Uploaded datasets are detailed exposure information (chemical concentrations and water quality parameters) for exposures conducted in a flow through diluter system with larval Pimephales promelas to four different pyrethroid pesticides. The GEO submission URL links to the NCBI GEO database and contains gene expression data from whole larvae exposed to different concentrations of the pyrethroids across multiple experiments. This dataset is associated with the following publication: Biales, A., M. Kostich, A. Batt, M. See, R. Flick, D. Gordon, J. Lazorchak, and D. Bencic. Initial Development of a Multigene Omics-Based Exposure Biomarker for Pyrethroid Pesticides. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY. CRC Press LLC, Boca Raton, FL, USA, 179(0): 27-35, (2016).

Astronauts are exposed to a unique combination of stressors during spaceflight, which leads to alterations in their physiology and potentially increases their susceptibility to infectious pathogens. Here we report the first microarray evaluation of any astronaut tissue sample, specifically whole blood, before and after spaceflight using an array comprising 234 well-characterized stress response genes. Differentially regulated genes included those important for DNA repair, oxidative stress, and protein folding/degradation. Microarrays comprising 234 well characterized stress-related genes were used to profile transcriptomic changes in six astronauts before and after short-duration spaceflight. Blood samples were collected for analysis from each eastronaut 10 days prior and 2-3 hours after return from spaceflight. Data submitted for platform GPL140 contain genes that have been pre-filtered by the analytical software to remove values of low certainty, resulting in missing values for some samples. Unfortunately, these original data are no longer available due to physical damage at Tulane University during hurricane Katrina, but the processed values were retained in redundant locations and these are submitted for upload to GEO.

Conventional Environmental Risk Assessment (ERA) of pesticide pollution is based on soil concentrations and apical endpoints, such as the reproduction of test organisms, but disregards information along the organismal response cascade leading to an adverse outcome. The Adverse Outcome Pathway (AOP) framework, on the other hand, facilitates the use of response information at any level of biological organization. Transcriptomic and proteomic data can provide thousands of data points on the response to toxic exposure. Combining multiple omics data types is necessary for a comprehensive overview of the response cascade and, therefore, AOP development. However, it is unclear if transcript and protein responses are synchronized in time or time-lagged. To understand if analysis of multi-omics data obtained at the same timepoint reveals one synchronized response cascade, we studied time-resolved shifts in gene transcript and protein abundance in the springtail Folsomia candida, a soil ecotoxicological model, after exposure to the neonicotinoid insecticide imidacloprid. We analyzed transcriptome and proteome data every 12 hours up to 72 hours after onset of exposure. The most pronounced shift in both transcript and protein abundances was observed after 48 hours of exposure. Moreover, cross-correlation analyses indicate that most genes displayed the highest correlation between transcript and protein abundances without a time-lag. This demonstrates that a combined analysis of transcriptomic and proteomic data can be used for AOP improvement. This data will promote the development of biomarkers for neonicotinoid insecticide pollution in soils or chemicals with a similar mechanism of action. Methods Please refer to a complete description of the methods to our peer-reviewed article. This upload is only for the proteomics data. For test soil, natural LUFA2.2. was used with or without imidacloprid. RNA and protein were sent to other facilities for further processing. Pools of 70 Folsomia candida were exposed to test soil and harvested every 12 hours for a total of 72 hours. The protein and RNA fractions from these animals were isolated using a TriZol-based method. Proteomic data: Shotgun LC-MS2 (Thermo-Fisher Orbitrap), searchGUI with msgf+*, PeptideShaker*, label-free quantification moFF*, further analysis with R-Msnbase, R-limma, R-MSqRob. * these steps have been performed on the EU Galaxy server.

Transcriptomics data have been uploaded to the NCBI GEO database (GSE220513) and Zenodo.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. This is a virtual dataset as the original is too large to upload to the registry. It may be accessed via the State of Victoria www.data.vic.gov.au Part of the Vicmap Features of Interest dataset series. This layer is derived from the Register of Geographic Names. Named locations described in this layer include town names, buildings/structures and place names in general.These locations are stored as named points. The layers primary function is to support production of map annotation and as a general reference for localities. The Register is the primary reference for official names and their applications. The Register holds the status of names (e.g. official; official alternative; official historical; etc). To provide the official legal name of a place or feature or asset as in section 18 of the Geographic Place Names Act 1998. ## Purpose To provide accurate locations to named features of interest in Victoria ## Dataset History This data was derived from the Register of Geographic Names. Description: 1:25 000 maps (approx. 100m accuracy); 1:100 000 maps (approx. 1000m accuracy) Determination: Comparison to independent source Vertical Accuracy (m): N/A (no height data maintained) ## Dataset Citation Victorian Department of Environment, Land, Water and Planning (2016) Vicmap towns, virtual dataset.. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/bc953598-7bb2-4667-8ff2-e2849234b82d.

Questions and Answers are organised in a tabular fashion. The questions act as titles of the columns. Recorded answers are text-based, nominal, and ordinal. The theme of the questions enable the assessment awareness of FAIR principles and data management practices for early career scientists (PhDs) in geography. Some questions were reused from related surveys with permission. No data was collected to identify the individual. Contact details for follow up interviews were removed from the questionnaire results prior to the upload of the dataset.

This dataset provides information on non-compliance of imported food and related products.

This dataset compares four cities FIXED-line broadband internet speeds: - Melbourne, AU - Bangkok, TH - Shanghai, CN - Los Angeles, US - Alice Springs, AU

ERRATA: 1.Data is for Q3 2020, but some files are labelled incorrectly as 02-20 of June 20. They all should read Sept 20, or 09-20 as Q3 20, rather than Q2. Will rename and reload. Amended in v7.

1. LAX file named 0320, when should be Q320. Amended in v8.

*lines of data for each geojson file; a line equates to a 600m^2 location, inc total tests, devices used, and average upload and download speed - MEL 16181 locations/lines => 0.85M speedtests (16.7 tests per 100people) - SHG 31745 lines => 0.65M speedtests (2.5/100pp) - BKK 29296 lines => 1.5M speedtests (14.3/100pp) - LAX 15899 lines => 1.3M speedtests (10.4/100pp) - ALC 76 lines => 500 speedtests (2/100pp)

Geojsons of these 2* by 2* extracts for MEL, BKK, SHG now added, and LAX added v6. Alice Springs added v15.

This dataset unpacks, geospatially, data summaries provided in Speedtest Global Index (linked below). See Jupyter Notebook (*.ipynb) to interrogate geo data. See link to install Jupyter.

** To Do Will add Google Map versions so everyone can see without installing Jupyter. - Link to Google Map (BKK) added below. Key:Green > 100Mbps(Superfast). Black > 500Mbps (Ultrafast). CSV provided. Code in Speedtestv1.1.ipynb Jupyter Notebook. - Community (Whirlpool) surprised [Link: https://whrl.pl/RgAPTl] that Melb has 20% at or above 100Mbps. Suggest plot Top 20% on map for community. Google Map link - now added (and tweet).

** Python melb = au_tiles.cx[144:146 , -39:-37] #Lat/Lon extract shg = tiles.cx[120:122 , 30:32] #Lat/Lon extract bkk = tiles.cx[100:102 , 13:15] #Lat/Lon extract lax = tiles.cx[-118:-120, 33:35] #lat/Lon extract ALC=tiles.cx[132:134, -22:-24] #Lat/Lon extract

Histograms (v9), and data visualisations (v3,5,9,11) will be provided. Data Sourced from - This is an extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).

**VERSIONS v.24 Add tweet and google map of Top 20% (over 100Mbps locations) in Mel Q322. Add v.1.5 MEL-Superfast notebook, and CSV of results (now on Google Map; link below). v23. Add graph of 2022 Broadband distribution, and compare 2020 - 2022. Updated v1.4 Jupyter notebook. v22. Add Import ipynb; workflow-import-4cities. v21. Add Q3 2022 data; five cities inc ALC. Geojson files. (2020; 4.3M tests 2022; 2.9M tests)

Melb 14784 lines Avg download speed 69.4M Tests 0.39M

SHG 31207 lines Avg 233.7M Tests 0.56M

ALC 113 lines Avg 51.5M Test 1092

BKK 29684 lines Avg 215.9M Tests 1.2M

LAX 15505 lines Avg 218.5M Tests 0.74M

v20. Speedtest - Five Cities inc ALC. v19. Add ALC2.ipynb. v18. Add ALC line graph. v17. Added ipynb for ALC. Added ALC to title.v16. Load Alice Springs Data Q221 - csv. Added Google Map link of ALC. v15. Load Melb Q1 2021 data - csv. V14. Added Melb Q1 2021 data - geojson. v13. Added Twitter link to pics. v12 Add Line-Compare pic (fastest 1000 locations) inc Jupyter (nbn-intl-v1.2.ipynb). v11 Add Line-Compare pic, plotting Four Cities on a graph. v10 Add Four Histograms in one pic. v9 Add Histogram for Four Cities. Add NBN-Intl.v1.1.ipynb (Jupyter Notebook). v8 Renamed LAX file to Q3, rather than 03. v7 Amended file names of BKK files to correctly label as Q3, not Q2 or 06. v6 Added LAX file. v5 Add screenshot of BKK Google Map. v4 Add BKK Google map(link below), and BKK csv mapping files. v3 replaced MEL map with big key version. Prev key was very tiny in top right corner. v2 Uploaded MEL, SHG, BKK data and Jupyter Notebook v1 Metadata record

** LICENCE AWS data licence on Speedtest data is "CC BY-NC-SA 4.0", so use of this data must be: - non-commercial (NC) - reuse must be share-alike (SA)(add same licence). This restricts the standard CC-BY Figshare licence.

** Other uses of Speedtest Open Data; - see link at Speedtest below.

BackgroundCD4+ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4+ memory T cells in gastric cancer (GC).MethodsThree datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4+ memory T cell related module (CD4+ MTRM) which was most significantly associated with CD4+ memory T cell. Univariate Cox analysis was used to screen prognostic CD4+ memory T cell-related genes (CD4+ MTRGs) in CD4+ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4+ MTRGs.ResultWe observed that a high abundance of CD4+ memory T cells was associated with better survival in GC patients. CD4+ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4+ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4+ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4+ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4+ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697–0.763). Calibration curves and DCA presented high credibility for the OS nomogram.ConclusionWe identified three molecule subtypes, ten CD4+ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.