cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/anrj1f/mkhubk
title: GAEZAct2015_Yield_Soybean_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/9do3zc
title: GAEZAct2015_Yield_Wheat_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/hojrfu
title: GAEZAct2015_Yield_CropsNES_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/sq1rag
title: GAEZAct2015_Yield_CropsNES_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/mvj5zy
title: GAEZAct2015_Yield_Sunflower_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/0hvwm7
title: GAEZAct2015_Yield_Tobacco_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/ypk6qg
title: GAEZAct2015_Yield_Sorghum_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/ewmg7h
title: GAEZAct2015_Yield_Cassava_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/81ne6f
title: GAEZAct2015_Yield_PotatoAndSweetpotato_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/n8hvcq
title: GAEZAct2015_Yield_Millet_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/nsc8dl
title: GAEZAct2015_Yield_Maize_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/nbfecc
title: GAEZAct2015_Yield_Rice_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/x1sozg
title: GAEZAct2015_Yield_Maize_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/oazu1r
title: GAEZAct2015_Yield_Yamsandotherroots_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/xggjmw
title: GAEZAct2015_Yield_Groundnut_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/2rftit
title: GAEZAct2015_Yield_Sugarbeet_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/aaqvh1
title: GAEZAct2015_Yield_Oilpalmfruit_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/etynfe
title: GAEZAct2015_Yield_Vegetables_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/azgzaz
title: GAEZAct2015_Yield_Wheat_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/95myka
title: GAEZAct2015_Yield_Sunflower_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/jvy803
title: GAEZAct2015_Yield_Olives_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/tbdpbc
title: GAEZAct2015_Yield_Rapeseed_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/2kahfk
title: GAEZAct2015_Yield_Sugarcane_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/sgbygw
title: GAEZAct2015_Yield_Banana_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/twtdlh
title: GAEZAct2015_Yield_Olives_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/yumr1k
title: GAEZAct2015_Yield_Rice_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/6w3ny8
title: GAEZAct2015_Yield_Oilpalmfruit_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/h59pfx
title: GAEZAct2015_Yield_Soybean_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/yslvlb
title: GAEZAct2015_Yield_Foddercrops_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/dyiwkw
title: GAEZAct2015_Yield_Tobacco_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/foiisn
title: GAEZAct2015_Yield_PotatoAndSweetpotato_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/c3vt6z
title: GAEZAct2015_Yield_Foddercrops_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/wui0bm
title: GAEZAct2015_Yield_Yamsandotherroots_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/9owh7e
title: GAEZAct2015_Yield_Pulses_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/nnoleg
title: GAEZAct2015_Yield_Stimulants_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/mlqexd
title: GAEZAct2015_Yield_Othercereals_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/th5w3g
title: GAEZAct2015_Yield_Banana_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/t9oskg
title: GAEZAct2015_Yield_Sugarcane_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/50fuaw
title: GAEZAct2015_Yield_Pulses_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/tgr4aq
title: GAEZAct2015_Yield_Vegetables_Irrigated.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/xdvfkq
title: GAEZAct2015_Yield_Cotton_Rainfed.tif

description: :unav
DOI: 10.7910/dvn/anrj1f/4kagye
title: GAEZ+_2015 metadata.pdf

description: MetadataPDF
DOI: 10.7910/dvn/anrj1f
title: GAEZ+_2015 Crop Yield

description: FAOSTAT yield data for all countries and crops, 2009-2100 and 2014-2016, are calculated from production and area, not directly reported yield values (http://www.fao.org/faostat/en/#data/QA). We computed the 2015 crop yield grids by doing a grid cell by grid cell division of crop production by crop harvested area, for all crops, both irrigated and rainfed. For each crop, m, and grid cell, k, crop yield in 2015 was computed as the ratio of crop production in 2015 to crop harvested area in 2015. Note that for many grid cells and many crops, the GAEZ+_2015 harvested area is zero. In these cases, the GAEZ+_2015 yield is reported as -3.39999999999999996e+38.
DOI: 10.17632/8tx7kjbkg4.1
title: BugHunter Dataset

description: Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc., therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning. We present the BugHunter Dataset: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74.
DOI: 10.17632/mx4hkgsnfn.1
title: SIMUPOR: Benchmark for multi-phase segmentation of porous media tomography images
DOI: 10.17632/c7ynv6x384.1
title: Set of data sheets used in the research: Spatiotemporal patterns and integrated approach to prioritise areas for surveillance and control of visceral leishmaniasis in a large metropolitan area in Brazil.

description: We aimed to analyze the spatial and spatiotemporal patterns of VL occurrence and to identify priority risk areas for surveillance and control in the metropolitan region of Belo Horizonte-MG, Brazil. An ecological study was conducted considering all cases of VL in humans confirmed from 2006 to 2017, reported in Brazil’s national notification database (Notifiable Diseases Information System [SINAN]). We used databases from two different versions of SINAN: Windows version 2006 and TabNet version 2007–2017 (Ministry of Health, 2019). Therefore, we aggregated these two databases encompassing the entire period (2006–2017). Incidence rates were calculated annually and for 3-year periods for all MRBH municipalities. To calculate the incidence rates, each case was aggregated by municipality of residence (analytical unit). The population estimates were set considering projections of the Federal Court of Audit (Tribunal de Contas da União [TCU]) calculated yearly for each municipality, based on data from the IBGE (2018). The incidence rates were aggregated in 3 years, as follows: 1st triennium, 2006–2008; 2nd triennium, 2009–2011; 3rd triennium, 2012–2014; and 4th triennium, 2015–2017. The incidence rates per 100,000 inhabitants were calculated for each triennium. Cumulative incidence rates were re-estimated for the geographic analytical units and for each triennium using empirical Bayesian space smoothing. Calculations were performed using GeoDa version 1.10 software (Arizona State University / Center for Geospatial Analysis and Computation, n.d.). To perform the empirical Bayesian space smoothing analysis, the Moran’s Global Test and the Local Indicators of Spatial Association (LISA) were used to create the first order neighborhood matrix (Queen). The units of analysis that presented at p ≤ 0.05 in the LISA were considered statistically significant. In order to view the priority municipalities for surveillance, we made choropleth maps. The Moran’s Global and LISA were calculated using GeoDa software version 1.10 (Arizona State University / Center for Geospatial Analysis and Computation, n.d.), and maps were constructed using QGIS software version 2.18 (QGIS project, 2019). To identify spatiotemporal clusters, the SaTScan ™ 9.4.4 (Kulldorff, 2015) software scanning statistics was used. Statistical significance was considered when p &lt;0.05 (Kulldorff and Nagarwalla, 1995). The spatial scan statistical analyzes for this study were performed using the case data set, population, and location. To perform the tests, the information regarding each municipality was inserted in the software: 1) number of cases, 2) year of infection, 3) population average of the 3 years that make up the 3-year period studied, and 4) geocode of each municipality. All this information was entered for the entire period.
DOI: 10.17632/c7ynv6x384.2
title: Dataset used in the research: Spatiotemporal patterns and integrated approach to prioritise areas for surveillance and control of visceral leishmaniasis in a large metropolitan area in Brazil.

description: We aimed to analyze the spatial and spatiotemporal patterns of VL occurrence and to identify priority risk areas for surveillance and control in the metropolitan region of Belo Horizonte-MG, Brazil. An ecological study was conducted considering all cases of VL in humans confirmed from 2006 to 2017, reported in Brazil’s national notification database (Notifiable Diseases Information System [SINAN]). We used databases from two different versions of SINAN: Windows version 2006 and TabNet version 2007–2017 (Ministry of Health, 2019). Therefore, we aggregated these two databases encompassing the entire period (2006–2017). Incidence rates were calculated annually and for 3-year periods for all MRBH municipalities. To calculate the incidence rates, each case was aggregated by municipality of residence (analytical unit). The population estimates were set considering projections of the Federal Court of Audit (Tribunal de Contas da União [TCU]) calculated yearly for each municipality, based on data from the IBGE (2018). The incidence rates were aggregated in 3 years, as follows: 1st triennium, 2006–2008; 2nd triennium, 2009–2011; 3rd triennium, 2012–2014; and 4th triennium, 2015–2017. The incidence rates per 100,000 inhabitants were calculated for each triennium. Cumulative incidence rates were re-estimated for the geographic analytical units and for each triennium using empirical Bayesian space smoothing. Calculations were performed using GeoDa version 1.10 software (Arizona State University / Center for Geospatial Analysis and Computation, n.d.). To perform the empirical Bayesian space smoothing analysis, the Moran’s Global Test and the Local Indicators of Spatial Association (LISA) were used to create the first order neighborhood matrix (Queen). The units of analysis that presented at p ≤ 0.05 in the LISA were considered statistically significant. In order to view the priority municipalities for surveillance, we made choropleth maps. The Moran’s Global and LISA were calculated using GeoDa software version 1.10 (Arizona State University / Center for Geospatial Analysis and Computation, n.d.), and maps were constructed using QGIS software version 2.18 (QGIS project, 2019). To identify spatiotemporal clusters, the SaTScan ™ 9.4.4 (Kulldorff, 2015) software scanning statistics was used. Statistical significance was considered when p &lt;0.05 (Kulldorff and Nagarwalla, 1995). The spatial scan statistical analyzes for this study were performed using the case data set, population, and location. To perform the tests, the information regarding each municipality was inserted in the software: 1) number of cases, 2) year of infection, 3) population average of the 3 years that make up the 3-year period studied, and 4) geocode of each municipality. All this information was entered for the entire period.
DOI: 10.14457/tu.the.2010.797
title: การศึกษาความเชื่อมั่นบริการชำระสินค้าผ่านระบบมือถือและวิเคราะห์ความสัมพันธ์ของปัจจัยที่ก่อให้เกิดความเชื่อมั่น
DOI: 10.14457/tu.the.2010.798
title: ปัจจัยที่มีผลต่อการยอมรับไฮโดรเจนเป็นพลังงานทางเลือกสำหรับรถยนต์ในเขตนิคมอุตสาหกรรมเอเซีย จ.ระยอง
DOI: 10.14457/tu.the.2010.799
title: ความเป็นไปได้และโอกาสของการเข้าไปลงทุนในธุรกิจธนาคารพาณิชย์ของไทยในประเทศจีน
DOI: 10.14457/tu.the.2010.800
title: โครงการดำเนินการจัดแสดงนิทรรศการพิพิธภัณฑ์ศิลปะ ยอดชาย เมฆสุวรรณ
DOI: 10.17632/b8fr3c64pp.1
title: Data and Tables Associated with Manuscript "Evidence for Multiple Diagenetic Episodes in Ancient Fluvial-Lacustrine Sedimentary Rocks in Gale Crater, Mars"

description: This dataset includes CheMin diffraction patterns, APXS bulk geochemistry, SAM H2O and SO2 evolved gas analyses, and the derived mineralogy, unit-cell parameters and amorphous chemistry for the Oudam, Marimba, Quela, and Sebina drill samples. These data correspond to the JGR manuscript entitled "Evidence for Multiple Diagenetic Episodes in Ancient Fluvial-Lacustrine Sedimentary Rocks in Gale Crater, Mars"
DOI: 10.14457/tu.the.2010.801
title: โครงการจัดตั้งบริษัทผลิตรายการโทรทัศน์ประเภทสาระความรู้และบันเทิงเชิงสถานการณ์จริง
DOI: 10.14457/tu.the.2010.802
title: การศึกษาความสัมพันธ์และผลกระทบของการใช้บริการโทรศัพท์เคลื่อนที่ต่อการใช้บริการโทรศัพท์ประจำที่ของผู้ใช้บริการในเขตกรุงเทพมหานคร
DOI: 10.14457/tu.the.2010.803
title: การศึกษาความเป็นไปได้ในทางเลือกการลงทุนเปรียบเทียบระหว่างโครงการอาคารชุดเพื่อขายกับโครงการอพาร์ทเม้นต์เพื่อเช่า :
DOI: 10.17632/9xvf3ph6bt.1
title: Landing Signalman gesture recognition dataset

description: These two gesture datasets are from the Computer & Graphics Journal submitted article: "Helicopter visual signaling simulation: Integrating VR and ML into a low-cost solution to optimize Brazilian Navy training". Both files have 17 helicopter visual signals using two hands, and each gesture was performed 25 times. They were created using MiVRy – 3D Gesture Recognition at Unity environment. They contain the raw relative positions of each gesture sample, the neural network itself, and the metadata. File “HVSS_gest_contr.dat” was recorded using VIVE(TM) controllers, for the virtual nighttime environment, and “HVSS_gest_tracker.dat” using VIVETM Tracker wrist-mounted, for the virtual daytime environment. The gestures are illustrated and explained according to "'Landing Signalman dataset.pdf".
DOI: 10.17632/ptspphdtgv.1
title: Bundled intervention to improve patient safety by reducing skin specimen related errors in a tertiary dermatology practice

description: Supplemental Figures: Figure 1: Standardized anatomic sites. Figure 2: Real-time specimen process flow mapping of specimens from Northwest (NW) community clinic to pathology using RFID tracking data.
DOI: 10.17632/jzc4mzr6hb.3
title: Data for geometry-based greenness index comparisons in lettuce biophysical estimations

description: The lettuce images were captured daily using consumer-grade Vivo17 Pro smartphone camera for full six-week crop life cycle. Four lettuces are both cultivated in indoor and outdoor aquaponic setup. The distance of camera to lettuce canopy is 10 inches. The presence of artificial and natural photosynthetic light is present on the images. The raw images have corresponding vegetation channel for triangular greenness index (TGI) and artificial bee colony-optimized visible band oblique dipyramid greenness index (vODGIabc). The vODGIabc channel must exhibit more enhanced vegetation pixels than TGI transformed channel. The data shows that using swarm intelligence based optimization and the introduction of saturation and value component rectification increases the sensitivity of vODGIabc to green color pixels resulting to subtracted non-vegetation pixels in the image even with the presence of shadow and bright light sources. TGI and vODGIabc images are discriminated against the ground truth images annotated using superpixel and watershed transformation. To further the analysis, each ground truth image is accompanied with actual measurement of biophysical signatures of lettuce. The use of computational intelligence for lettuce fresh weight, height, number of spanning leaves, leaf area index and growth stage estimation is of significant consideration. Matlab codes for optimization, and machine learning and deep transfer network estimations are also included.
DOI: 10.17632/jzc4mzr6hb.4
title: Data for geometry-based greenness index comparisons in lettuce biophysical estimations

description: The lettuce images were captured daily using consumer-grade Vivo17 Pro smartphone camera for full six-week crop life cycle. Four lettuces are both cultivated in indoor and outdoor aquaponic setup. The distance of camera to lettuce canopy is 10 inches. The presence of artificial and natural photosynthetic light is present on the images. The raw images have corresponding vegetation channel for triangular greenness index (TGI) and artificial bee colony-optimized visible band oblique dipyramid greenness index (vODGIabc). The vODGIabc channel must exhibit more enhanced vegetation pixels than TGI transformed channel. The data shows that using swarm intelligence based optimization and the introduction of saturation and value component rectification increases the sensitivity of vODGIabc to green color pixels resulting to subtracted non-vegetation pixels in the image even with the presence of shadow and bright light sources. TGI and vODGIabc images are discriminated against the ground truth images annotated using superpixel and watershed transformation. To further the analysis, each ground truth image is accompanied with actual measurement of biophysical signatures of lettuce. The use of computational intelligence for lettuce fresh weight, height, number of spanning leaves, leaf area index and growth stage estimation is of significant consideration. Matlab codes for optimization, and machine learning and deep transfer network estimations are also included.
DOI: 10.17632/mpzjpf7xfk.1
title: Disassociated rhamphotheca of fossil bird Confuciusornis informs reconstruction, stress regime, and developmental patterns of the early beak

description: Finite element models and their inputs and outputs used to create Figure 2 in Miller et al. 2020, "Disassociated rhamphotheca of fossil bird Confuciusornis informs reconstruction, stress regime, and developmental patterns of the early beak". A README text file is included to explain the file types included and methods to recreate our outputs.
DOI: 10.17632/2dg3spxsbh.2
title: Data for: Invasive Phragmites increases blue carbon stock and soil volume in a St. Lawrence estuary marsh

description: Our study of a St. Lawrence Estuary marsh reveals that, compared to the native Spartina patens-dominated vegetation, invasive Phragmites australis makes a greater contribution to soil volume and carbon stock (referred to as blue carbon). Phragmites contributions to soil volume enhances marsh sustainability in the face of sea level rise and its greater contribution to soil carbon helps to reduce the atmospheric concentration of CO2. An invasive genetic strain, introduced from Europe or Asia, has expanded extensively along the St. Lawrence River in the last few decades, but has been little studied on the estuarine portion of the St. Lawrence River. We collected soil cores from Phragmites stand and S. patens-dominated stand in a St. Lawrence Estuary salt marsh near la Pocatiere, Quebec. We measured the bulk density, carbon content, volume and mass of belowground organic matter in 2-cm thick soil layers of 3 cores at each Phragmites site. Only bulk density and carbon content were measured in 5-cm thick soil layers of 3 cores at S. patens site. Results showed that soil in the Phragmites stands held 37-77% more blue carbon than in the S. patens-dominated marsh. Based upon their diameter size, Phragmites rhizomes could be contributing 7.4-10.2 cm to the thickness in the upper 20 cm of soil. We suggest that any management of invasive Phragmites include consideration of its role in increasing blue carbon stocks and marsh resilience along with other ecosystem services. This dataset includes soil carbon and belowground biomass composition data in a St. Lawrence estuary marsh invaded by Phragmites australis. Data were collected in 3 Phragmites sites in 2019 and 1 Spartina patens site in 2014. There are no data in belowgroud biomass composition in S. patens site. Data were collected to estimate the invasive Phragmites contribution to blue carbon stock and soil volume. This dataset contains one Excel file with two sheets ("main data" and "diameter"). These data are from field samples and lab analysis. Dry bulk density is calculated by dividing the dry weight by the initial volume. The organic matter content of the ground samples was measured through loss-on-ignition (LOI) with the equation developed by Craft et al. (1991): % organic carbon = 0.40×%LOI + 0.0025(%LOI)2. As much as possible, rhizomes and roots were identified to species using the key of Niering et al. (1977). We used the color and turgor of rhizomes and roots to discern live from dead. Live material is usually light in color and turgid. Dead tissue is dark and flaccid. Unidentified material (mostly roots) was grouped together as “other”. The volume of each species was determined by placing the entire sample of a species in a graduated cylinder with a known amount of water and measuring the volume of water displaced. The sorted groups of belowground biomass were oven dried at 60°C to constant weight.
DOI: 10.17632/k5fz57zd9z.2
title: Cursive-Text: A Comprehensive Dataset for End-to-End Urdu Text Recognition in Natural Scene Images

description: We present a comprehensive dataset for Urdu text detection and recognition in natural scene images. To develop the dataset, more than 2500 natural scene images were captured with a digital camera and a built-in mobile phone camera. Three separate datasets for isolated Urdu character images, cropped word images and end-to-end text spotting are developed. The isolated Urdu character image dataset contains 19901 images, while the cropped word image dataset contains 14100 cropped words. A lexicon of more than 40K commonly used Urdu words is also created. The ground truths for each of the image in the isolated character, cropped word or text spotting datasets are provided separately. The proposed datasets can be used to perform Urdu text detection, recognition or end-to-end recognition in natural scenes. These datasets can also be helpful to develop Arabic and Persian natural scene text detection and recognition systems, as Urdu is a derived language of these scripts and has many similar letters. The datasets can also be helpful to develop multi-language translation systems.