Modeled ground magnetic data was extracted from the Pan American Center for Earth and Environmental Studies database at http://irpsrvgis08.utep.edu/viewers/Flex/GravityMagnetic/GravityMagnetic_CyberShare/ on 2/29/2012. The downloaded text file was then imported into an Excel spreadsheet. This spreadsheet data was converted into an ESRI point shapefile in UTM Zone 13 NAD27 projection, showing _location and magnetic field strength in nano-Teslas. This point shapefile was then interpolated to an ESRI grid using an inverse-distance weighting method, using ESRI Spatial Analyst. The grid was used to create a contour map of magnetic field strength.

T1 values for intraobserver reproducibility assessment; Excel data with semiautomatic ROI placement by observer 1.

This collection of 9-second raster data was compiled for use in modelling biodiversity pattern by developers engaged in supporting the New South Wales Biodiversity Indicators Program. Substrate and landform data derive from existing collections and have been altered from their native format to fill missing and erroneous data gaps as described in the lineage. Climate data were derived using existing methods as described in the lineage. Masks derived or adopted for use in processing the data are included in this collection. Data are supplied in ESRI float grid format, GCS GDA94 Geographic Coordinate System Geocentric Datum of Australia (GDA) 1994.
Lineage: The abiotic environmental data in this collection are grouped by broad type - climate, substrate and landform. Datasets are provided in separate compressed folders (*.zip or *.7z). An excel spreadsheet is included with the collection that list and briefly describes all datasets and their source URLs, and the processing location of the data in the CSIRO project archive. A lineage document summarises the mask and gap filling processes. Mask data were developed from existing spatial boundary data including Australian coastline, State and administration boundaries, and previous raster modelling masks for the NARCLIM region. The data gap filling process was conducted in three stages (python processing scripts are included in this collection). In the first stage, the process used a 10 cell Inverse Distance Weighted (IDW) algorithm to fill no Data areas with data. The IDW algorithm used the distance of data values in the search radius as inverse weights in a neighbourhood average. To deal with remaining larger gaps, a second stage IDW was run on the outputs of the first stage with an increased radius of 500 cells. Any remaining data gaps were filled with a global data average. This process of data filling may make the data unsuitable for other uses and should be carefully considered before use. Images of each dataset are provided in the collection for ease of reference. Data are supplied in ESRI float grid format, GCS GDA94 Geographic Coordinate System Geocentric Datum of Australia (GDA) 1994.

Nigeria adopted dolutegravir (DTG) as part of first line (1L) antiretroviral therapy (ART) in 2017. However, there is limited documented experience using DTG in sub-Saharan Africa. Our study assessed DTG acceptability from the patient’s perspective as well as treatment outcomes at 3 high-volume facilities in Nigeria. This is a mixed method prospective cohort study with 12 months of follow-up between July 2017 and January 2019. Patients who had intolerance or contraindications to non-nucleoside reverse-transcriptase inhibitors were included. Patient acceptability was assessed through one-on-one interviews at 2, 6, and 12 months following DTG initiation. ART-experienced participants were asked about side effects and regimen preference compared to their previous regimen. Viral load (VL) and CD4+ cell count tests were assessed according to the national schedule. Data were analysed in MS Excel and SAS 9.4. A total of 271 participants were enrolled on the study, the median age of participants was 45 years, 62% were female. 229 (206 ART-experienced, 23 ART-naive) of enrolled participants were interviewed at 12 months. 99.5% of ART-experienced study participants preferred DTG to their previous regimen. 32% of particpants reported at least one side effect. “Increase in appetite” was most frequently reported (15%), followed by insomnia (10%) and bad dreams (10%). Average adherence as measured by drug pick-up was 99% and 3% reported a missed dose in the 3 days preceding their interview. Among participants with VL results (n = 199), 99% were virally suppressed (

Quantifying lithospheric strength is essential to better understand seismicity in continental regions. In the manuscript “Using Syntectonic Calcite Veins to Reconstruct the Strength Evolution of an Active Low‐Angle Normal Fault, Woodlark Rift, SE Papua New Guinea”, we estimate differential stresses and principal stress orientations that drive rapid slip on the active Mai’iu fault (dipping ~16-24° at the Earth’s surface) in Papua New Guinea. We compile stress-depth snapshots by taking advantage of space-for-time relationships provided by progressive slip localization within the cooling and exhuming footwall of the Mai’iu fault. Estimated differential stresses are based on the mechanical twinning and/or recrystallized grain-size of deformed calcite veins that cross-cut the sequentially formed fault rock units (mylonites, foliated cataclasites, ultracataclasites and gouges). The orientation of principal stresses acting on the fault zone are estimated using stress-inversion techniques on crystallographic data for calcite-twins collected by electron backscatter diffraction (EBSD), and on fault-slip data of late outcrop-scale brittle faults cross-cutting the footwall and hangingwall of the Mai’iu fault. The data repository provides the raw dataset used for the paleostress analyses in this study from which we derive the fault’s peak strength (140–185 MPa) and the integrated strength of the extending brittle crust. The raw dataset includes:

(1) Calcite E-Twin Analysis: 12 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), overview maps (.jpeg) of all analyzed calcite grains, excel-sheets with orientation and twin morphology data on the analyzed calcite grains, and EBSD Euler conversion output files (_out.xlsx; see below); (2) Calcite Grain-Size Piezometer: 8 subfolders with EBSD data on the analyzed calcite veins (.cpr, .crc), multiple overview maps (.png) of the analyzed calcite veins, and grain-size histograms of the relict and recrystallized grains; (3) Calcite Paleostress Analysis: twinning data that was made analogous to fault-slip data (.fdt) and best-fit stress orientations as calculated by the multiple inverse method (.mi4); (4) EBSD Euler Conversion: MATLAB code to calculate slip plane (e-plane) and glide direction from calcite host-twin pairs and to transform EBSD acquired orientation data from a sample reference frame into a geographic system; (5) Mai'iu Fault Structural Data: an excel-sheet with all structural data collected in the Suckling-Dayman Metamorphic Core Complex during the field campaigns in 2014, 2015 and 2016 (includes sample locations, fault-slip data, bedding data of the Gwoira Conglomerates, etc…; version 7, date: 26.09.2016).

All geothermometric data and explanations on how to reproduce the estimated paleostresses (using the provided raw dataset) can be found in the main manuscript. This unique dataset provides insights into the strength and stress evolution of the Woodlark Rift, Papua New Guinea.

Full dataset and supplementary analyses for Lopiccolo and Chang (2021, "Cultural factors weaken but do not reverse left-to-right spatial biases in numerosity processing: Data from Arabic and English monoliterates and Arabic-English biliterates", PLoS ONE). The dataset is provided in Excel format (.xlsx) and tab-delimited text format (.txt): Sheet 1 of the Excel file provides the raw (trial-by-trial) reaction time data; Sheet 2, the reaction time difference data; Sheet 3, the demographic data for all participants; and Sheet 4, a key explaining each column of the data spreadsheets in sheets 1-3. Supplementary analyses of error rates, along with a summary table of raw response times, are provided in the PDF file.

Background and objectivesAim of the current study was to assess the perception, preference, and practice of endodontists and restorative dentists at different locations around the world about dental magnification instruments.Materials and methodsA multicenter, cross-sectional study was ethically approved from the local committee of bioethics. After thorough literature search, a questionnaire was designed and validated. Later, the questionnaire was distributed to 10% (53 participants) of the total planned participants to conduct a pilot study. Based on the feedback from these participants, any ambiguities or discrepancies observed in the items and content of the questionnaire was modified. The questionnaire was assessed for its internal consistency as part of validating the items with Cronbach’s alpha of 0.80. The completed questionnaire with an informed consent form for the participant was administered to the endodontists and restorative dentists in three different geographical regions namely MENA (Middle East and Northern Africa), British-Isles, and Indian Sub-continent using WhatsApp through the snowball convenience sampling technique.ResultsMajority of the participants were male (56.5%) and in the age group of 25–35 years (30.3%). About 68.9% were from Indian sub-continent, followed by the British-Isles (16.5%) and the least (14.6%) were from the MENA region. By large, the participants of the present study, strongly agreed that dental magnification devices improved ergonomics, quality of work, and should be considered as standard of care in modern endodontic. Flip-up magnifiers (51.1%) and medium (8x-16x) magnification were preferred by majority of the participants. About 46.3% of specialist reported that they always used devices for all operative and endodontic procedures, especially while locating hidden and canals and negotiating calcified canals. Participants practicing in British-Isles have 2.42 times (P

Excel sheets provide data, grouped in respective tabs, for Figs 1B and S1A; 1C; 2D; 2E; 3B and S2A; 3C and S2B; 4E and S1D; 5B and S3A; 5D and S3C; 5F and S3D; 6B and S5A; 6C and S5B; 8B and S6A; 8E and S6B; 8F and S6E; S4B; S4D; and S6C. (XLSX)

Excel spreadsheet containing, in separate sheets, the underlying numerical data presented in the manuscript.

Excel file containing compiled primary experimental data subjected to statistical analyses.