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Lifetime brain atrophy estimated from a single MRI: measurement characteristics and genome-wide correlates
No full textHere we provide the GWAS summary statistics associated with Lifetime brain atrophy estimated from a single MRI: measurement characteristics and genome-wide correlates, Fürtjes et al.Fürtjes, A. (2025). Lifetime brain atrophy estimated from a single MRI: measurement characteristics and genome-wide correlates [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1486074
desihub/fastspecfit: 3.1.3
No full textFastSpecFit version 3.1.3.Moustakas, Dirk Scholte, Biprateep Dey, Ashod Khederlarian, Benjamin Alan Weaver, & Stephen Bailey. (2025). desihub/fastspecfit: 3.1.3 (3.1.3). Zenodo. https://doi.org/10.5281/zenodo.1478694
Carrot-Mapper
No full textCarrot-Mapper enables conversion of data to the OMOP Common Data Model, without data being egressed from its secure location, nor requiring access to the secure location. Carrot-Mapper automates as much of the process as possible, and also enables users to reuse each others' mappings. Carrot-Mapper pairs with Carrot-CDM to complete the OMOP conversion process.Cox, S., Quinlan, P., Jefferson, E., Lea, D., Panagi, V., Macdonald, C., Rae, A., Akashili, E., Tarr, S., Adejumo, S., Mumtaz, S., Santos, R., & Nguyen, T. T. (2025). Carrot-Mapper (2.5.1). Zenodo. https://doi.org/10.5281/zenodo.1464443
Micro-CT scans of archaeological human teeth from the Belarusian sites of Połack, Pahošča and Niaśviž
No full textThis database contains Micro-CT scans of archaeological human teeth from the Belarusian sites of Połack, Pahošča and Niaśviž. These data were created as part of the documentation process for archaeological tooth samples before destructive analysis was conducted as part of Vera Haponava's PhD research. A series of steps were taken to preserve as much data related to the samples as possible, as well as to retain spare tissues (dentine, enamel, and calculus) that did not need to be destroyed during the analysis. These steps included creating a written catalogue of tooth features, taking photographs from six aspects, and making casts of tooth crowns. The database includes: (1) a README file with the description of its structure, summary of the data and its uses, technical details of the CT-scanning process, and a short description of the archaeological sites with relevant sources; (2) a Catalogue of the samples with relevant contextual information; (3) five deposits of CT-scans of teeth by site (Połack Upper Castle, Połack Lower Castle, Połack Township, Pahošča, Niaśviž). The README and the Catalogue files, as well as the five deposits may be retrieved separately. The database contains scans of 109 teeth from 64 individuals: - Breakdown by site: Połack - Upper Castle (28), Lower Castle (32) and Township (13); Pahošča (13), Niaśviž Corpus Christi Church (23). - Breakdown by tooth type: 24 fist incisors (I1), 34 first molars (M1), 3 second molars (M2), 47 third molars (M3), 1 second premolar (P2). - Breakdown by time period: 10-13th centuries CE (41), 13-16th centuries CE (24), 17-18th centuries CE (44). Potential applications of this dataset include analyzing the internal structure of teeth or taking precise measurements (a scale is provided for all images in the form of the voxel size). The images can be used independently or reconstructed into digital 3D models of teeth. The CT scanning was funded by the School of History, Classics, and Archaeology, the University of Edinburgh. The Micro-CT scans were made using a bespoke scanner designed and built in the School of GeoSciences of the University of Edinburgh. Data related to publication: Medieval and early modern diets in the Polack region of Belarus: A stable isotope perspective. Vera Haponava , Aliaksei Kots, Mary Lucas, Max Both, Patrick Roberts . Published October 7, 2022 in PLOS One. https://doi.org/10.1371/journal.pone.0275758 This data can be accessed by contacting the Owner or Creator named on this page. All access requests will be granted. Vera Haponava's contact details are listed on ORCID: https://orcid.org/0000-0002-1532-483
A course on the setup, running, and analysis of biomolecular simulations
No full textThis is a course aimed at beginners in biomolecular simulation. It is expected that students are already familiar with key concepts of molecular dynamics simulation theory, and have a basic working knowledge of Jupyter notebooks, Python (especially the NumPy library), and the bash shell. The course is constituted of lectures (L1-8) and practical (P) sessions, subdivided in two units. Unit 1 is dedicated to providing foundations on protein structure and their preparation for molecular dynamics (MD) simulation. Unit 2 is dedicated to describing means of extracting information from the MD simulation of a protein.Degiacomi, M., Gowers, R., Matta, M., & Mey, A. S. J. S. (2025, January 29). A course on the setup, running, and analysis of biomolecular simulations. Zenodo. https://doi.org/10.5281/zenodo.1476701
Output of Fenics_ice experiments on ice streams draining into the Amundsen Sea Embayment
No full textOutput of several experiments with Fenics_ice over ice streams draining into the Amundsen Sea Embayment. The code to produce this output can be found in the following repository; ASE_fenics_ice_exp More information on how to read and plot this data can be found in this repository wiki: Citation for the ASE_fenics_ice_exp repository used to produce this data: https://doi.org/10.5281/zenodo.15676427 Citation for the Fenics_ice version used to produce this data: https://doi.org/10.5281/zenodo.1463189
Harnessing tensor cores for greater computational efficiency and lower energy usage in phylogenetic inference
No full textYou can find the scripts and installation instructions for BEAGLE to replicate the benchmarking in this Github repository. We provide the resulting logs and profiling information here.Gangavarapu, K., Ji, X., Shao, Y., Rambaut, A., Lemey, P., Baele, G., & Suchard, M. (2025). Harnessing tensor cores for greater computational efficiency and lower energy usage in phylogenetic inference [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1560515
DNS Kolmogorov flow data for Re 30
No full textThe dataset contains a three dimensional (3-D) Direct Numerical Simulation (DNS) of a Kolmogorov flow with the unit Reynolds number defined as Re=1/nu and a sinusoidal forcing on streamwise axis and one wavenumber. It is an extended shear flow simulation from 2-D simulation into 3-D with periodic boundary on the side walls, input on the left and output on the right walls. It was generated in python using the spectral solver Dedalus. The data can be used to train machine learning algorithm such as an autoencoder for reduce-order modelling of turbulent flow. This is a subset of three increasing Reynolds numbers of Kolmogorov flow set to Re= 30, 50 and 90. You can find the other dataset on the Datashare
Estimating the Environmental Impact of Dog Foods Marketed in the UK Dataset
No full textThis file contains relevant scripts and datasets.Harvey, J. (2025). Estimating the Environmental Impact of Dog Foods Marketed in the UK Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1572139
Short range intervortex force coefficients
No full textWe present the coefficients for the short range expansion of the static vortex interaction from Ginzburg-Landau or Abelian Higgs model. The corresponding expansion of the vortex energy is given in the paper "Short range intervortex forces" by Martin Speight and Thomas Winyard. The columns include the required c's from section 5 to be able to model 2 and 3-body interactions as well as the coefficients q and m for the long-range interaction calculated in section V of Speight, J. M. (1997). Static intervortex forces. Physical Review D, 55(6), 3830
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