Resultados totales (Incluyendo duplicados): 33
Encontrada(s) 4 página(s)
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/288226
Dataset. 2023

SPEIBASE V.2.8 [DATASET]

  • Beguería, Santiago
  • Vicente Serrano, Sergio M.
  • Reig-Gracia, Fergus
  • Latorre Garcés, Borja
The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.7 (https://digital.csic.es/handle/10261/268088).-- What’s new in version 2.8: 1) Based on the CRU TS 4.06 dataset, spanning the period between January 1901 to December 2021. For more details on the SPEI visit http://sac.csic.es/spei, No

Proyecto: //
DOI: http://hdl.handle.net/10261/288226, https://doi.org/10.20350/digitalCSIC/15121
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/288226
HANDLE: http://hdl.handle.net/10261/288226, https://doi.org/10.20350/digitalCSIC/15121
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/288226
PMID: http://hdl.handle.net/10261/288226, https://doi.org/10.20350/digitalCSIC/15121
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/288226
Ver en: http://hdl.handle.net/10261/288226, https://doi.org/10.20350/digitalCSIC/15121
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/288226

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/291131
Dataset. 2023

MOPREDASCENTURY: A LONG-TERM MONTHLY PRECIPITATION GRID FOR THE SPANISH MAINLAND, V.2.0.0 [DATASET]

MOPREDASCENTURY_PP_1916-2020_ZERO-INFLATED

  • Beguería, Santiago
  • Peña-Angulo, Dhais
  • Trullenque Blanco, Víctor
  • González Hidalgo, José Carlos
[EN] A monthly precipitation gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations. Spatial interpolation was performed using geostatistical techniques over a regular 0.1° × 0.1° grid, using a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude., [ES] Conjunto de datos en rejilla de precipitación mensual en la España peninsular, entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 11.312 observatorios. Se utilizaron técnicas geoestadísticas para interpolar espacialmente las observaciones sobre una rejilla regular de 0.1° × 0.1°, utilizando un proceso en dos pasos: en primer lugar se interpoló la probabilidad de mes seco (precipitación igual a cero), y en un segundo paso la magnitud de la precipitación., Projects CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland) and PID2020-116860RB-C22 EXE: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Mean grid; standard deviation grid; netCDF., No

DOI: http://hdl.handle.net/10261/291131, https://doi.org/10.20350/digitalCSIC/15136
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/291131
HANDLE: http://hdl.handle.net/10261/291131, https://doi.org/10.20350/digitalCSIC/15136
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/291131
PMID: http://hdl.handle.net/10261/291131, https://doi.org/10.20350/digitalCSIC/15136
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/291131
Ver en: http://hdl.handle.net/10261/291131, https://doi.org/10.20350/digitalCSIC/15136
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/291131

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/308427
Dataset. 2022

FLOWCHART OUTLINING THE PIPELINE FOR SMALL RNASEQ ANALYSIS

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
1 figure, Including the identification of known and putative novel miRNAs, miRNA abundance profiling and differential abundance analysis. rRNA: ribosomal RNA; tRNA: transfer RNA; snoRNA: small nucleolar RNA; snRNA: small nuclear RNA; RE: repeat elements; qPCR: quantitative real-time PCR., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/308427
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/308427
HANDLE: http://hdl.handle.net/10261/308427
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/308427
PMID: http://hdl.handle.net/10261/308427
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/308427
Ver en: http://hdl.handle.net/10261/308427
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/308427

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310598
Dataset. 2022

STACKED BAR PLOT REPORTING THE FRACTION OF SMALL RNASEQ READS ASSIGNED TO THE ANNOTATED FELIS CATUS MIRNAS (FCA-MIRNAS) FROM ENSEMBL V.99 (BLUE), FELINE GENOME (ORANGE) OR THAT WERE NOT MAPPED (RED)

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
1 figure, CKD: Chronic kidney disease; PN: Pyelonephritis; SB/C: Subclinical bacteriuria/Cystitis; UO: Ureteral obstruction, Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310598, https://doi.org/10.20350/digitalCSIC/15309
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310598
HANDLE: http://hdl.handle.net/10261/310598, https://doi.org/10.20350/digitalCSIC/15309
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310598
PMID: http://hdl.handle.net/10261/310598, https://doi.org/10.20350/digitalCSIC/15309
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310598
Ver en: http://hdl.handle.net/10261/310598, https://doi.org/10.20350/digitalCSIC/15309
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310598

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310603
Dataset. 2022

PRINCIPAL COMPONENT ANALYSIS (PCA) OF SAMPLES PROFILED BY SMALL RNASEQ TECHNIQUE

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
A. PCA of urine samples on the basis of normalized read counts of the known and putative novel miRNAs for the 38 samples initially processed. The red arrows indicate the outlier Control samples (C5, C6 and C7). B. PCA excluding the high outlier samples. CKD: Chronic kidney disease; PN: Pyelonephritis; SB/C: Subclinical bacteriuria/Cystitis; UO: Ureteral obstruction., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310603
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310603
HANDLE: http://hdl.handle.net/10261/310603
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310603
PMID: http://hdl.handle.net/10261/310603
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310603
Ver en: http://hdl.handle.net/10261/310603
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310603

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310606
Dataset. 2022

DETAILED CHARACTERISTICS OF THE KNOWN AND PUTATIVE NOVEL MIRNAS IN CAT URINE FOR THE 35 SAMPLES BASED ON RNASEQ DATA

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
A. Proportion of samples for which each of the known miRNAs across the different groups were detected. B. Cumulative abundance of the known feline miRNAs. The dots indicate the log10 of the miRNA abundance for each miRNA. miRNAs are sorted in each group in a decreasing order by their miRNA abundance on the x-axis, independently for each group. C. Proportion of samples for which each of the putative novel miRNA candidates across the different groups were detected. D. Cumulative abundance of the putative novel miRNAs. The dots indicate the log10 of the miRNA abundance for each miRNA. miRNAs are sorted in each group in a decreasing order by their miRNA abundance on the x-axis, independently for each group. CKD: Chronic kidney disease; PN: Pyelonephritis; SB/C: Subclinical bacteriuria/Cystitis; UO: Ureteral obstruction, CPM: Counts per million., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310606
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310606
HANDLE: http://hdl.handle.net/10261/310606
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310606
PMID: http://hdl.handle.net/10261/310606
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310606
Ver en: http://hdl.handle.net/10261/310606
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310606

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310610
Dataset. 2022

PRINCIPAL COMPONENT ANALYSIS (PCA) OF URINE SAMPLES (N = 38) ON THE BASIS OF LOG2 NORMALIZED RELATIVE QUANTITIES (RQ) OF PROFILED MIRNAS USING QPCR

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
1 figure., All samples together (all groups), as well as each one of the contrasts considered (Controls vs. PN; Control vs. SB/C; Control vs. UO; Control vs. CKD; PN vs. SB/C; PN vs. UO; PN vs. CKD and PN vs. other Pathologies) are shown. CKD: Chronic kidney disease; PN: Pyelonephritis; SB/C: Subclinical bacteriuria/Cystitis; UO: Ureteral obstruction., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310610
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310610
HANDLE: http://hdl.handle.net/10261/310610
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310610
PMID: http://hdl.handle.net/10261/310610
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310610
Ver en: http://hdl.handle.net/10261/310610
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310610

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310617
Dataset. 2022

PEARSON CORRELATION ANALYSIS BETWEEN ABUNDANCE PROFILES OF SMALL RNASEQ AND QPCR DATA FROM SELECTED MIRNAS THAT WERE DA (|LOG2FC| ≥ 1.5 FOR QPCR AND ≥ 2 FOR SMALL RNASEQ; Q-VALUE < 0.05) USING BOTH METHODOLOGIES

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
1 figure., CKD: Chronic kidney disease; PN: Pyelonephritis; SB/C: Subclinical bacteriuria/Cystitis; UO: Ureteral obstruction, CPM: Counts per million, Rq: Relative quantities., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310617
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310617
HANDLE: http://hdl.handle.net/10261/310617
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310617
PMID: http://hdl.handle.net/10261/310617
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310617
Ver en: http://hdl.handle.net/10261/310617
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310617

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310621
Dataset. 2022

BLAND-ALTMAN PLOTS OF ABUNDANCE PROFILES OF SMALL RNASEQ AND QPCR

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
The data presented is from selected miRNAs that were DA (|log2FC| ≥ 1.5 for qPCR and |log2FC| ≥ 2 for small RNAseq; q-value < 0.05) using both methodologies. CKD: Chronic kidney disease; PN: Pyelonephritis; SB/C: Subclinical bacteriuria/Cystitis; UO: Ureteral obstruction, CPM: Counts per million, Rq: Relative quantities., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310621
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310621
HANDLE: http://hdl.handle.net/10261/310621
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310621
PMID: http://hdl.handle.net/10261/310621
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310621
Ver en: http://hdl.handle.net/10261/310621
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310621

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310630
Dataset. 2022

MIRNAS SELECTED FOR QPCR VERIFICATION

  • Gòdia, Marta
  • Brogaard, Louise
  • Mármol-Sánchez, Emilio
  • Langhorn, Rebecca
  • Nordang Kieler, Ida
  • Reezigt, Bert Jan
  • Nielsen, Lise Nikolic
  • Jessen, Lisbeth Rem
  • Cirera, Susanna
1 table., The table includes for each miRNA the arguments for its selection for further validation, the forward and reverse sequence, the miRBase sequence used as template for primer design, if successful miRNA amplification was obtained with qPCR and qPCR amplification efficiency., Peer reviewed

Proyecto: //
DOI: http://hdl.handle.net/10261/310630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310630
HANDLE: http://hdl.handle.net/10261/310630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310630
PMID: http://hdl.handle.net/10261/310630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310630
Ver en: http://hdl.handle.net/10261/310630
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/310630

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