-003219-00
title: HERCULES 2022 - Scientific Instrument Femtosecond X-ray Experiments FXE and data analysis Group A & B
DOI: 10.22003/xfel.eu-data-003224-00
title: Measurement of Mn-containing reference complexes in preparation for 3167 user experiment
DOI: 10.6084/m9.figshare.19611558
title: PRSum_Summary_Statistics

description: Thses are summary statistics to construct Polygenic risk score (PRS) for hypertension (HTN) that we developed (HTN-PRS) in manuscript A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood
DOI: 10.6084/m9.figshare.19611558.v2
title: PRSum_Summary_Statistics

description: Thses are summary statistics to construct Polygenic risk score (PRS) for hypertension (HTN) that we developed (HTN-PRS) in manuscript A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood
DOI: 10.6084/m9.figshare.19611558.v1
title: A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood

description: The files are summary statistics to construct PRSum
DOI: 10.5281/zenodo.6348930
title: Four-year measurements from Sierra Nevada ELF station. Years 2013, 2017

description: This folder contains subfolders for the years 2013 (from March, inclusive) and 2017 (January and February) with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1412 stands for the raw data during December 2014. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348929
title: Four-year measurements from Sierra Nevada ELF station. Years 2013, 2017

description: This folder contains subfolders for the years 2013 (from March, inclusive) and 2017 (January and February) with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1412 stands for the raw data during December 2014. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348838
title: Four-year measurements from Sierra Nevada ELF station. Year 2016

description: This folder contains subfolders for the year 2016 with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1612 stands for the raw data during December 2016. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348837
title: Four-year measurements from Sierra Nevada ELF station. Year 2016

description: This folder contains subfolders for the year 2016 with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1612 stands for the raw data during December 2016. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348773
title: Four-year measurements from Sierra Nevada ELF station. Year 2015

description: This folder contains subfolders for the year 2015 with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1512 stands for the raw data during December 2015. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348772
title: Four-year measurements from Sierra Nevada ELF station. Year 2015

description: This folder contains subfolders for the year 2015 with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1512 stands for the raw data during December 2015. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348691
title: Four-year measurements from Sierra Nevada ELF station. Year 2014

description: This folder contains subfolders for the year 2014 with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1412 stands for the raw data during December 2014. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.5281/zenodo.6348690
title: Four-year measurements from Sierra Nevada ELF station. Year 2014

description: This folder contains subfolders for the year 2014 with time domain raw data from the Sierra Nevada ELF station. Each year contains folders for each month, named with the four digits corresponding to year and month, e.g., 1412 stands for the raw data during December 2014. Within each month folder, the data and information files for each sensor and each hour are available. Data are stored in files containing measurements corresponding to a time period of approximately one hour. The filenames begin with a common part, “smplGRTU1_sensor_”, followed by a specific part to denote the sensor used (0 for the NS orientation and 1 for the EW orientation), the date and the initial time of the measurement recorded. For example, smplGRTU1_sensor_0_1412010430, stands for data measured by the NS-oriented magnetometer, in the year 2014, month 12, day 01, hour 04, and starting minute, 30. The information file has the same name but ends with _info.txt. Since the initial time is not fixed, the filenames in each folder are not completely determined. For this reason, each month folder includes two files, 'ficheros0' and 'ficheros1', that contain the set of filenames for that month. Therefore, for each normally measured we have 24 data files and also 24 information files for each sensor. Each hour data file occupies 1.8 MB, so each month has roughly a data volume of 2.6 GB.
DOI: 10.6084/m9.figshare.3761562.v2192
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5517/ccdc.csd.cc29xxkq
title: CCDC 2142954: Experimental Crystal Structure Determination

description: Related Article: Okan Zafer Yeşilel, Umut Güler, Esengül Çiftçi, Mürsel Arıcı|2022|J.Mol.Struct.|1262|132991|doi:10.1016/j.molstruc.2022.132991
DOI: 10.5517/ccdc.csd.cc29xxlr
title: CCDC 2142955: Experimental Crystal Structure Determination

description: Related Article: Okan Zafer Yeşilel, Umut Güler, Esengül Çiftçi, Mürsel Arıcı|2022|J.Mol.Struct.|1262|132991|doi:10.1016/j.molstruc.2022.132991
DOI: 10.6084/m9.figshare.17824430
title: Supplementary Materials

description: - Summary of retrofit options covered under the EcoENERGY Retrofit for Homes program, 2007-2012<br>- Summary information of selected studies examining energy retrofit drivers<br>- Multiple Linear Regression Outputs
DOI: 10.6084/m9.figshare.17824430.v1
title: Supplementary Materials

description: - Summary of retrofit options covered under the EcoENERGY Retrofit for Homes program, 2007-2012<br>- Summary information of selected studies examining energy retrofit drivers<br>- Multiple Linear Regression Outputs
DOI: 10.6084/m9.figshare.19611600
title: _RoB2_© Dopper A, Bogossian F, Steele M, Hough J. High flow nasal cannula for respiratory support in term infants. Cochrane Database of Systematic Reviews 2014, Issue 3. Art. No.CD011010..xlsm

description: RoB 2 excel tool for Cochrane review
DOI: 10.6084/m9.figshare.19611600.v1
title: _RoB2_© Dopper A, Bogossian F, Steele M, Hough J. High flow nasal cannula for respiratory support in term infants. Cochrane Database of Systematic Reviews 2014, Issue 3. Art. No.CD011010..xlsm

description: RoB 2 excel tool for Cochrane review
DOI: 10.6084/m9.figshare.14115569
title: The origins and genetic interactions of KRAS mutations are allele- and tissue-specific

description: Mutational activation of <em>KRAS</em> promotes the initiation and progression of cancers, especially in the colorectum, pancreas, lung, and blood plasma, with varying prevalence of specific activating missense mutations. Although epidemiological studies connect specific alleles to clinical outcomes, the mechanisms underlying the distinct clinical characteristics of mutant <em>KRAS</em> alleles are unclear. Here, we analyze 13,492 samples from these four tumor types to examine allele- and tissue-specific genetic properties associated with oncogenic <em>KRAS</em> mutations. The prevalence of known mutagenic mechanisms partially explains the observed spectrum of <em>KRAS</em> activating mutations. However, there was are substantial differences between the observed and predicted frequencies for many alleles, suggesting that biological selection underlies the tissue-specific frequencies of mutant alleles. Consistent with experimental studies that have identified distinct signaling properties associated with each mutant form of K-RAS, our genetic analysis reveals that each <em>KRAS</em> allele is associated with a distinct tissue-specific comutation network. Moreover, we identify tissue-specific genetic dependencies associated with specific mutant <em>KRAS</em> alleles. Overall, this analysis demonstrates that the genetic interactions associated with oncogenic <em>KRAS</em> mutations are allele- and tissue-specific, underscoring the complexity that drives their clinical consequences.<br>
DOI: 10.6084/m9.figshare.14115569.v1
title: The origins and genetic interactions of KRAS mutations are allele- and tissue-specific

description: Mutational activation of <em>KRAS</em> promotes the initiation and progression of cancers, especially in the colorectum, pancreas, lung, and blood plasma, with varying prevalence of specific activating missense mutations. Although epidemiological studies connect specific alleles to clinical outcomes, the mechanisms underlying the distinct clinical characteristics of mutant <em>KRAS</em> alleles are unclear. Here, we analyze 13,492 samples from these four tumor types to examine allele- and tissue-specific genetic properties associated with oncogenic <em>KRAS</em> mutations. The prevalence of known mutagenic mechanisms partially explains the observed spectrum of <em>KRAS</em> activating mutations. However, there was are substantial differences between the observed and predicted frequencies for many alleles, suggesting that biological selection underlies the tissue-specific frequencies of mutant alleles. Consistent with experimental studies that have identified distinct signaling properties associated with each mutant form of K-RAS, our genetic analysis reveals that each <em>KRAS</em> allele is associated with a distinct tissue-specific comutation network. Moreover, we identify tissue-specific genetic dependencies associated with specific mutant <em>KRAS</em> alleles. Overall, this analysis demonstrates that the genetic interactions associated with oncogenic <em>KRAS</em> mutations are allele- and tissue-specific, underscoring the complexity that drives their clinical consequences.<br>