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An attitudes survey in the UK county of Leicestershire was conducted in 2008 by Leicestershire County Council (LCC) as part of the UK government's Department of Communities and Local Government's 'Place Survey'. Leicestershire is a rural county, with the City of Leicester (a separate local authority) forming a hole in centre of the county (see Figure 1). The Place Survey is a postal survey designed to collect data to support national indicators. Individual local government authorities were responsible for administering the survey and were able to include additional questions if they so wished. Because of this, LCC included questions that asked respondents to describe their perceptions of their access to a range of health services (GP surgeries, dentists, hospitals and pharmacies) using a 5-point scale that allowed respondents to indicate whether they found access 'Very easy', 'Fairly easy'. 'Neither easy nor difficult', 'Fairly difficult' or 'Very difficult'. Respondents were also asked to indicate their general health (a 5-point scale from very good to very bad), whether they had any long-standing illness, disability or infirmity (yes or no) and whether they owned a car or not. In Leicestershire there were 8530 responses to the Place Survey, with 4.9% indicating difficulty (i.e. replying either 'difficult' or 'very difficult') in their access to GPs and 20.2% indicating difficulty in their access to hospitals. Of the respondents, 4.6% stated that they had 'bad health' or 'very bad health' (henceforth 'Bad Health'), 33.1% indicated that they had some Long Term Illness and 16.0% stated that they did not own a car (henceforth 'Non-Car Ownership'). The sampling frame for the Place Survey selected household addresses at random from the Post Office small users Address File database. For each of the 7 districts in Leicestershire, sampling was stratified with the aim of reaching a sample size of at least 1,100 in each district, regardless of population size. Central government provided the sample of addresses. The questionnaire was sent to households only and was completed by any resident aged 18 or over living at the address. A total of 20,260 questionnaires were sent out and the response rate for each district was between 41% and 43%. The survey response rates by demographic factors are summarised in Table 1. Leicestershire Statistics and Research Online provide detail of the Place Survey in Leicestershire1 and an interactive visualisation of the results2.

In the UK GP surgeries provide free access to a medical practitioner who treats acute and chronic illnesses, provides preventive care and health education for all. Data for GP surgeries and major National Health Service (NHS) hospitals, with and without Emergency Department (ED) facilities, were downloaded from the NHS website and spatially located from their postcodes. In the UK there are an average of 15 residential addresses per postcode providing a fine level of geographical detail. The locations of GP surgeries, hospitals and Place Survey respondents are shown in Figure 1. The road data was the Ordnance Survey MasterMap Integrated Transport Network layer provided via the EDINA data library A GIS-based network analysis (ArcGIS 9.3) was used to calculate the road distance from each Place Survey post-code location respondent to the nearest GP surgery, hospital and hospital with ED facilities. All of the statistical analyses and mappings were performed in R version 2.13.0, the open source statistical software -project.org/.

Whilst distance to hospitals was not found to be a good predictor of difficulty in hospital access, distance to hospitals with EDs was significant but negative. The relative odds decreased slightly (1%) with each extra km distance to the nearest ED hospital;

To complement the logistic regression above and to examine the spatial variation in these relationships, GWR was used to generate spatially explicit logistic regression models. Table 4 summarises the results of the two GWR analyses (Equations 11 and 12) and describes the variation of the odds ratios for the different independent variables. The Inter-Quartile Range of the odds ratios provides a good indicator of the spatial variation. For Access to GPs, there was little spatial variation in Distance and Long Term Illness as predictors of access difficulty, whilst Bad Health showed some variation, with the relative odds of experiencing difficulty in access to this service ranging from 69% to 81% greater than for who do not have Bad Health. There was more variation in the effects of Non-Car Ownership, which ranged from 3.58 to 3.94 times greater than for those with cars, although the 25th percentile is close to the median, indicating a positive skew in the distribution of the variation. For access to ED hospitals the relative odds of experiencing difficulty with Bad Health ranged from 35% to 64% greater than for those without Bad Health. The effects of Non-Car Ownership were greater but with similar spatial variation, and the relative odds of experiencing difficulty ranged from 47% to 73% greater than for those who owned a car.

Some limitations to this study should be noted. The hospital data was downloaded from the NHS website to include 'major' NHS Hospitals. However, survey respondents were simply asked about their perceptions of access to 'hospitals' which, depending on their personal experiences may include children's hospitals and long stay psychiatric facilities. The attitudes survey captured the degree of difficulty experienced in accessing services but not the underlying reasons for that difficulty. Similarly, the analysis uses geographic distance to the nearest facility, which may or may not be the facility actually used by the survey respondents. However, the responses do provide an indication of the exclusion experienced by a robust sample of the population in the study area. Ongoing work will seek to unpick the underlying causes of the negative perceptions of access. The data used in the study did not capture any information about use and access to private facilities, such as are available under personal health insurance schemes.

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from

The recount3 R/Bioconductor package allows users to download gene, exon, and exon-exon junction counts data provided by the recount3 resource. recount3 is designed to be user friendly and enable users to utilize the full set of analytical and visualization software available in Bioconductor for RNA-seq data. recount3 achieves this by enabling downloads per study and by presenting the data through RangedSummarizedExperiment R/Bioconductor objects [12]. recount3 provides multiple options for converting the base-pair coverage counts [10] into read counts, RPKM values, among other options. Furthermore, recount3 provides the URLs for accessing all of the recount3 resource text files such as the sample bigWig coverage files [19], enabling non-R users to build their own utilities for accessing the data. See Appendix: recount3 R package interface for more details.

recount3 uses BiocFileCache [60] to cache the raw files after downloading them, thus simplifying the end-user experience that users had with recount (for recount2) when they repeatedly access the same data. Unlike recount, recount3 does not provide a function for computing coverage for genomic regions from the recount3 bigWig files since this functionality has been greatly improved in megadepth [29]. Furthermore, recount3 functions have a recount_url argument which can be set to a different mirror or a local directory. This enables using recount3 with data produced by Monorail locally that is not necessarily public.

All EEG, eye tracking and imaging data can be accessed through the 1,000 Functional Connectomes Project and its International Neuroimaging Data-sharing Initiative (FCP/INDI) based at _1000.projects.nitrc.org/indi/cmi_healthy_brain_network (Data Citation 1). This website provides an easy-to-use interface with point-and-click download of HBN datasets that have been previously compressed; the site also provides directions for those users who are interested in direct download of the data from an Amazon Simple Storage Service (S3) bucket. Imaging data is stored in the Brain Imaging Data Structure (BIDS) format, which is an increasingly popular approach to describing MRI data in a standard format54. 041b061a72