cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7488/ds/3141
title: Wnt Pathway Analysis mapped gene-expression data
title: 3D Spatially mapped gene-expression patterns for the full Wnt gene family and key pathway genes for mouse embryo developmental stages E9.5 (Theiler 15) , E10.5 (Theiler 17) and E11.5 (Theiler 19)

description: We provide a unique dataset of 3D mapped gene expression patterns that operate within a vital signalling system for normal development, openly available to the scientific community for further analysis. The associated published manuscript provides novel insight to the function of the Wnt pathway genes and demonstrates how the data can be used to build testable hypotheses. This dataset includes the spatial expression patterns of the whole Wnt and Fzd families of genes plus selected additional pathways genes mapped onto standard spatial reference embryos at three stages of mouse embryo development. These were selected as of the most critical times for organ development and are E9.5 (Theiler stage 15), E10.5 (Theiler stage 17) and E11.5 (theiler stage 19).
description: See README.txt
DOI: 10.7488/ds/3781
title: Global junction angle dataset

description: A global dataset of channel junction angles. File is a zipped csv file. Data is extracted using LSDTopoTools (https://github.com/LSDtopotools). The DEM underlying this data is NASA’s 30 m resolution void-filled Shuttle Radar Topography Mission Digital Elevation Model Version 3 (SRTM-DEM). Dataset headers: latitude (decimal degrees), longitude (decimal degrees), donor1_stream_order (Horton-Strahler stream order of first tributary), donor2_stream_order (Horton-Strahler stream order of second tributary), receiver_stream_order (Horton-Strahler stream order of the channel formed at the junction), donor1_drainage_area (m2), donor2_drainage_area (m2), this_junction_drainage_area (m2), donors_junction_angle (°), donor1_receiver_junction_angle (°), donor2_receiver_junction_angle (°), gradient_donor1, gradient_donor2, gradient_receiver, ai (aridity index value from Trabucco et al., (2019)), AR (ratio of tributary drainage areas), AI_class (as according to Trabucco et al., (2019))
description: junction_angles_global.zip: A zipped file that contains a single csv file, junction_angles_global.csv This file contains the headers: latitude (decimal degrees), longitude (decimal degrees), donor1_stream_order (Horton-Strahler stream order of first tributary), donor2_stream_order (Horton-Strahler stream order of second tributary), receiver_stream_order (Horton-Strahler stream order of the channel formed at the junction), donor1_drainage_area (m2), donor2_drainage_area (m2), this_junction_drainage_area (m2), donors_junction_angle (°), donor1_receiver_junction_angle (°), donor2_receiver_junction_angle (°), gradient_donor1, gradient_donor2, gradient_receiver, ai (aridity index value from Trabucco et al., (2019)), AR (ratio of tributary drainage areas), AI_class (as according to Trabucco et al., (2019))
DOI: 10.7488/ds/3140
title: Luanyjang Dinka noun number morphology database

description: This database contains supplementary materials for the paper “On the systematic nature of Dinka noun number morphology” by D. Robert Ladd and Mirella L. Blum. The supplementary materials consist of a dataset with Dinka nouns, marked for root vowel, grade alternation, lexical quantity, voice quality, and tone class, and explanatory notes for how to use the database.
DOI: 10.7488/ds/3142
title: Wnt Pathway Analysis mapped gene-expression point-cloud data
title: 3D Spatially mapped gene-expression point-cloud patterns for the full Wnt gene family and key pathway genes for mouse embryo developmental stages E9.5 (Theiler 15) , E10.5 (Theiler 17) and E11.5 (Theiler 19)

description: We provide a unique dataset of 3D mapped gene expression patterns that operate within a vital signalling system for normal development, openly available to the scientific community for further analysis. The associated published manuscript provides novel insight to the function of the Wnt pathway genes and demonstrates how the data can be used to build testable hypotheses. This dataset includes the spatial expression patterns of the whole Wnt and Fzd families of genes plus selected additional pathways genes mapped onto standard spatial reference embryos at three stages of mouse embryo development. These were selected as of the most critical times for organ development and are E9.5 (Theiler stage 15), E10.5 (Theiler stage 17) and E11.5 (theiler stage 19). The patterns have been converted to a “point-cloud” representation to allow the 3D visualisation of individual and combined patterns in a standard web-browser such as Chrome, Firefox or Safari. Any webGL enabled browser should work.
description: See README.txt
DOI: 10.7488/ds/3094
title: Charting the Impact of Bilingualism for Autistic and Non-Autistic Children

description: The overall aim of this research is to ask how growing up in a bilingual environment affects the linguistic, cognitive and social development of children and, importantly, do these developmental effects differ for children with autism spectrum disorders. Here we collected data from autistic and non-autistic children at two timepoints, one year apart, (November 2018 - September 2020) in order to understand the influence of bilingual exposure on changes in cognitive development. Data is split into: - Demographic information (including language profile) and diagnostic criteria that are the same for timepoints 1 and 2 - Standardised cognitive assessments from timepoint and parent reports from timepoints 1 and 2 (executive functions, vocabulary, IQ, social cognition) - Computer based tasks measuring aspects of executive functions at timepoints 1 and 2 - Eyetracking tasks measuring: theory of mind and social attentional preferences, from timepoints 1 and 2. * See Readme file for instructions on how to access different files. * See protocol for detail regarding the project timeline, participants, and task battery/ data available. * See "Task information files" folder for more detailed information about each task (and matching dataset). This includes any preprints and peer reviewed articles where we have analysed some of this data, or journal articles from which we have (with permission) used stimuli or a task protocol.
DOI: 10.7488/ds/3087
title: Mozambique travel time to any health facility

description: Travel time surface was estimated using the cost allocation (friction surfaces) which used three primary input datasets on land surface characteristics that help or hinder travel speeds: land cover, roads and topography. This Geotiff is a 20-m spatial resolution surface where each pixel has a value which is the time in seconds to travel to the nearest health facility documented in the paper by Maina et al. (2020) doi: 10.1038/s41597-019-0142.
description: service_area_Moz_All.tif - travel time in seconds to the nearest helth facility of any type from every pixel; Travel_Time_Any_Facility_Map.jpg - jpeg image of the geotiff file service_area_Moz_All.tif to demonstrate what the file should look like.
DOI: 10.7488/ds/3062
title: Metrics for quality control of results from super-resolution machine-learning algorithms – Data extracted from publications in the period 2017- May 2021
title: Metrics for super-resolution quality control

description: The quality enhancement and restoration of poor-quality low-resolution magnetic resonance (MR) data are paramount for improving patient care, accuracy in diagnosis and quality in clinical research. Since 2017, with the popularity of deep-learning machine-learning algorithms, there have been an increase in interest, and consequently funding, in applying these algorithms to enhance the spatial resolution of MR data. Deep-learning schemes have demonstrated superiority over the more conventional machine-learning algorithms, as they have produced very accurate results in different medical image processing tasks. The increase in the attempts of applying these algorithms to increase the spatial resolution of MRI data parallels an increase in the number of metrics considered for evaluating their performance. This dataset summarises the metrics and strategies to evaluate the performance of super-resolution machine-learning algorithms applied to MRI, from the articles published up to May 2021 in this field. The aims are two-fold: 1) to inform on the metrics used to evaluate results of super-resolution algorithms 2) to inform on publications that have applied the state-of-the-art deep-learning algorithms to increase the spatial resolution of magnetic resonance images
description: The quality enhancement and restoration of poor-quality low-resolution magnetic resonance (MR) data are paramount for improving patient care, accuracy in diagnosis and quality in clinical research. Since 2017, with the popularity of deep-learning machine-learning algorithms, there have been an increase in interest, and consequently funding, in applying these algorithms to enhance the spatial resolution of MR data. Deep-learning schemes have demonstrated superiority over the more conventional machine-learning algorithms, as they have produced very accurate results in different medical image processing tasks. The increase in the attempts of applying these algorithms to increase the spatial resolution of MRI data parallels an increase in the number of metrics considered for evaluating their performance. This dataset summarises the metrics and strategies to evaluate the performance of super-resolution machine-learning algorithms applied to MRI, from the articles published up to May 2021 in this field. The documentation of this dataset can be found in Review_QC_SR_Readme_file.pdf
DOI: 10.7488/ds/3134
title: Interface size drives cotranslational assembly of protein complexes

description: The dataset contains an R notebook and the associated data files that are parsed by the analysis script. The R notebook is an R Markdown document written in R version 4.1.1, and it is best viewed, edited, and run in R Studio version 1.4.1717 under a 64 bit Windows platform. Abstract of the study: Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this is by seeding complex assembly during translation via nascent chain engagement. Here, we considered the possibility that the propensity of subunits to cotranslationally assemble is ingrained within the interface hierarchy of protein complexes. Using a combination of proteome-specific structure data and assembly-onset positions determined by ribosome profiling, we show that larger interfaces are prioritised in the course of cotranslational assembly. We observe that this effect is not exclusive to homomeric complexes, but appears to drive the assembly of heteromeric subunits, to the extent that interface size differences are detectable between N and C-terminal locations, with the former being larger on average. We provide explanations to this phenomenon and discuss its importance in a biological context.
description: raw_data/README.txt contains file structure and data source information.
DOI: 10.7488/ds/3067
title: Diffusion, phase behavior and gelation in a two-dimensional layer of colloids in osmotic equilibrium with a polymer reservoir

description: The addition of enough non-adsorbing polymer to an otherwise stable colloidal suspension gives rise to a variety of phase behavior and kinetic arrest due to the depletion attraction induced between the colloids by the polymers. We report a study of these phenomena in a two-dimensional layer of colloids. The three-dimensional phenomenology of crystal-fluid coexistence is reproduced, but gelation takes a novel form, in which the strands in the gel structure are locally crystalline. We compare our findings with a previous simulations and theory, and find substantial agreement.
description: CSD - Images used to calculate the cluster size distributions and average size over time. Dz - Tracking data for diffusivity calculations for a range of polymer volume fractions with two example movies. PD - images used to calculate \psi_6 for a range of area fractions and polymer volume fractions. Plots - Python scripts to reproduce plots as well as copies of the figures
DOI: 10.7488/ds/3144
title: New methods for the synthesis of spirocyclic cephalosporin analogues

description: Spiro compounds provide attractive targets in drug discovery due to their inherent three-dimensional structures, which enhance protein interactions, aid solubility and facilitate molecular modelling. However, synthetic methodology for the spiro-functionalisation of important classes of penicillin and cephalosporin β-lactam antibiotics is comparatively limited. We report a novel method for the generation of spiro-cephalosporin compounds through a Michael-type addition to the dihydrothiazine ring. Coupling of a range of catechols is achieved under mildly basic conditions (K2CO3, DMF), giving the stereoselective formation of spiro-cephalosporins (d.r. 14:1 to 8:1) in moderate to good yields (28−65%).
DOI: 10.7488/ds/3139
title: ISARIC4C OAS data
title: A Prenylated dsRNA Sensor Protects Against Severe COVID-19

description: Inherited genetic factors can influence the severity of COVID-19, but the molecular explanation underpinning a genetic association is often unclear. Intracellular antiviral defenses can inhibit the replication of viruses and reduce disease severity. To better understand the antiviral defenses relevant to COVID-19, we used interferon-stimulated gene (ISG) expression screening to reveal that OAS1, through RNase L, potently inhibits SARS-CoV-2. We show that a common splice-acceptor SNP (Rs10774671) governs whether people express prenylated OAS1 isoforms that are membrane-associated and sense specific regions of SARS-CoV-2 RNAs, or only express cytosolic, nonprenylated OAS1 that does not efficiently detect SARS-CoV-2. Importantly, in hospitalized patients, expression of prenylated OAS1 was associated with protection from severe COVID-19, suggesting this antiviral defense is a major component of a protective antiviral response.
DOI: 10.7488/ds/3152
title: Mechanisms for destabilisation of RNA viruses at air-water and liquid-liquid interfaces

description: Understanding the interactions between viruses and surfaces or interfaces is important, as they provide the principles underpinning the cleaning and disinfection of contaminated surfaces. Yet, the physics of such interactions is currently poorly understood. For instance, there are longstanding experimental observations suggesting that the presence of air-water interfaces can generically inactivate and kill viruses, yet the mechanism underlying this phenomenon remains unknown. Here we use theory and simulations to show that electrostatics provides one such mechanism, and that this is very general. Thus, we predict that the electrostatic free energy of an RNA virus should increase by several thousands of k B T as the virion breaches an air-water interface. We also show that the fate of a virus approaching a generic liquid-liquid interface depends strongly on the detailed balance between interfacial and electrostatic forces, which can be tuned, for instance, by choosing different media to contact a virus-laden respiratory droplet. Tunability arises because both the electrostatic and interfacial forces scale similarly with viral size. We propose that these results can be used to design effective strategies for surface disinfection.
DOI: 10.7488/ds/2936
title: Data supporting the paper "A climate-dependent global model of ammonia emissions from chicken farming"

description: This dataset contains files of ammonia emissions from global chicken agriculture, from chicken housing to land spreading of chicken manure. All files are in netCDF format and can be easily read and processed by Python packages, e.g. Xarray or netCDF4. More details can be read from the files. Please note that these files are to support the paper submitted to the Journal Biogeosciences. Any inquiries go to Jize.Jiang@ed.ac.uk Broiler_file.nc: ammonia emission and volatilisation rate from broiler housing Layer_file.nc: ammonia emission and volatilisation rate from layer housing bc_file.nc: ammonia emission and volatilisation rate from backyard chicken barley NH3.nc: ammonia emission and volatilisation rate from fertilising barley maize NH3.nc: ammonia emission and volatilisation rate from fertilising maize potato NH3.nc: ammonia emission and volatilisation rate from fertilising potato rice NH3.nc: ammonia emission and volatilisation rate from fertilising rice sugarbeet NH3.nc: ammonia emission and volatilisation rate from fertilising sugarbeet wheat NH3.nc: ammonia emission and volatilisation rate from fertilising wheat
DOI: 10.7488/ds/3153
title: Pathogenic loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

description: The dataset contains all the protein variants, their annotations and variant effect predictor scores used in the analysis outlined in our study. Abstract of the study: Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we have investigated the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors underperform on non-LOF mutations, even those based solely on sequence conservation. However, we do find that non-LOF mutations could potentially be identified by their tendency to cluster in space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mutations are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.
description: The Excel file "variant_effect_predictions_and_clustering.xlsx" contains the data used in the pre-print publication titled "Pathogenic loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction". The dataset "Variants" sheet contains mappings of ClinVar disease and gnomAD putatively benign mutations to respective protein structural entries in the Protein Data Bank, structure-derived variant annotations, predictions of impact on stability and numerous variant effect predictor scores. "Inheritance_dist_clustering" and "Mechanism_dist_clustering" sheets contain gene-specific values for a disease variant spatial clustering metric, with genes annotated with disease inheritance and molecular mechanism classes, respectively.
DOI: 10.7488/ds/3154
title: Verbal risk descriptors in patient information leaflets: Communicating risk of Covid-19 vaccination side-effects

description: This submission contains a dataset as elicited and used for the MScLinguistics dissertation "Verbal risk descriptors in patient information leaflets: Communicating risk of Covid-19 vaccination side-effects". It includes the main dataset (as a CSV file), with 144 complete and incomplete responses, information on the questionnaire design (as a PDF file), and a sheet that explains the column names of the dataset (as a PDF file).
DOI: 10.7488/ds/3057
title: Additional files for 'Genomic insights into the biosynthesis and physiology of the cyanobacterial neurotoxin 2,4-diaminobutanoic acid (2,4-DAB)'

description: Supplementary data for Mantas MJQ, Nunn PB, Ke Z, Codd GA and Barker D 'Genomic insights into the biosynthesis and physiology of the cyanobacterial neurotoxin 2,4-diaminobutanoic acid (2,4-DAB)' (2021, Phytochemistry 192:112953, doi:10.1016/j.phytochem.2021.112953). For further details, see 'README.pdf' within the zipfile.
DOI: 10.7488/ds/3132
title: Characterising shear-induced dynamics in flowing complex fluids using differential dynamic microscopy

description: Microscopic dynamics reveal the origin of the bulk rheological response in complex fluids. In model systems particle motion can be tracked, but for industrially relevant samples this is often impossible. Here we adapt differential dynamic microscopy (DDM) to study flowing highly-concentrated samples without particle resolution. By combining an investigation of oscillatory flow, using a novel "echo-DDM" analysis, and steady shear, through flow-DDM, we characterise the yielding of a silicone oil emulsion on both the microscopic and bulk level. Through measuring the rate of shear-induced droplet rearrangements and the flow velocity, the transition from a solid-like to liquid-like state is shown to occur in two steps: with droplet mobilisation marking the limit of linear visco-elasticity, followed by the development of shear localisation and macroscopic yielding. Using this suite of techniques, such insight could be developed for a wide variety of challenging complex fluids.
description: See readme.txt for description of files.
DOI: 10.7488/ds/3504
title: Single photon kilohertz frame rate imaging of neural activity

description: Establishing the biological basis of cognition and its disorders will require high precision spatiotemporal measurements of neural activity. Recently developed genetically encoded voltage indicators (GEVIs) report both spiking and subthreshold activity of identified neurons. However, maximally capitalising on the potential of GEVIs will require imaging at millisecond time scales, which remains challenging with standard camera systems. Here we report application of single photon avalanche diode (SPAD) sensors to imaging neural activity at kilohertz frame rates. SPADs are electronic devices that when activated by a single photon cause an avalanche of electrons and a large electric current. We use an array of SPAD sensors to image individual neurons expressing the GEVI Voltron-JF525-HTL. We show that subthreshold and spiking activity can be resolved with shot noise limited signals at frame rates of up to 10 kHz. SPAD imaging was able to reveal millisecond scale synchronisation of neural activity in an ex-vivo seizure model. SPAD sensors may have widespread applications for investigation of millisecond timescale neural dynamics. Note. This worked was funded by: 1. Wellcome Trust (ISSF3 award IS3-R2.36 to IG and MFN, and Investigator Award 200855/Z/16/Z to MFN); 2. BBSRC EastBio doctoral training programme; 3. EPSRC (EP/S001638/1).
description: The data and codes are organised by figures in the related publication
DOI: 10.7488/ds/3513
title: SUPERSEDED - cAge and bAge EWAS - Epigenome-wide association study of chronological age (linear and quadratic) and of survival

description: ## This deposit has been superseded by the one which can be found at https://doi.org/10.7488/ds/3792 ## These datasets correspond to the manuscript “Refining epigenetic prediction of chronological and biological age“. Here, we include the summary statistics for an epigenome-wide association study (EWAS) of age, considering both CpG (linear) and CpG^2 (quadratic) associations, as well as for an EWAS of survival (using a fixed effects Cox Proportional Hazards Model; results for a mixed effects model to account for relatedness included in Supplementary Table 8 of manuscript). Further methodological details, including quality control steps, software used, and covariates adjusted for, are described in the accompanying manuscript. Analyses were conducted using the Generation Scotland cohort (N = 18,413).