cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.20350/digitalcsic/6782
title: SPEIbase v.2.3

description: 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.
description: 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).
description: 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 v2.2 (http://hdl.handle.net/10261/72264).-- What’s new in version 2.3: 1) Data has been extended to the period 1901-2013 (it was 1901-2011 in v 2.0), based on the CRU TS3.22 dataset.-- For more details on the SPEI visit http://sac.csic.es/spei.
DOI: 10.20350/digitalcsic/5577
title: SPEIbase v.2.4

description: 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).
description: 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 v2.3 (http://hdl.handle.net/10261/104742).-- What’s new in version 2.4: 1) Data has been extended to the period 1901-2014 (it was 1901-2013 in v 2.3), based on the CRU TS3.23 dataset.-- For more details on the SPEI visit http://sac.csic.es/spei.
DOI: 10.20350/digitalcsic/8537
title: Egg colouration predicts brood size, telomere length and body condition of spotless starling fledglings [Dataset]

description: En la tabla se indican características de los nidos y pollos de estornino negro (Sturnus unicolor) utilizados en el artículo: Juan J. Soler, Cristina Ruiz-Castellano, Jordi Figuerola, Josué Martínez-de la Puente, Magdalena Ruiz-Rodríguez and Gustavo Tomás (2018) Egg colouration predicts brood size, telomere length and body condition of spotless starling fledglings. Journal of Avian Biology, e01686. doi: 10.1111/jav.01686 Nest identity: identidad de los nidos utilizados . feather treatment: si se añadieron plumas a los nidos durante el crecimiento de los pollos o no. plant treatment: si se añadieron plantas verdes a los nidos durante el crecimiento de los pollos o no. Brightness: Brillo (luminosidad) de los huevos de estorninos de los distintos nidos. Blue-Green chroma (400-580): Croma verde-azulado, reflectancia en longitudes de onda. comprendidas entre 400 y 580 nm dividida por la reflectanca de todo el espectro (300-700nm) en tanto por ciento. hathing date (1=1st april): fecha de eclosion (el valor 1 corresponde al 1 de abril). Clutch size: tamaño de puesta. Brood size: tamaño de pollada. Body mass: peso corporal de pollos de 14 dias de edad. Tarsus length: longitude del tarso de pollos de 14 días de edad. Body condition: condicion corporal de pollos de 14 días de edad estimada como los residuos del peso después de controlar por la longitud del tarso. Telomere length (hatchlings): longitud de los telómeros de pollos recién eclosionados. Telomere length (fledglings): longitude de los telómeros de pollos poco antes de abandonar el nido.
description: Para la recogida de los datos se ha seguido la reproducción de dos poblaciones de estornino negro. Además de anotar información sobre distintas variables reproductoras (fecha de puesta, taño de puesta y de pollada,, y éxito reproductor, se ha medido el color de sus huevos, se han tomado muestras de sangre de pollos recién eclosionados y poco antes de abandonar el nido. También se midió el tarso y el peso corporal de los pollos poco antes de abandonar el nido. La estima del tamaño de los telomeros se realizó en el laboratorio, a partir de ADN de los glóbulos rojos de las muestras de sangre recogidas en el campo.
DOI: 10.20350/digitalcsic/3738
title: A 1970-2010 archive of climate surfaces for the Iberian Peninsula

description: This dataset will be permanently stored in Digital.CSIC. This DataSet is in Idrisi Raster Format and in latlong projection.
description: This dataset is an archive of gridded surfaces of mean maximum, mean and mean minimum temperature, and precipitation, for the Iberian Peninsula. Temporal resolution is of 1 month, and spatial resolution is of 0.0089999994 degrees. The study area ranges from 43.8969994ºN through 35.5000000ºN, and from 9.6999998ºW through 3.5029993ºE. It covers continental Spain and Portugal. There is a total of 1920 grids (40 years x 12 months x 4 variables). The surfaces were interpolated using ANUSPLIN on georeferenced climate records drawn from the Spanish State Agency of Meteorology and the Portuguese Water Resources Information System. Variable networks of input stations were used to interpolate the surfaces, averaging 390 and 1877 points for temperature and precipitation respectively. A fixed configuration was used for all the interpolations: trivariate quadratic spline model with longitude, latitude and elevation as independent variables. A square root transformation was appied to precipitation fields to reduce skewness. The software was made to minimize the Generalized Cross Validation (GCV) error. The signal to data point ratio was below 50% for temperature surfaces, with many values between 25% and 35%. This suggests limitations in the number of input data points which may have resulted in a loss of detail on the interpolated surfaces. This is not necessarily bad in terms of broad spatial patterns, but it does indicate that absolute predictions should be used with caution for fine microclimatic patterns. Root generalized cross validation was usually lower than 1ºC for temperature, reaching up to 0.85ºC for summer months of mean maximum temperature. Absolute errors in precipitation did range from 4.41 mm in July to 12.82 mm in December, but the seasonal pattern of prediction errors was inverse and corresponded to 24% and 13% respectively.
DOI: 10.5281/zenodo.3885255
title: Dataset related to article: Intensity modulated proton therapy compared to volumetric modulated arc therapy in the irradiation of young female patients with hodgkin's lymphoma. Assessment of risk of toxicity and secondary cancer induction

description: This record contains data related to article: Intensity modulated proton therapy compared to volumetric modulated arc therapy in the irradiation of young female patients with hodgkin's lymphoma. Assessment of risk of toxicity and secondary cancer induction Abstract <strong>Background: </strong> To investigate the role of intensity modulated proton therapy (IMPT) compared to volumetric modulated arc therapy (VMAT) for advanced supradiaphragmatic Hodgkin's lymphoma (HL) in young female patients by assessing dosimetric features and modelling the risk of treatment related complications and radiation-induced secondary malignancies. <strong>Methods: </strong> A group of 20 cases (planned according to the involved-site approach) were retrospectively investigated in a comparative planning study. Intensity modulated proton plans (IMPT) were compared to VMAT RapidArc plans (RA). Estimates of toxicity were derived from normal tissue complication probability (NTCP) calculations with either the Lyman or the Poisson models for a number of endpoints. Estimates of the risk of secondary cancer induction were determined for lungs, breasts, esophagus and thyroid. A simple model-based selection strategy was considered as a feasibility proof for the individualized selection of patients suitable for proton therapy. <strong>Results: </strong> IMPT and VMAT plans resulted equivalent in terms of target dose distributions, both were capable to ensure high coverage and homogeneity. In terms of conformality, IMPT resulted ~ 10% better than RA plans. Concerning organs at risk, IMPT data presented a systematic improvement (highly significant) over RA for all organs, particularly in the dose range up to 20Gy. This lead to a composite average reduction of NTCP of 2.90 ± 2.24 and a reduction of 0.26 ± 0.22 in the relative risk of cardiac failures. The excess absolute risk per 10,000 patients-years of secondary cancer induction was reduced, with IMPT, of 9.1 ± 3.2, 7.2 ± 3.7 for breast and lung compared to RA. The gain in EAR for thyroid and esophagus was lower than 1. Depending on the arbitrary thresholds applied, the selection rate for proton treatment would have ranged from 5 to 75%. <strong>Conclusion: </strong> In relation to young female patients with advanced supradiaphragmatic HL, IMPT can in general offer improved dose-volume sparing of organs at risk leading to an anticipated lower risk of early or late treatment related toxicities. This would reflect also in significantly lower risk of secondary malignancies induction compared to advanced photon based techniques. Depending on the selection thresholds and with all the limits of a non-validated and very basic model, it can be anticipated that a significant fraction of patients might be suitable for proton treatments if all the risk factors would be accounted for.
DOI: 10.34848/fk2/0oepjh/nvgp8f
title: ecuacion_scopus.txt
DOI: 10.34848/fk2/0oepjh/vsxdvh
title: diccionario.xlsx
DOI: 10.34848/fk2/yg6das/oocj1g
title: venezuela.gml
DOI: 10.34848/fk2/yg6das/gjaj8r
title: dominican republic.gml
DOI: 10.34848/fk2/yg6das/uwmc7x
title: nicaragua.gml
DOI: 10.34848/fk2/yg6das/8nrgob
title: costa rica.gml
DOI: 10.34848/fk2/yg6das/ujm6mg
title: bolivia.gml
DOI: 10.34848/fk2/yg6das/5odm9f
title: chile.gml
DOI: 10.34848/fk2/yg6das/tnkmlw
title: argentina.gml
DOI: 10.34848/fk2/yg6das/4ju4zc
title: panama.gml
DOI: 10.34848/fk2/yg6das/jkpjp7
title: cuba.gml
DOI: 10.34848/fk2/yg6das/onj57a
title: guatemala.gml
DOI: 10.34848/fk2/yg6das/a5qsdu
title: latam.gml
DOI: 10.34848/fk2/yg6das/aehaur
title: ecuador.gml
DOI: 10.34848/fk2/yg6das/lgkyjr
title: puerto rico.gml
DOI: 10.34848/fk2/yg6das/5tyv61
title: uruguay.gml
DOI: 10.34848/fk2/yg6das/b9czdf
title: peru.gml
DOI: 10.34848/fk2/yg6das/rv1ist
title: honduras.gml
DOI: 10.34848/fk2/yg6das/iacxnr
title: colombia.gml
DOI: 10.34848/fk2/yg6das/wgnnm6
title: mexico.gml
DOI: 10.34848/fk2/yg6das/mxg1bf
title: brasil.gml
DOI: 10.5281/zenodo.3898287
title: Base de datos titanic.csv

description: Base de datos utilizada como entrada para la visualización del descubrimiento de subgrupos
DOI: 10.5281/zenodo.3898286
title: Base de datos titanic.csv

description: Base de datos utilizada como entrada para la visualización del descubrimiento de subgrupos
DOI: 10.18112/openneuro.ds002896.v1.0.0
title: Resting State fMRI study of non bothersome tinnitus
DOI: 10.5281/zenodo.3898303
title: KDD Cup Dataset (without Missing Values)

description: This dataset was used in the KDD Cup 2018 forecasting competition. It contains long hourly time series representing the air quality levels in 59 stations in 2 cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 to 31/03/2018. The air quality level is represented in multiple measurements such as PM2.5, PM10, NO2, CO, O3 and SO2. The dataset uploaded here contains 270 hourly time series which have been categorized using city, station name and air quality measurement. The original dataset contains missing values. The leading missing values of a given series were replaced by zeros and the remaining missing values were replaced by carrying forward the corresponding last observations (LOCF method).
description: {"references": ["Kdd cup 2018. URL https://www.kdd.org/kdd2018/kdd-cup"]}
DOI: 10.5281/zenodo.3898318
title: KDD Cup Dataset (with Missing Values)

description: This dataset was used in the KDD Cup 2018 forecasting competition. It contains long hourly time series representing the air quality levels in 59 stations in 2 cities: Beijing (35 stations) and London (24 stations) from 01/01/2017 to 31/03/2018. The air quality level is represented in multiple measurements such as PM2.5, PM10, NO2, CO, O3 and SO2. The dataset uploaded here contains 270 hourly time series which have been categorized using city, station name and air quality measurement.
description: {"references": ["Kdd cup 2018. URL https://www.kdd.org/kdd2018/kdd-cup"]}
DOI: 10.7910/dvn/9l2llq/x8l6gt
title: Final_Main_YI_042520.tab

description: Main data file
DOI: 10.7910/dvn/9l2llq/kwrbgs
title: Final_Supp_YI_042520.tab

description: Supplementary data file for the additional analysis (in the Appendix)
DOI: 10.7910/dvn/9l2llq/yigm2a
title: Replication.do

description: :unav
DOI: 10.7910/dvn/9l2llq/kskznr
title: fin_vdem_ccode_covariates_lagged_after1960.tab

description: :unav
DOI: 10.7910/dvn/9l2llq/jiqhbd
title: fin_original.tab

description: Source: Carnegie and Marinov (2017)
DOI: 10.7910/dvn/9l2llq/imyiij
title: fin_populationtrade_lagged.tab

description: Source: Dreher, Fuchs, and Langlotz (2019)
DOI: 10.18112/openneuro.ds002894.v1.0.1
title: simpcomp
DOI: 10.5281/zenodo.3898315
title: Auto-segmentation DICOM

description: DICOM files for e2e testing of auto-segmentation
DOI: 10.5281/zenodo.3898338
title: Kaggle Wikipedia Web Traffic Weekly Dataset

description: This is the aggregated version of the daily dataset used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-05, after aggregating them into weekly. The original dataset contains missing values. They have been simply replaced by zeros before aggregation.
description: {"references": ["Google, 2017. Web traffic time series forecasting. URL https://www.kaggle.com/c/web-traffic-time-series-forecasting"]}
DOI: 10.3886/e113292
title: Replication data for: Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially

description: This paper experimentally examines image motivation--the desire to be liked and well regarded by others--as a driver in prosocial behavior (doing good), and asks whether extrinsic monetary incentives (doing well) have a detrimental effect on prosocial behavior due to crowding out of image motivation. Using the unique property of image motivation--its dependency on visibility--we show that image is indeed an important part of the motivation to behave prosocially, and that extrinsic incentives crowd out image motivation. Therefore, monetary incentives are more likely to be counterproductive for public prosocial activities than for private ones. (JEL D64, L31, Z13)
DOI: 10.5281/zenodo.3898350
title: Melbourne Pedestrian Counts Dataset

description: This dataset contains hourly pedestrian counts captured from 66 sensors in Melbourne city starting from May 2009. The original dataset is regularly updated when a new set of observations become available. The dataset uploaded here contains pedestrian counts up to 2020-04-30.
description: {"references": ["City of Melbourne, 2017. Pedestrian counting system \u2013 2009 to present (counts per hour). URL https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-2009-to-Present-counts-/b2ak-trbp"]}
DOI: 10.5281/zenodo.3898355
title: M4 Hourly Dataset

description: This dataset contains 414 hourly time series used in the M4 forecasting competition.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74."]}
DOI: 10.21227/s5dt-7s72
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(190+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3878488
title: Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology

description: Cantemist shared task train and development sets. It contains the train and development sets of the three subtasks: cantemist-ner, cantemist-norm and cantemist-coding. For subtasks cantemist-norm and cantemist-ner, annotations are distributed in Brat format. See Brat webpage for more information For subtask cantemist-coding, codes are grouped in a TSV file with the following columns (this follows the format used in CodiEsp shared task): filename code In the three subtasks, the goal will be to predict the annotations (either the ANN files or the TSV with the codes) given only the plain text files. For further information, please visit https://temu.bsc.es/cantemist/ or email us at encargo-pln-life@bsc.es
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.15161/oar.it/30437
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.3898361
title: M4 Daily Dataset

description: This dataset contains 4227 daily time series used in the M4 forecasting competition.
description: {"references": ["Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2018. The m4 competition: results, findings, conclusion and way forward, International Journal of Forecasting 34 (4), 802 \u2013 808.", "Makridakis, S., Spiliotis, E., Assimakopoulos, V., 2020. The m4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 36 (1), 54 \u2013 74."]}