Find research datasets worth reusing

opencc-by-4.0Nov 2016View details →

Imperial College London femur and tibia surface mesh set

The dataset contains bone geometries of the left and right thigh (femur) and shank (tibia and fibula) segmented from magnetic resonance (MR) scans of 35 healthy volunteers (22 male, 13 female; height 155 cm to 193 cm, weight 45 kg to 108 kg, body mass index 17.0 kg/m2 to 34.8 kg/m2). If you are using this dataset, please cite: Nolte, D., Tsang, C.K., Zhang, K., Ding, Z., Kedgley, A.E., Bull, A.M.J., 2016. Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models. J. Biomech. 49, 3576–3581. doi:10.1016/j.jbiomech.2016.09.005

opencc-by-4.0Dec 2023View details →

Mesh dataset for the paper "Bending-Reinforced Grid Shells for Free-form Architectural Surfaces"

If using this dataset, please cite the paper:  Laccone, F., Pietroni, N., Cignoni, P., Malomo, L.: Bending-Reinforced Grid Shells for Free-form Architectural Surfaces. Computer-Aided Design. Available online 23 December 2023, 103670. https://doi.org/10.1016/j.cad.2023.103670

opencc-by-4.0Aug 2024View details →

S-MESh based daily 1km surface PM2.5 over Europe for the years 2021-2022

These datasets consist of estimated daily surface PM2.5 concentrations over Europe at ~1km spatial resolution for the years 2021-2022 in tiff file format generated using our Satellite and ML-based Estimation of Surface air quality at High resolution (S-MESH) model. The S-MESH model for PM2.5 generates surface PM2.5 over Europe by downscaling CAMS regional forecast using satellite AOD and other meteorological parameters through a stacked XGBoost model. The downscaled S-MESH products have accuracies similar to CAMS regional interim reanalysis. More details on this methodology and study can be found in the related research article  <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envres.2024.120363" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.envres.2024.120363</a> The attached dataset files are zipped into 2 folders each corresponding to a year and can be unzipped from command line using "tar -xvzf filename.tar.gz". Each tiff file represents daily PM2.5 and is a single band image with an extent of 26°W-45°E and 34°N–72°N in EPSG:4326 - WGS 84 projection.  This work was primarily supported by a PhD fellowship from the Norwegian Research Council under grant agreement 321851.

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during 2022 at the monthly sampling observatory of Blanes Bay Microbial Observatory.

Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seasonally resolved seawater measurements. Mediterranean surface seawater sampling at the Blanes Bay Microbial Observatory was carried out monthly during 2022. January, March and April were not sampled due to technical difficulties. Seawater was hand-collected in glass sampling bottles around 11:00 am each day. The samples were kept in the dark and analysed at the ICM-CSIC for MeSH and DMS with the Vocus-PTR coupled to SFCE within 3 h of sample collection. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature and sea surface salinity). Sea surface temperature and salinity were determined using a CTD profiler Model SD204 (SAIV A/S). The chlorophyll from the Blanes 2022 timeseries was measured on a fluorometer (10AU Turner fluorometer). Details on the measurement methodology can be found here: Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates south) Lon=longitude (negative indicates west ) Hourlybin = The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) fluo = chlorophyll a concentration in mg m^(-3) SST= sea surface temperature in degrees Celsius SSS= sea surface salinity in practical salinity units (PSU)

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during POLAR-CHANGE Antarctic expedition 2023 on board the BIO Hesperides.

Global emissions of methanethiol (MeSH) are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seawater concentration measurements from the Southern Ocean. The POLAR-CHANGE cruise was conducted in February and March 2023 on board the BIO RV Hesperides A33 in waters of the Antarctic Peninsula and the Weddell Sea. A Vocus PTR-ToF (TOFWERK AG, Thun, Switzerland, Vocus Scout) coupled to a segmented flow coil equilibrator (SFCE) was used to measure DMS and MeSH in seawater from the underway seawater inlet. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature (SST) and sea surface salinity (SSS)). Sea surface temperature (SST) and sea surface salitity (SSS) were measured in seawater from the underway water inlet located at 4-5 m depth. There was good agreement between the underway SST, SSS and the CTD sensors, confirming that the underway sensors were well calibrated. Underway seawater fluorescence measurements were not calibrated for total chlorophyll a (Chl a) concentration by frequent discrete measurements of Chl a from the on-board 10AU Turner fluorometer. For calibrated Chl a data, please get in touch with the autors.   From the beginning of the POLAR-CHANGE cruise we observed extremely high MeSH concentrations which immediately dropped down to within previously observed ranges, after cleaning the equilibrator daily with 10 % HCl. We decided to exclude all MeSH measurements before regular cleaning commenced from 26/02/2023 onwards as these datapoints are likely affected by biofouling. MeSH seawater concentrations prior to this date are replaced by -999 in the dataset shared here. Other gaps in the timestamp of data acquisition are due to calibrations or measuring other samples.   Details on the measurement methodology can be found here: Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates west and south) Lon=longitude (negative indicates west and south) Date=  The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) fluo = surface fluorescence, a proxi for chlorophyll a concentration in mg m^(-3) SST= sea surface temperature in degrees Celsius SSS= sea surface salinity in practical salinity units (PSU)

opencc-by-4.0Oct 2023View details →

High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"

High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"

opencc-by-4.0Feb 2022View details →

Shadow Surface Meshes

This repository contains triangle meshes of the shadow-recieving surfaces of 13 ancient sundials; three of them are from Greece and 10 from Italy. The meshes are in correspondence. The dataset was used in the Math+ project EF2-3 "Spline Models for Shape Trajectory Analysis." It is used in the paper "Intrinsic shape analysis in archaeology: A case study on ancient sundials" by Hanik et al. in a study that applied nonlinear shape analysis methods to archaeological data.

opencc-by-4.0Mar 2024View details →

Generating high fidelity surface meshes of neocortical neurons using skin modifiers

<h2>Summary</h2> The dataset contained in this repository is the resulting meshes from applying the Skin Modifier in Blender on all neuronal morphological types (mtypes) identified for comastosensory cortical cells by the Blue Brain Project.  The papser, Generating high fidelity surface meshes of neocortical neurons using skin modifiers, is published in the 2019 EG Computer Graphics & Visual Computing (CGVC) conference.  <div><a href="https://doi.org/10.2312/cgvc.20191257">https://doi.org/10.2312/cgvc.20191257</a> <a href="https://diglib.eg.org/handle/10.2312/cgvc20191257">https://diglib.eg.org:443/handle/10.2312/cgvc20191257</a></div> <h2>Paper abstract </h2> <div>We present the results of exploring the capabilities of skinning modifiers to generate high fidelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain specific framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neuroscientific community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientific illustrations of in silico neuronal circuits for media production with physically-based path tracers.</div> <h2>BibTex citation</h2> <blockquote> @inproceedings {10.2312:cgvc.20191257,        booktitle = {Computer Graphics and Visual Computing (CGVC)},        editor = {Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.},        title = {{Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers}},        author = {Abdellah, Marwan and Favreau, Cyrille and Hernando, Juan and Lapere, Samuel and Schürmann, Felix},        year = {2019},        publisher = {The Eurographics Association},        ISBN = {978-3-03868-096-3},        DOI = {10.2312/cgvc.20191257}     } </blockquote>

Global climatology of sea-surface methanethiol (MeSH) concentration fields

This dataset contains a global climatology of sea-surface MeSH concentration and sea-to-air flux, as described in this article (see README file): Wohl, C., Villamayor, J., Galí, M., Mahajan, A. S., Fernández, R. P., Cuevas, C. A., Bossolasco, A., Li, Q., Kettle, A. J., Williams, T., Sarda-Esteve, R., Gros, V., Simó, R., and Alfonso Saiz-Lopez, A. (2024). Marine emissions of methanethiol increase aerosol cooling in the Southern Ocean. Science Advances, 10(48), eadq2465. DOI: 10.1126/sciadv.adq2465 Given the several choices and uncertainties associated with sea-air gas flux calculations, we refrain from providing flux estimates, and encourage data users to calculate their own emission fields in a way that maximizes consistency with their chosen geophysical fields or model setup.

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater digitalised from AMT-7 1998.

Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we have digitalised measurements from 1998 taken during AMT-7 (Atlantic Meridional Transect), which may be useful for future investigators. The measurements are originally presented in the following reference: Kettle, A. J., Rhee, T. S., von Hobe, M., Poulton, A., Aiken, J., and Andreae, M. O.: Assessing the flux of different volatile sulfur gases from the ocean to the atmosphere, J. Geophys. Res., 106, 12193–12209, https://doi.org/10.1029/2000JD900630, 2001. Measurements from AMT-7 were extracted from the graphs in the manuscript using an image extraction technique (https://apps.automeris.io/wpd/, last accessed July 2024). To ensure concurrency of the measurements and deal with the error in the time stamp related to the use of an image extraction technique, data from AMT-7 was binned in 6-hourly bins. Data gaps are listed with an improbable number of -999.   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates south) Lon=longitude (negative indicates west) t_Kettle01 = The mean measured concentration during each 6 hour bin is shown, 3 hours either side of the listed time. The timestamp indicates sampling time in local solar time, expressed as DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) Chla_mg_m3 = surface chlorophyll a concentration in mg m^(-3) SST_C= sea surface temperature in degrees Celsius SSS_PSU= sea surface salinity in practical salinity units (PSU)

opencc-by-4.0Sep 2023View details →

Cipher Machine Hagelin C-446 A_surface meshes (Inv. 2017-396)

Surface Meshes created from reconstructed CT-image stacks. The inital CT dataset is linked as a related identifier.