Extending the Ability of Near-Infrared Images to Monitor Small River Discharge on the Northeastern Tibetan Plateau (2018WR023808)
Contributors: li haojie, Hongyi Li
... Data accessing (2018WR023808) Note: All data used in this study are provided in this repository, including daily gauge discharge data, drawing data and calculations code. Please refer to this paper for details on how to use the data. 1. The all figures of this paper are provided. The vector data used in Figure 1 and Figure 4 are uploaded to separate folders (shp). 2. The data for drawing in this study are shared in data attachment file (excel). 3. Gauged discharge data The daily gauge discharge data used in this study are shared in the data attachment file (excel). 4. Landsat images and code The Landsat image sets and calculation code used in this study are available on the Google Earth Engine platform. The links of image sets and sample code as below. TM https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1 ETM https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1 OLI https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC8_L1T Calculation code https://code.earthengine.google.com/2254187574dedb2c0b2e9a7fc1832eb2
UAV imagery and in-situ measurements for structure-from-motion snow depth mapping over the Laurichard rock glacier, France - surveyed in 2017
Contributors: Jason Goetz, Marco Marcer, Alexander Brenning, Xavier Bodin
... Unmanned aerial veichle (UAV) imagery and in-situ field measurements at the Combe de Laurichard, France (45.01ºN, 6.37ºE, 2500 m a.s.l.) were collected to explore uncertainties in mapping snow depth with structure-from-motion and multi-view stereo 3D reconstruction in an alpine area. Repeat UAV surveys were flown on each survey date to create multiple elevation models to determine the precision (i.e., repeatability) of SFM-MVS elevation models. This measure of uncertainty can be used to determine the precision of SFM-MVS snow depths using a model of error propagation. It also can illustrate how uncertainty in the SFM-MVS snow depths and elevation models vary spatially. The repeated snow-cover (snow-on) elevation models (6 in total) were acquired on June 1, 2017, and the snow-free (snow-off) elevation models (7 in total) on October 5, 2017. These elevation models were derived by performing SFM-MVS reconstruction using Agisoft PhotoScan. The UAV imagery was surveyed using a DJI Phantom 4, which flew in pre-programmed parallel flight paths with 75% side and top image overlap. The flying height of the UAV was approximately 60 m above ground level. Artificial targets were used for SFM camera calibration and georeferencing using the RGF93 / Lambert-93 projection and the NGF-IGN69 vertical datum (EPSG::5698). Validation data was collected by measuring topographic heights (i.e., check points) using a real-time-kinematic (RTK) global navigation satellite system (GNSS) survey with an accuracy < 2 cm (at 1 σ). At each check point location, snow depths were measured using an avalanche probe to a maximum 3 m depth. This RTK-GNSS survey was also used for collecting ground control points (GCPs). The position of the base-station was corrected using the PUYA reference station, which is located approximately 19 km from the study area. Additionally, to compare the uncertainty of SFM-MVS snow depths in stable and active deforming terrain (i.e., rock glacier creep), a mask of the rock glacier area was mapped using the UAV derived imagery and elevation models.
Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants
Contributors: Colin K. Khoury, Daniel Amariles, Jonatan Soto, Maria Victoria Diaz, Steven Sotelo, Chrystian C. Sosa, Julian Ramírez-Villegas , Harold Achicanoy, Nora P. Castañeda-Álvarez , Blanca León
... The datasets presented here are related to the research article entitled “Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets” (Khoury et al., 2019). The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes. Khoury CK, Amariles D, Soto JS, Diaz MV, Sotelo S, Sosa CC, Ramírez-Villegas J, Achicanoy HA, Velásquez-Tibatá J, Guarino L, León B, Navarro-Racines C, Castañeda-Álvarez NP, Dempewolf H, Wiersema JH, and Jarvis A (2019) Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets. Ecological Indicators 98: 420-429. doi: 10.1016/j.ecolind.2018.11.016. Available online at: https://doi.org/10.1016/j.ecolind.2018.11.016 Khoury CK, Amariles D, Soto JS, Diaz MV, Sotelo S, Sosa CC, Ramírez-Villegas J, Achicanoy HA, Castañeda-Álvarez NP, León B, and Wiersema JH (2019) Data for the calculation of an indicator of the comprehensiveness of conservation of useful wild plants. Data in Brief. doi: 10.1016/j.dib.2018.11.125.
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Data for: Classification methods for point clouds in rock slope monitoring: a novel machine learning approach and comparative analysis
Contributors: Luke Weidner, Gabriel Walton, Ryan Kromer
... Data for the following submission: Title: Classification methods for point clouds in rock slope monitoring: a novel machine learning approach and comparative analysis Weidner, L.1*, Walton, G.1, Kromer, R.1 1Colorado School of Mines, Golden, USA *Corresponding author email: email@example.com In the event the manuscript is unavailable, please reach out to us for a copy. The main contents of this file are as follows: -Supplementary figures referenced in the manuscript -All processed point clouds used in the through-time analysis. (~9.3 GB) -Scripts used to calculate the results shown in Figures 11, 12 and 13. (~1.6 GB) -Numeric data in other tables, graphs, and figures. Due to the nature of the research, many large point clouds are created, too many to be all uploaded to this repository. If you are looking for data that is not provided in this dataset, please reach out to the authors and we would be happy to provide any additional data. Scripts labeled "RUNME" are found in the main file directory for creating the ML method results ('tests_RUNME.m'), and for hybrid and masking results. For the most part, scripts can be run without modification and should provide results (assuming the required MATLAB toolboxes are installed) Note that for hybrid and masking, multiple runs of the script are required, changing the filenames at the beginning of the script for each of the four dates calculated. The Random Forest TreeBagger object ('tb_t14_jun16dec18') is also included and all the feature sets used for training and validation ('date_struct.mat').
Imaging mass cytometry reveals tumor and immune spatial and phenotypic clusters associated with clinical outcomes in diffuse large B cell lymphoma (DLBCL)
Contributors: Monirath Hav, Anthony Colombo, Akil Merchant
... DLBCL IMC Analysis raw data, algorithm and codes
Contributors: Joshua Soderholm
... The following collection is used to demonstrate the HailPixel survey technique as part of an AGU GRL publication. The following is an abstract for this paper: A new technique, named "HailPixel," is introduced for measuring the maximum dimension and intermediate dimension of hailstones from aerial imagery. The photogrammetry procedure applies a convolutional neural network for robust detection of hailstones against complex backgrounds and an edge detection method for measuring the shape of identified hailstones. This semi-automated technique is capable of measuring many thousands of hailstones within a single survey, which is several orders of magnitude larger than population sizes from existing sensors (e.g., a hail pad). Comparison with a co-located hail pad for an Argentinan hailstorm event demonstrates the larger population size of the HailPixel survey significantly improves the shape and tails of the observed hail size distribution. When hailfall is sparse, such as during large and giant hail events, the large survey area of this technique is especially advantageous for resolving the hail size distribution. The dataset contains the DEM and orthomosaic imagery, processing reports, final location of hail centroids, final measurements of hail major and minor axis, subset offsets and hail pad data. For more information applying the subset offsets to calculate the true position of the hail centroids please see the paper.
Allometric patterns of mirixis (Byrsonima spp.) in Roraima savanna area, northern Brazilian Amazonia
Contributors: Rodrigo Leonardo Costa de Oliveira, Reinaldo Imbrozio Barbosa , Hugo Leonardo Sousa Farias, Williamar Rodrigues Silva
... This dataset presents BDH and height values of Byrsonima spp. individuals in an area of savanna in Roraima, northern Brazilian Amazonia. These values were collected in four plots (each 0.25ha) installed in Darora Indigenous Community (3°10'42"N and 60°23'34"W), São Marcos Indigenous Land. The fieldwork was carried out in 2014, where 268 individuals of Byrsonima crassifolia and 163 of Byrsonima coccolobifolia were mensured. The data set is presented in one file (allometry_mirixis.xlsx) containing the sampling date, vegetation type, plots, subplots, geographic location of the plots (lat / long WGS 84), species names) and DBH and Height values. This dataset is a part of project “Uso e conservação dos recursos vegetais de comunidades indígenas no norte de Roraima” authorized by FUNAI: Proccess 08620.002869/ 2014-15; IPHAN: Processo 01450.001678/ 2014-88 and CEP-INPA/ CONEP: Parecer 814.370.
Data for: Proteomic analysis of therapeutic effects of Qingyi pellet on rodent severe acute pancreatitis-associated lung injury
Contributors: Hailong Chen, Jialin Qu, Lei Li, Hailong Li, Zhongwei Sun
... It's for the paper titled “Proteomic analysis of therapeutic effects of Qingyi pellet on rodent severe acute pancreatitis-associated lung injury”.
Port Data Penelitian Personalisasi Konten E-learning Berdasarkan Felder-Silverman Learning Style Model
Contributors: Jeremiah Hasudungan Sihombing
... Port data berikut berisi keseluruhan Data, Dokumen , Produk, dan Perangkat Lunak yang dihasilkan dari penelitian ini. Mohon untuk digunakan sebagai mana mestinya. -JHS-2019
Contributors: Anders Thomsen, Morten Kristiansen, Ewa Kristiansen, Benny Endelt
... The data describes the measurement of a v-bend shape formed during multi-scan laser forming. The purpose of the measurements was to determine the dynamic response during laser forming of a v-bend. A measurement scanner was used to measure the height of a line perpendicular to the heating scan line of a laser during laser forming. In order to estimate a surface, 105 samples were made with identical settings with the measurement scanner moved along the heating scan line between samples. A total of 21 positions along the heating scan line were measured. Each position was measured using 5 samples. Due to a memory problem, the measurement scanner could only measure about 3.12 seconds at a time. The measurement scanner is started slightly before each heating scan line starts. Furthermore, each heating scan line is split into its own text file in the data set. This data set contains 21 folders, one for each position of the measurement scanner along the heating scan line. The folders are named as 'ymm', where y is the distance from the trailing edge of the heating scan line, '_' is used instead of decimals here. Each folder contains 30 text files, 6 for each of the samples used, structured as (x, y, z, t). Each file is named as 'sample_i_plate_j_scannumber_k.txt', where i is the sample number (1-5) at this position, j is the plate number (1 or 2), k is the scan number (1-6). Scan number 6 does not contain any heating, but is set as a final measurement of about 60 seconds after forming. Warning: The unzipped data fill 51.6 GB