Find research datasets worth reusing

Xenopus tissue data for testing segmentation models

<pre>This dataset is of xenopus tissue imaged with the following settings and it comes with a trained UNET model for performing the segmentation of such tissues. In order to use the segmentation model please install the vollseg-napari plugin from the napari hub and the model will be automatically downloaded for usage. Dataset was acquired by Mari Tolonen and Jakub Sedzinski, (0000-0002-4395-9022,0000-0002-1788-0329) at the university of Copenhagen and the model was trained by Varun Kapoor at Kapoorlabs. A Z projection of 21 Z slices acquired by the ImageJ Z Projection plugin was performed on the original acquired data. ObjectiveSettings ID="Objective:0" Medium="Water" RefractiveIndex="1.333"</pre> <pre>LensNA="1.2000000000000002" Model="C-Apochromat 40x/1.2 W AutoCorr M27" NominalMagnification="40.0"</pre> <pre>Physical Size X="0.6918881841365326" Physical Size X Unit="µm" </pre> <pre>Physical Size Y="0.6918881841365326" Physical Size Y Unit="µm" </pre> <pre>Physical Size Z="2.0" Physical Size Z Unit="µm"</pre> <pre>Time interval frames 1-160: 182 sec Time interval frames 161-262: 283 sec</pre> <pre>SignificantBits="8" Type="uint8"></pre> <pre>Channel AcquisitionMode="LaserScanningConfocalMicroscopy" ExcitationWavelength="488.0" ExcitationWavelengthUnit="nm" Fluor="EGFP"</pre>

NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"

NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System. The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis. The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.

Supporting data files for "A consistent picture of phosphate-divalent cations binding from models with implicit and explicit electronic polarization"

Additional supporting data for the paper "A consistent picture of phosphate-divalent cations binding from models with implicit and explicit electronic polarization". Includes parameter files, as well as typical input files and analysis scripts to reproduce the simulations.

Two global ensemble M5.95+ seismicity models obtained from the combination of interseismic strain rates and earthquake-catalogue data

Contains two global earthquake-rate forecasts developed by Bayona et al. (2021) to be prospectively evaluated by the Collaboratory for the Study of Earthquake Predictability (CSEP). The Tectonic Earthquake Activity Model (TEAM) is a geodetic-based model using Version 2.1 of the Global Strain Rate Map (GSRM2.1; Kreemer et al., 2014), while the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) is a model obtained from a multiplicative log-linear combination of TEAM with the Smoothed Seismicity (KJSS) model of Kagan and Jackson (2011). Earthquake densities are expressed as number of M5.95+ events per unit 0.1o cell per year. The forecasts are stored in tab separated value files, with the following fields (the first row of data is shown as an example): <table> <tbody> <tr> <td>lon_min</td> <td>lon_max</td> <td>lat_min</td> <td>lat_max</td> <td>depth_min</td> <td>depth_max</td> <td>5.95</td> <td>6.05</td> <td>...</td> </tr> <tr> <td>-180.0</td> <td>-179.9</td> <td>-90.0</td> <td>-89.9</td> <td>0.0</td> <td>70.0</td> <td>4.95e-11</td> <td>3.97e-11</td> <td>...</td> </tr> </tbody> </table> Data and forecasts are described in detail in the following publications: Bayona, J.A., Savran, W., Strader, A., Hainzl, S., Cotton, F. and Schorlemmer, D., 2021. Two global ensemble seismicity models obtained from the combination of interseismic strain measurements and earthquake-catalogue information. Geophysical Journal International, 224(3), pp.1945-1955. Kreemer, C., Blewitt, G. and Klein, E.C., 2014. A geodetic plate motion and Global Strain Rate Model. Geochemistry, Geophysics, Geosystems, 15(10), pp.3849-3889. Kagan, Y.Y. and Jackson, D.D., 2011. Global earthquake forecasts. Geophysical Journal International, 184(2), pp.759-776.

Raw Data for the article: Donor Preconditioning with Inhaled Sevoflurane Mitigates the Effects of Ischemia-Reperfusion Injury in a Swine Model of Lung Transplantation

Primary graft dysfunction (PGD) and ischemia-reperfusion injury (IRI) occur in up to 30% of patients undergoing lung transplantation and may impact on the clinical outcome. Several strategies for the prevention and treatment of PGD have been proposed, but with limited use in clinical practice. In this study, we investigate the potential application of sevoflurane (SEV) preconditioning to mitigate IRI after lung transplantation. The study included two groups of swines (preconditioned and not preconditioned with SEV) undergoing left lung transplantation after 24-hour of cold ischemia. Recipients' data was collected for 6 hours after reperfusion. Outcome analysis included assessment of ventilatory, hemodynamic, and hemogasanalytic parameters, evaluation of cellularity and cytokines in BAL samples, and histological analysis of tissue samples. Hemogasanalytic, hemodynamic, and respiratory parameters were significantly favorable, and the histological score showed less inflammatory and fibrotic injury in animals receiving SEV treatment. BAL cellular and cytokine profiling showed an anti-inflammatory pattern in animals receiving SEV compared to controls. In a swine model of lung transplantation after prolonged cold ischemia, SEV showed to mitigate the adverse effects of ischemia/reperfusion and to improve animal survival. Given the low cost and easy applicability, the administration of SEV in lung donors may be more extensively explored in clinical practice.

Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"

This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".

Raw data of: "Controlling Hand Movements Relying on Tactile Illusions: A Model Predictive Control Framework"

in Fig4_a.txt: raw the data for the plot of Fig4_a  (x and y of the first simulated trajectory from trajectory 1 to 50) in Fig4_b.txt: raw the data for the plot of Fig4_b  in Fig4_c.txt raw the data for the plot of Fig4_b. Each column corresponds to the optimal angle of the plate for each of the 50 trajectories simulated in Fig4_a

Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"

The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery) This repository contains the input data used for building the RF models and the files describing the modeling results. The description of the files is given in the README.txt file. Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613. <a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a>

opencc-by-4.0Sep 2021View details →

Data for the publication "Assessing the potential for simplification in global climate model cloud microphysics"

This repository contains the data for the paper: Authors: Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann Titel: Assessing the potential for simplification in global climate model cloud microphysics Date: 2022 Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.5506588)

dryad40/100

Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds

For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (Egretta caerulea) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.

opencc-zeroMar 2022View details →

Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines

This repository contains the processed data and plotting routines associated with the article Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914 The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots. The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.

opencc-by-4.0Dec 2021View details →

Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model

This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy. Wouter Meijninger1, Berien Elbersen1, Michiel van Eupen1, Stephan Mantel2, Pilar Ciria Ciria3, Andrea Parenti4, Marina Sanz Gallego3 and Paloma Perez Ortiz3, Marco Acciai4,and Andrea Monti4 Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University, Abstract (Manuscript) In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect. Readme data files Coherence_quarterly_statisitcs_2017_to_2020.zip Radar coherence quarterly statistics - Albacete (Spain) Radar coherence data is based on Sentinel-1B Period: 2017 to 2020 File naming (.tif files) per year (YYYY): <ul> <li>Mean coherence: mean_YYYY_1to4.tif</li> <li>Standard deviation coherence: std_YYYY_1to4.tif</li> <li>Range coherence: range_YYYY_1to4.tif</li> <li>Mean delta coherence: mean_delta_YYYY_1to4.tif</li> <li>Standard deviation delta coherence: std_delta_YYYY_1to4.tif</li> <li>Maximum delta coherence: max_delta_YYYY_1to4.tif</li> </ul> Each file consists of 4 bands: <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> Statistics are based on radar coherence data, which is scaled between >0 and 1 No data: 0-values Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m SIGPAC_data_Albacete_2018_to_2020.zip <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> More than 5 year fallow (according to SIGPAC) Period: 2018 to 2020 File naming (ENVI files): <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> Pixel values: 0: Not fallow 1: Fallow more than 5 years Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m Land Use Land Cover LULC (according to SIGPAC) Period: 2018 to 2020 File naming (ENVI files): <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> Pixel values: <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m Annual_unused_used_land_maps_Albacete_2017_to_2020.zip Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model Period: 2017-2020 File naming (ENVI files): <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> Pixel values: 0 - Used (and/or Nan) 1 - Unused Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m Four_year_abandoned_land_Albacete_2017_to_2020.zip Four-year abandonment map is based on the 4 annual unused/used land maps File naming (ENVI): <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> Pixel values: 0 - (Nan) 1 - Used (1 year unused in period 2017 - 2020) 2 - Used (2 year unused in a row in period 2017 - 2020) 3 - Abandoned (3 year unused in a row in period 2017 - 2020) 4 - Abandoned (4 year unused in a row in period 2017 - 2020) Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m

A volumetric model of rabbit heart and torso including ECG data and ventricular activation sequence

Data generated and analyzed of our work titled "A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution". Please see the respective publication for more context.   <ul> <li>BSPM_filtered.dat <ul> <li>Contains the filtered ECG Data</li> </ul> </li> <li>BSPM_original.bdf <ul> <li>Contains the originally recorded signal. Information on the file format itself can be found here: <a href="https://www.biosemi.com/faq/file_format.htm">https://www.biosemi.com/faq/file_format.htm</a> Links to various toolboxes to open the file can be found here: <a href="https://www.biosemi.com/download.htm">https://www.biosemi.com/download.htm</a></li> </ul> </li> <li>CT_DataDCM.zip <ul> <li>Contains the recorded CT images of heart and torso in DCM file format</li> </ul> </li> <li>ECG_NodeIndices.txt <ul> <li>Contains the the node IDs of the torso mesh corresponding to the electrode positions of the ECG Vest</li> </ul> </li> <li>Endocardial_Surface_Papillary.stl <ul> <li>Segmented endocardial surface including papillary muscles</li> </ul> </li> <li>Mat_LeadField.dat <ul> <li>Contains the calculated lead field matrix to be used in combination with the provided Mesh_Ven.vtu. Make sure to keep the node order</li> <li> <pre><code class="language-python"># Python example of usage # Define a read_vm_vec function which reads your calculated data beforehand import numpy as np t_begin = 0 t_end = 400 LF_mat = np.loadtxt('Mat_LeadField.dat') times = np.linspace(t_begin, t_end, t_end-t_begin) result = np.zeros((len(times), 31)) for i,t in enumerate(times): vm_vec = read_vm_vec(t) result[i, :] = LF_mat.dot(vm_vec)[0:31] result = np.insert(result, 0, times, axis=1) np.savetxt('BSPM.dat', result)</code></pre>   </li> </ul> </li> <li>Mesh_PurkinjeTree.vtp <ul> <li>The resulting optimized Purkinje Node tree. We recommend using <a href="https://www.paraview.org/">ParaView</a> for visualization</li> </ul> </li> <li>Mesh_StimPoints.vtp <ul> <li>The resulting points of stimulation.</li> </ul> </li> <li>Stim_IndexTime.dat <ul> <li>Contains the stimulation pattern in terms the node index of Mesh_Ven.vtu and the respective stimulation time</li> </ul> </li> <li>Mesh_Ven.vtu <ul> <li>Contains the ventricular mesh as well as the repective lead field matrix values for each surface node.</li> <li>Material: Right Ventricle 30 Left Ventricle 31</li> </ul> </li> <li>Mesh_Torso.vtu <ul> <li>Contains the whole torso mesh.</li> <li>Material: Fat 2 Bones 3 Blood 9 Cartilage 14 Liver 20 Lungs 17 Right Ventricle 30 Left Ventricle 31 Right Atrium 32 Left Atrium 33 Aorta 60 Pulmonary artery 61 Left Vena Jugularis 62 Right Vena Jugularis 62 Post Vena Cava 62</li> </ul> </li> </ul>

opencc-by-4.0Dec 2021View details →

Proteomics data of mitochondrial fraction of CRL-2097 cancer cell line model

The cancer cell line model developed using human dermal fibroblasts CRL-2097 was used in these experiments: Sample 1 - CRL2097 + hTERT Sample 2 - CRL2097 + hTERT + LT Sample 2 - CRL2097 + hTERT + LT + Ras The mitochondrial fraction was prepared from each of these cell lines and analysed via mass spec for their proteomics. The experiment was done in duplicates.

dryad40/100

Data for: Growing faster, longer or both? Modelling plastic response of Juniperus communis growth phenology to climate change

Aim: Plant growth and phenology plastically respond to changing climatic conditions both in space and time. Species-specific levels of growth plasticity determine biogeographical patterns and the adaptive capacity of species to climate change. However, a direct assessment of spatial and temporal variability in radial-growth dynamics is complicated, as long records of cambial phenology do not exist. Location: 16 sites across European distribution margins of Juniperus communis L. (the Mediterranean, the Arctic, the Alps and the Urals). Time period: 1940-2016 Major taxa studied: Juniperus communis Methods: We applied the Vaganov-Shashkin process-based model of wood formation to estimate trends in growing season duration and growth kinetics since 1940. We assumed that J. communis would exhibit spatially and temporally variable growth patterns reflecting local climatic conditions. Results: Our simulations indicate regional differences in growth dynamics and plastic responses to climate warming. Mean growing season duration is the longest at Mediterranean sites and, recently, there is a significant trend towards its extension of up to 0.44 days per year. However, this stimulating effect of longer growing season is counteracted by declining summer growth rates caused by amplified drought stress. Consequently, overall trends in simulated ring-widths are marginal in the Mediterranean. By contrast, durations of growing seasons in the Arctic show lower and mostly non-significant trends. However, spring and summer growth rates follow increasing temperatures, leading to a growth increase of up to 0.32 % per year. Main conclusions: This study highlights the plasticity in growth phenology of widely distributed shrubs to climate warming–an earlier onset of cambial activity that offsets the negative effects of summer droughts in the Mediterranean and, conversely, an intensification of growth rates during the short growing seasons in the Arctic. Such plastic growth responsiveness allows woody plants to adapt to the local pace of climate change.

opencc-zeroAug 2022View details →

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>

RACMO regional climate model data, postprocessed for winter precipitation and winter temperature

This contains statistics of winter precipitation and winter temperature derived from the 16 model ensemble by RACMO2. In addition to the GCM driven runs, also a PGW (pseudo global warming) set is given. Data is used for a paper to be submitted. Reference on the RACMO2 runs: Aalbers EE, Lenderink G, van Meijgaard E, van den Hurk BJJM (2018) Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50:4745–4766. <a href="https://doi.org/10.1007/s00382-017-3901-9">https://doi.org/10.1007/s00382-017-3901-9</a>

opencc-by-4.0Apr 2021View details →

Sticky Pi -- Machine Learning Data, Configuration and Models

Dataset for the Machine Learning section of the Sticky Pi project (https://doc.sticky-pi.com/) Contains the dataset for the three algorithms described in the publication: Universal Insect Detector, Siamese Insect Matcher and Insect Tuboid Classifier. Universal Insect Detector: `universal_insect_detector/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication. <ul> <li>`data/` – A set of svg images that contain the embedded jpg raw image, and a set of non-intersecting polygon around the labelled insects</li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>`config/` <ul> <li>`config.yaml `– The configuration file defining the hyperparameters to train the model</li> <li>`mask_rcnn_R_101_C4_3x.yaml` – the base configuration file from which config is derived</li> </ul> </li> </ul>   Siamese Insect Matcher `siamese_insect_matcher/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication. <ul> <li>`data/` – a set of svg images that contain two embedded jpg raw images vertically stacked corresponding to two frames in a series. Each predicted insect is labelled as a polygon. Insects that are labelled as the same instance, between the two frames, are grouped (i.e. SVG group). The filename of each image is `<device>.<datetime_frame_1>.<datetime_frame_2>.svg`</li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>`config/` <ul> <li>`config.yaml` – The configuration file defining the hyperparameters to train</li> </ul> </li> </ul> Insect Tuboid Classifier: `insect_tuboid_classifier/` contains images of insect tuboid, a database file describing their taxonomy, a configuration file to train the model, and the model as trained and used for publication. <ul> <li>`data/` <ul> <li>`database.db`: a sqlite file with a single table `ANNOTATIONS`. The table maps a unique identifier of each tuboid (tuboid_id) to a set of manually annotated taxonomic variables.</li> <li>A directory tree of the form: `<series_id>/<tuboid_id>/`. Each terminal directory contains: <ul> <li> <ul> <li>`tuboid.jpg` – a jpeg image made of 224 x 224 tiles representing all the shots in a tuboid, left to right, top to bottom – might be padded with empty images</li> <li>`metadata.txt` – a csv text file with columns: <ul> <li> <ul> <li>parrent_image_id – <device>.<UTC_datetime></li> <li>X – the X coordinates of the object centroid</li> <li>Y – the Y coordinates of the object centroid</li> </ul> </li> </ul> </li> <li>scale – The scaling factor applied between the original and image and the 224 x 224 tile (>1 => image was enlarged)</li> <li>`context.jpg` – a representation of the first whole image of a series, with a box around the first tuboid shot (this is for debugging/labelling purposes)</li> </ul> </li> </ul> </li> </ul> </li> <li>`output/` <ul> <li>`model_final.pth` – the model as trained for the publication</li> </ul> </li> <li>config/ <ul> <li>`config.yaml` – The configuration file defining the hyperparameters to train the model as well as the taxonomic labels</li> </ul> </li> </ul>

Accompanying simulated data for "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity"

The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners. This repository contains data generated for the manuscript: "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.