The dataset includes sociodemographic, group assignment, and participation variables as well as outcome measures (total and subscale scores) for participants at each assessment point: 1. EF (enrolment) 2. T0 (baseline) 3. T1 post-intervention) 4. T2 (follow-up at 6 months/4 months post-intervention)
Data from a clinical study of two interventions including ice cap cooling and remote ischemic conditioning on stroke patients including GOSE and Marshall CT scan and Apache 2 and SOD and TTN F-alpha and interleukin 10 in four periods including admission time, 72 hours, 6 days and 28 days after admission.
This algorithm is associated with the paper entitled "A class of nonlinear observers with controllable covariance" by Shakouri et al. that is currently under review. This algorithm comprises a number of recursive steps to construct a controllability matrix $R_k$ for the covariance elements as well as state variables of an LTI system with a linear measurement model when the proposed state-dependent Luenberger-like observer is used.
This dataset includes the following MATLAB functions:
1- Symmetric Kronecker product between two matrices A and B: C=skron(A,B).
2- Symmetric vectorization operator of a symmetric matrix S: s=svec(S).
3- Symmetric matricization operator of a n(n+3)/2-dimensional vector v: S=smat(s).
4- The commutation matrix: Kgen(n,m).
5- The Q matrix needed for symmetric operations: Qgen(n).
Note: The codes of function "Qgen.m" are derived from the link mentioned in the references that is provided for public use in a comment by David Goodmanson on 17 Oct 2017.
Climate change has a significant impact on seasonal snow cover. However, obtaining robust data on snow cover remains a challenge. There is a significant lack of ground-based data for verification of remote and model data. Observation network in Siberia is quite rare, and the location of the snow stations does not always represent the characteristics of the territory. We aimed to extend the observation coverage of climate stations and to assess variability in different ecosystems. We focused on the representation of different ecosystem types in the southern West Siberian Plain and Altai low mountain area.
We carried out our research in two catchments - Kasmala and Maima, located in the forest-steppe and lowland areas. The observations were conducted during the peak snow accumulation (late February - early March). In the Kasmala catchment, the observations were conducted in 2011-2014 and 2017-2019, in the Mayma catchment from 2015 to 2019. These works were funded by state projects of the Institute for Water and Environmental Problems SB RAS. In 2019, a joint 3S (South Siberian Snowpack) project funded by RFBR (N 19-35-60006, 2019-2022) was launched at Lomonosov Moscow State University. As part of this project, we expanded the observation network and conducted observations during the whole winter season 2019-2020 in three catchments: Kuchuk (steppe), Kasmala (forest-steppe), and Mayma (low mountains). Also, the 3S project merged existing data into a single dataset on snow properties (depth, density, SWE).
Observations till 2019 were carried out on snow courses and small snow sites. Courses were 500 m to 2 km long. Depth measurements were made every 20 m, density measurements every 100/200 m. The snow sites were two perpendicular transects of 50 or 20 meters long, including 20 depth and 5 density measurements. In the 3S project, we changed the observation scheme (data 2019-2020). All observations were made at the snow sites, which included 61 depth and 13 density measurements. The sampling scheme was proposed by Jost et al., 2007. In total, in the Kasmala catchment, we carried out about 600 depth/70 density measurements, in the Mayma catchment about 800 depth/200 density measurements. Within the 3S project, we carried out 8781 depth and 1873 density measurements during the winter season. We highly recommend aggregating the data by courses, sites or catchments (do not use individual values).
The presented data article aims to provide the whole dataset obtained during an experiment of updating laser scan point clouds with photogrammetry meshes. In this context, the data quality and calculation time of photogrammetry models from different recording devices and different software solutions were compared. It was investigated whether photos from smartphones are also appropriate for updating point clouds by using photogrammetry in a factory environment. The photos of a technical installation were taken in 08:30 min with these three devices: Nikon D810 with Sigma art 24mm, iPhone 6 and iPhone XS. With each of the mentioned devices, three datasets have been created to provide enough data for the comparisons. One dataset (photos in .TIFF) of the iPhone XS is provided. The results of the data sets are used for a photogrammetry mesh quality comparison and a calculation time comparison. For the mesh quality comparison, visual qualitative inspections were performed on the models and the results were compared. Furthermore, all settings in the RealityCapture BETA 18.104.22.16896 ppi and Meshroom 2019 2.0 software are provided. A comparison of the quality of the photogrammetric 3D meshes was performed by comparing the rendering results. The dataset of the iPhone XS can be used to compare further photogrammetry software or single algorithms. Besides the images, the initial point cloud of the laser scanner is provided. Also included is the combined file which consists of the laser scan point cloud and the photogrammetry mesh of the end of the experiment.
Contributors:le Roux Elizabeth, van Veenhuisen Laura, Kerley Graham, Cromsigt Joris
This data accompanies the following manuscript: le Roux et al 2020 PNAS Animal body size distribution influences the ratios of nutrients supplied to plants
All relevant information is contained within this manuscript