Find research datasets worth reusing

Data for the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice"

All data related to the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice" will be made available to the scientific public here. The paper will be published soon in Behavioral Research Methods.

Fig. 5 in Asplenium danxiaense sp. nov. (Aspleniaceae, Aspleniineae), a new tetraploid fern species from Guangdong, China, based on morphological and molecular data

Fig. 5. Spores of the new species Asplenium danxiaense K.W.Xu sp. nov. and its affinities. A, B. A. danxiaense K.W.Xu sp. nov. C. A. cornutissimum X.C.Zhang & R.H.Jiang. D. A. coenobiale Hance. E. A. pulcherrimum.(Baker) Ching ex Tardieu.

Fig. 2 in Asplenium danxiaense sp. nov. (Aspleniaceae, Aspleniineae), a new tetraploid fern species from Guangdong, China, based on morphological and molecular data

Fig. 2. The phylogenetic position of Asplenium danxiaense sp. nov. based on nuclear gene pgiC. The numbers associated with branches are maximum likelihood bootstrap (MLBS) values followed by bayesian inference posterior probabilities (PP). * indicates MLBS = 100% or PP=1.

Fig. 4. Asplenium danxiaense K.W in Asplenium danxiaense sp. nov. (Aspleniaceae, Aspleniineae), a new tetraploid fern species from Guangdong, China, based on morphological and molecular data

Fig. 4. Asplenium danxiaense K.W.Xu sp. nov. A. Danxia landform in the type locality of the new species. B. Habitat of the new species in a cave. C. Habit. D. Abaxial view of lamina. E. Abaxial view of lamina apex. F. Adaxial view of lamina. E. Rhizome and root.

Fig. 1 in Asplenium danxiaense sp. nov. (Aspleniaceae, Aspleniineae), a new tetraploid fern species from Guangdong, China, based on morphological and molecular data

Fig. 1. The phylogenetic position of Asplenium danxiaense K.W.Xu sp. nov. based on five plastid markers (atpB, rbcL, rps4-trnS, rpl32-trnP, and trnL-F). The numbers associated with branches are maximum likelihood bootstrap (MLBS) values followed by bayesian inference posterior probabilities (PP). * indicates MLBS = 100% or PP = 1.

Fig. 6 in Asplenium danxiaense sp. nov. (Aspleniaceae, Aspleniineae), a new tetraploid fern species from Guangdong, China, based on morphological and molecular data

Fig. 6. Estimation of Asplenium danxiaense K.W.Xu sp. nov. genome size by flow cytometry. The internal control Zea mays L. cv. B73 has 1C = 2.3Gbp.

Fig. 3 in Asplenium danxiaense sp. nov. (Aspleniaceae, Aspleniineae), a new tetraploid fern species from Guangdong, China, based on morphological and molecular data

Fig. 3. Scale morphology of the new species and its affinities. A, E. Asplenium danxiaenseK.W.Xu sp. nov. B, F. A. pulcherrimum (Baker) Ching ex Tardieu. C, G. A. coenobiale Hance. D, H. A. cornutissimum X.C.Zhang & R.H.Jiang.

opencc-by-4.0Oct 2020View details →

Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a>  The data included: 1) Raster data of commodity maps (TIFF Compressed in ZIP) 2) READ ME for the dataset (DOCX) 3) Legend for raster data in ArcGIS Format (LYR)

Training and Testing Data, Associated Code, and WRF Code for ML-based nonhydrostatic alternative scheme in dynamical core of atmosphere

Data and codes for a nonhydrostatic alternative scheme (NAS) in dynamical core of atmosphere based on machine learning. In this new version, the randomly sampled training data samples testing data samples from nonhydrostatic simulations in WRF baraclinic wave test are provided. They are processed into a new data structure, which can be directly utilized in training and testing.  Follow the instructions in README.txt and download the training and testing data, and the associated codes. Here we provide 3 parts of data and codes: 1, Training and testing data from WRF; 2, Training and testing codes for two machine learning emulators: machine learning and neural network 3, WRF application.

Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis

A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase. Contents: <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul>   Preprint version of the related paper: Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a>

Data for analysis procedure for rectenna-based THz-spectroscopy as published in Lechelon et al., Sci. Adv. 8, eabl5855 (2022)

Example of data for R-PE data processing

Data used for the article "Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data"

Data used to obtain the results presented in the article "Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data". <ul> <li>CNRS_PROMES_DNI_2020-09-03_2021-01-11.zip : contains DNI measurements taken at PROMES-CNRS laboratory in Odeillo.</li> <li>The other zipped files contain image data taken at PROMES-CNRS laboratory in Odeillo. Each zipped file contains all images for one day (the date is given in the file name).</li> </ul> Images and GHI measures from the following days have been used for training and cross-validation: <ol> <li>2020-09-11</li> <li>2020-09-12</li> <li>2020-09-16</li> <li>2020-09-19</li> <li>2020-09-20</li> <li>2020-09-21</li> <li>2020-09-22</li> <li>2020-09-23</li> <li>2020-09-24</li> <li>2020-09-29</li> <li>2020-10-01</li> </ol> Images and GHI measures from the following days have been used for test: <ol> <li>2020-10-04</li> <li>2020-10-05</li> <li>2020-10-08</li> <li>2020-11-05</li> <li>2020-11-15</li> </ol>

European plant-based foods sales data 2017-2020 (Nielsen Market Track)

<ul> <li>The dataset consists of Excel (.xlsx) files with data on sales of plant-based food products between 2017 and 2020 in a number of European countries (i.e. Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Poland, Romania, Spain and the UK.)</li> </ul> <ul> <li>The data are clearly labelled within each file. The key variables (common across datasets) are Value in Euros, Volume in KG/LIT and Volume in Selling Units for a number of meat and dairy substitute food products specific to the retail region.</li> </ul> <ul> <li>The data were originally collected by Nielsen Market Track. They were analysed on the <a href="http://www.smartproteinproject.eu">Smart Protein project</a> in 2021 and used to publish an extensive <a href="https://smartproteinproject.eu/plant-based-food-sector-report/">market data report</a> and to host a <a href="https://www.youtube.com/watch?v=dsIJqvpXXgw">public webinar</a>, both entitled Plant-based foods in Europe: how big is the market?</li> </ul>

Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data

UAV images used for SfM model generation and all images (both SI and OM), in which we measured grain sizes. The code used for image processing and uncertainty estimation of grain size distributions as python files and executable jupyter notebooks, where the latter also serve as documentation.

A hierarchical graph-based model for mobility data representation and analysis

Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.

Data - Coherent combining of low-power optical signals based on optically amplified error feedback

This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached. This work was funded by the Swedish Research Council (grant VR-2015-00535).

opencc-by-4.0May 2019View details →

Fig 5 in Taxonomic Position Of Anastrangalia Reyi And A. Sequensi (Coleoptera, Cerambycidae) Based On Molecular And Morphological Data

Fig 5. Male aedeagi (A–D) and parameres (E–H) of A. reyi (A, E), A. sequensi (B, F), A. dubia (C, G) and A. sanguinolenta (D, H).

opencc-by-4.0May 2019View details →

Fig. 1 in Taxonomic Position Of Anastrangalia Reyi And A. Sequensi (Coleoptera, Cerambycidae) Based On Molecular And Morphological Data

Fig. 1. Distribution of nucleotide substitutions within haplogroups: A — A. reyi (ArEu) (KJ964792); B — A. sequensi (AsFe) (KY683642); C — A. dubia (AdAl) (KM439943); D — A. dubia (AdPy) (KM285974). The unic nucleotide substitutions are red circled; the common substitutions are blue marked.

opencc-by-4.0May 2019View details →

Fig. 3 in Taxonomic Position Of Anastrangalia Reyi And A. Sequensi (Coleoptera, Cerambycidae) Based On Molecular And Morphological Data

Fig. 3. Detailed phylogenetic subtree for the dubia group (hybrids of A. reyi and A. dubia are indicated by arrows).