Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta

submitted abstract We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).

opencc-by-4.0Feb 2023View details →

Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D reconstruction (UAVID3D)

Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However,  these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area.  To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository.  RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition,  thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and  between 58 and 65 degree fahrenheit respectively.  For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images,  similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories <a href="https://github.com/LBNL-ETA/a3dbr">https://github.com/LBNL-ETA/a3dbr</a>, and <a href="https://github.com/LBNL-ETA/AutoBFE">https://github.com/LBNL-ETA/AutoBFE</a> This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

opencc-by-4.0Apr 2023View details →

A highly magnetized environment in a pulsar binary system

A highly magnetized environment in a pulsar binary system Data availability: <ul> <li>IPV and DM, RM variations used for Fig 1 (Demo how to read the code and plot: plot_Fig1.py)  DM_Icut_60s_140114.txt, DM_Icut_60s_200621.txt,  RM_140114_20s_rmfit.txt,RM_200621_20s_rmfit.txt,  ILV_140114_20s.npy, ILV_200621_20s.npy </li> <li>A full table of DM and RM variations (duplicated with the txt files, but with better readability): Li_DM_RM_Ter5A.xlsx</li> <li>Calibrated full stokes data with frequency information for Fig 2 (in psrfit format, can be read in with standard psrchive commands): VEGAS_Ter5A_200621_0001.zap.E2.calib.rot.f32</li> </ul> Code availability <ul> <li>The basic process of the raw data and DM, RM fitting:</li> </ul> DSPSR (http://dspsr.sourceforge.net) PSRCHIVE (http://psrchive.sourceforge.net) <ul> <li>Modeling and fitting:</li> </ul> https://github.com/dongzili/Pol_prop_fitting NOTICE: If there are updated data due to new demands, the information or new URL will be posted in https://github.com/dongzili/Pol_prop_fitting

opencc-by-4.0Feb 2023View details →

Global OCpetro Oxidation: data, code and environments

A repository of data files, code and python/R environments for the manuscript "Rock organic carbon oxidation CO2 release offsets silicate weathering sink" by Jesse R. Zondervan, Robert G. Hilton, Mathieu Dellinger, Fiona J. Clubb, Tobias Roylands, Mateja Ogrič. This repository contains an Excel file and several zip files. Zip files containing code, data and python environment to run a simulation of the Global OCpetro Oxidation model: <ul> <li>River rhenium (Re) and OCpetro oxidation data: Supplementary Tables.xlsx</li> <li>Code only: Global_OCpetro_Oxidation-v1.1.0.zip (uploaded Github repository)</li> <li>Geospatial data files only: input_global_(data files only).zip</li> <li>Python environment only: ocpetro_oxidation_env.zip</li> <li>Code, data and environment: ocpetro_oxidation_code_data_env.zip</li> </ul> Outputs of the model presented in the manuscript: <ul> <li>Geospatial raster files of Fig 1: <ul> <li>Re sample locations shapefile (panel A): Re_sample_locations.zip</li> <li>Re sample catchment shapefile (panel A): Re_sample_catchments.zip</li> <li>Median OCpetro stocks model (panel B): Output_OCpetro_stock_median.zip</li> <li>Median denudation model (panel C): Output_denudation_median.zip</li> <li>Best-fit OCpetro oxidation extrapolation (panel D): Output_OC_petro_oxidation.zip</li> </ul> </li> </ul> Denudation and OCpetro stock subroutines (for transparency only, not needed to run OCpetro oxidation simulation): <ul> <li>Code, data and R environment (python environment from ocpetro_oxidation_env.zip, see above): Submodels_code_data_Renv.zip</li> </ul> The easiest way to run the code is by downloading the zip file containing code, data and environment, and then unpacking using packages provided by the OS, or by running the 'anaconda-project unarchive' command. Instructions for this can be found by searching for anaconda-project online, or directly via <a href="https://anaconda-project.readthedocs.io/en/latest/user-guide/tasks/create-project-archive.html?highlight=unarchive#extracting-the-archive-file">https://anaconda-project.readthedocs.io/en/latest/user-guide/tasks/create-project-archive.html?highlight=unarchive#extracting-the-archive-file</a> [last accessed 26/01/2023] The code was developed in a python environment detailed in anaconda-project.yml, with every recursive dependency down to the individual build in anaconda-project-locked.yml. The code file is "Glob_newmethod_parr_globalresidual.py" in this repository. This code should be reproducible indefinitely, without depending on online package repositories. Both the commands and the environment have been captured into a fully locked anaconda project with all conda packages unpacked and included using anaconda-project --pack-envs. Note that only packages for running the code on Linux can be unpacked in this way; building on other platforms (Windows, Mac) will still require access to repositories.    Notes: This code was run on an HPC environment with a job submitter called SLURM. As such, the code will run according to a slurm job array with numbers from 1-100 (10,000 monte carlo simulations). The command to run the Monte Carlo simulation as 10,000 seperate jobs is done like this: "sbatch --array=1-10000:1 job_script_file_name.sh". Note that the version of code uploaded here is set to run 100 simulations ("sbatch --array=1-100:1 job_script_file_name.sh"). When running 10,000 simulations, please change line 46 to "quantile = float(os.getenv('SLURM_ARRAY_TASK_ID'))/10000." Whilst it is possible to run this code on a single machine such as a personal computer, the user is warned that it takes 24 core hours per simulation to run. For example, a typical 4-core laptop would need 6 hours to run one simulation. Now calculate how many 10,000 would take... To run one simulation, line 46 ("quantile = float(os.getenv('SLURM_ARRAY_TASK_ID'))/100.") can be replaced with ("quantile = float(number between 0 and 1)"). Outputs will be saved for each simulation, which can rack up a lot of space, unless you specifically put in lines to delete these from the disk, or, in the case of the example job script for HPC usage, exclude the files when moving data from the node that ran the job.   Example: sbatch --array=1-100:1 run_Glob_OCpetro_model.sh #note that this runs 100 simulations. An example of a job script file has been appended. Please note that the details of this job script depend on your machine or HPC system. Please consult your HPC support or platform's (Linux, Mac, Windows) command prompt instructions.

edi44/100

Descriptive data file for information regarding microbial genetic research in the environs of Plum Island Sound watersheds, PIE LTER, Massachusetts.

This is a descriptive, tabular dataset of publications related to microbial or genomic research conducted within PIE. Assession numbers for genetic sequences generated from PIE samples are provided where available, followed by a very brief description of analysis type and study objectives. Sampling locations within PIE, sampling dates, and habitat type (sea water, fresh water, sediment, marsh) are also given. Environmental data are included in some publications and are listed here (if brief) or availability is described. Links to sequence archives are given in Methods.

openCC (other)Jul 2021View details →

opencc-by-4.0Jul 2019View details →

Transgenerational plasticity of inducible defenses: combined effects of grand-parental, parental and current environments - PDF, datasets and R script

PDF, dataset and R script for the paper: Tariel J, Plénet S, & Luquet E. Transgenerational plasticity of inducible defenses: combined effects of grand-parental, parental and current environments.  Two data sets are provided: one for the analysis of the behavior (dataBehaviour.csv) and one for the analysis of other variables (weight, shell thickness and morphology; dataMorphology.csv). There are analyzed in the same R script (Script_TARIEL-Juliette.R) Signification of variables names: G1: treatment/environment of the grand-parental generation (control C or with predator-cue P) G2: treatment/environment of the parental generation (control C or with predator-cue P) G3: treatment/environment of the offspring generation (control C or with predator-cue P) Family: unique ID for each family Individual: number to identify siblings within a family  ID: unique ID for each individual  W: snail total weight (g) Th: shell thickness (mm) L: shell length (mm) l: shell width (mm) Lo: aperture length (mm) lo: aperture width (mm) crawlout : position of the snail (0:  below the water surface; 1: above/on the water surface) Day : day of measurement (crawling-out behaviour was measured three times through three consecutives days d2, d3 and d4)

opencc-by-4.0Jan 2020View details →

"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material

"Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments" video material

opencc-by-4.0Mar 2020View details →

Dataset of biodiversity of the Seine nursery over 20 years in a highly disturbed environment

Estuaries are crucial ecosystems where human activities deeply affect numerous ecological functions. The Seine estuary, located on the French coast of the eastern English Channel, is a very dynamic environment where this conflict between the two contrasting backgrounds exists. The Seine watershed is highly disturbed by human activities due to significant industrial development and high population density. The estuary is a historic fishing ground for brown shrimp (Crangon crangon) and various species of flatfish, among which sole (Solea solea) and plaice (Pleutronectes platessa). However, it is a nursery area for fish and plays a crucial role in the life cycle of many demersal and benthic fish and invertebrates. Here we present a survey dataset of biodiversity in the nursery of the Seine estuary and eastern bay of Seine collected using a beam trawl throughout three periods from 1995 to 2019. IFREMER (the French Institute for the Exploitation of the Sea) implemented scientific cruises on coastal nursery grounds aimed at describing the fish population and give an insight into the ecosystem functioning in these areas. The NOURSEINE survey presented here came to existence in this context. The surveys happen at the start of autumn to maximize the catchability of juvenile fish.  The beam trawl targets mainly benthic and demersal species over a more than 600 square kilometers study area. The dataset includes abundance and densities of 161 species for 634 hauls performed at around 40 stations each year. These data can be used by fishery scientists and ecologists motivated by earlier stage life of commercial species or by the impact of human disturbances, such as harbor developments, on estuarine communities. They can help in understanding how the nursery functions may change through time and potential human disturbances.   Dataset.csv: The data represents the density for the different species encounters in the trawl stations across the 14 years where the NOURSEINE campaign took place.  The table contains 22435 rows and 22 columns. Each row corresponds to the density of a species or individuals of the same size in a given haul, and this separation comes from the sorting operation. After each haul, the content of the trawl is emptied on deck, and a total or partial sorting is carried out depending on the volume and homogeneity of the capture. All species, both fish and benthic, are sorted, identified, counted and weighted. Fishes of commercial value and all others flatfish are measured. Fish’s otoliths are collected on the main commercial fish species (sole, plaice, flounder, dab, pouting, large whiting and European bass) and their age group determined later on in the laboratory. In 1999, the sampling was incomplete for technical reasons, and only commercial invertebrates were sampled (King scallop and lobster). Sorting the capture can be separated into three different steps (See pdf figure attached):                1. Total capture weighting: when the hauls are emptied on the deck, the whole capture is distributed in several baskets/box in order to weight it.                2. Fish and large taxa sorting: All fish and large taxa of invertebrates easily identified (edible crab, common spider crab, large cephalopods) are sorted, identified, numbered, measured (for fish) and weighted (total weight per taxa). Depending on the size of the capture, a subsample might be necessary, and the operation is only performed on it. In case visual identification is too difficult (for instance due to a large mud proportion), the capture may be washed using a 5mm sieve. The weight ratio between the total capture and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a taxon has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density.                3. Benthic fauna sorting: What is left from the second step is weighted before the sorting operation. All taxa constituting benthic fauna are sorted, identified, numbered and weighted (total weight per taxa). Some taxa may be measured (whelk, scallop). Just like step 2, according to the quantity of benthic fauna, a subsample might be necessary before sorting. All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database. All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database.     Description of the columns found in the dataset <ul> <li>Year</li> </ul> The year when the sampling event took place. Ranges between 1995 and 2019. <ul> <li>Month</li> </ul> The month when the sampling event took place. Either 08 (August) or 09 (September). <ul> <li>Day</li> </ul> The day of the month when the sampling event took place. Ranges from 01 to 30. <ul> <li>Boat_Engine_kw</li> </ul> Engine power reflecting the boat used for sampling, given in kilowatt. Ranges between 81 and 552. <ul> <li>Gear_Code</li> </ul> Code describing the fishing gear used during the sampling event. “BT2” corresponds to beam trawl 2 meters and “BT3” is beam trawl 3 meters. <ul> <li>Haul_duration</li> </ul> The number of minutes of the haul operation. For 1250 rows, NA values indicate that this information was not available. <ul> <li>Station_Code</li> </ul> A unique number identifying the sampling event. Ranges between 1 and 1299. <ul> <li>Starting_Longitude_decimal</li> </ul> The longitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Ending_Longitude_decimal</li> </ul> The longitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Starting_Latitude_decimal</li> </ul> The latitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Ending_Latitude_decimal</li> </ul> The latitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system. <ul> <li>Sector</li> </ul> The sector of the sampling area where the sampling event took place. The study area is divided in 12 sectors roughly based on bathymetry and distance to the mouth of the estuary and identified by letters from “A” to “M”. <ul> <li>Starting_Depth</li> </ul> The depth registered when the sampling event started. Given in meters, ranges from 2 to 26. For 13 rows, NA values indicate that this information was not available. <ul> <li>Ending_Depth</li> </ul> The depth registered when the sampling event ended. Given in meters, ranges from 2 to 26. For 313 rows, NA values indicate that this information was not available. <ul> <li>Trawled_Distance_m</li> </ul> The distance covered between the starting position and the ending position. Given in meters, ranges from 332 to 3128. <ul> <li>Trawled_Surface_m2</li> </ul> The surface trawled between the starting position and the ending position. Given in square meters, ranges from 963 to 9070. <ul> <li>Scientific_Name</li> </ul> Scientific name of the individuals identified in the haul. All names have been checked on WORMS (last accessed: 11/12/2019). <ul> <li>Number_Measured</li> </ul> The number of individuals measured during the sampling event. Ranges between 1 and 340. <ul> <li>Size</li> </ul> Size recorded during the measure of the individuals’ size. Given in centimeters for all species, except benthic fauna where millimeters are used. Ranges between 1 and 185. <ul> <li>Age_Group</li> </ul> Code giving the age category of a particular individual belongs to. “G0” means the individuals are less than 1 year old and born the year the sampling event took place. “G1” means the individuals are 1 year old and born the year before the sampling event took place. “G2+” means they are 2 years old <ul> <li>Number_in_Haul</li> </ul> The number of individuals counted or estimated in the entire haul (if individuals have not been numbered). Depending on the size of the capture, a subsample might be necessary. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density. <ul> <li>Weight_in_Haul</li> </ul> The weight of individuals counted or estimated in the entire haul (if individuals have not been numbered). Given in gram. Depending on the size of the capture, a subsample might be necessary. The weight ratio between the total catch and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density. For 7 rows, NA values indicated that this information was not available. <ul> <li>Subsample</li> </ul> Depending on the size of the capture, a subsample might be performed to estimate abundance and weight. This column indicates “Yes” if the capture has been subsample to identified the taxa and “No” if the taxa as identified on the whole catch. <ul> <li>Division</li> </ul> A number used to calculate the density from the “Number_in_Haul” column. It represents the weight ratio between the total capture and the subsample. It ranges between 1 and 512. When it is equal to one, the species was sorted in the whole capture. <ul> <li>Species_Density</li> </ul> The density calculated in individuals per square meters. Species densities are calculated based on the trawled surface but also taking into account if the haul has been partially sorted or not. The formula to calculate the density of individual per surface unit is: Density = (Number_in_Haul * Division) / Trawled_Surface_m2 where Division is a factor used to elevate the abundance if the whole haul was not sorted.

opencc-by-4.0Oct 2019View details →

CURE-OR: Challenging Unreal and Real Environments for Object Recognition

As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (<a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In <a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a> dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives.  Please refer to our <a href="https://github.com/olivesgatech/CURE-OR">GitHub page</a> for code, papers, and more information. Some data specifications are provided below: Image Name Format :  "backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg" Background ID:  1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office Object Orientation ID:  1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top Object ID:  1-100 Challenge Type:  No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise Challenge Level:  A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.

opencc-by-4.0Aug 2020View details →

Connexin-46/50 in a dynamic lipid environment resolved by CryoEM at 1.9 Å

These are the molecular dynamics (MD) data that are analyzed, in Flores et al. 2020. Each trajectory file (.dcd) has an associated structure file (.psf), which can be analyzed in VMD. Each gap junction system, Cx46 & Cx50, were simulated with either KCl or NaCl in the intracellular space: Cx46_KCl/NaCl, Cx50_KCl/NaCl. All four systems were equilibrated for 30ns and then followed up with 2 separate 100ns production runs. For MD lipid-densities the Cx50_KCl system was used as the representative dataset. Lipid densities from all other systems can be calculated with the provided TCL script calc-density.tcl.  In VMD TK-Console: > mol new <system>.psf > mol addfile <system>.dcd waitfor all > source /path/to/scripts/LipNetwork.tcl > align > source /path/to/scripts/calc-density.tcl > dmpcdensity <outname> [output] outname_ltailden.dx Convert from .dx to .mrc using UCSF-Chimera Volume_Viewer plugin. [output] outname_ltailden.mrc Using Relion: $ relion_image_handler --i outname_ltailden.mrc --o outname_ltailden_D6-Sym.mrc --sym D6 [output] outname_ltailden_D6-Sym.mrc   All trajectory files (.dcd) are 100ns longs (1,000 frames x 100ps/frame). For questions regarding MD-data analysis and full trajectory files (2ps/frame), please contact Dr. Steve Reichow (reichow@pdx.edu).

opencc-by-4.0Sep 2020View details →

The Acoustic Environment of York Minster's Chapter House

This repository contains the data set related to the paper “The Acoustic Environment of York Minster's Chapter House”, published in "Acoustics" as part of the "Special Issue Historical Acoustics: Relationships between People and Sound over Time" and available at: DOI: 10.3390/acoustics2010003 This dataset contains the B-format Room Impulse Responses (RIR) in the Waveform Audio File standard Format (.wav) measured and simulated at a selected set of source-receiver combinations in the York Minster's Chapter House, used for the acoustical analysis performed as part of the CATHEDRAL ACOUSTICS project (CA-MRIR-YM-CH and CA-SRIR-YM-CH folders respectively). The .xls spreadsheets (CA-YM-CH-MRIR-ResultData-EnergyParameters and CA-YM-CH-SRIR-ResultData-EnergyParameters) include the results derived for the measured and simulated RIR used for the discussion presented in the manuscript. LICENCE.txt, METADATA.txt and README.txt contain a brief description of the folder contents, authors, other useful information. Details on the acoustic measurement campaigns and simulations can be found in the manuscript. Please cite both the paper and dataset if used. -------------------------------------------------- This work is license under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). (see https://creativecommons.org/licenses/by-nc-sa/4.0/) -------------------------------------------------- Dataset curated by Lidia Álvarez-Morales, Theatre, Film, Television and Interactive Media Department, University of York. Contact: lidia.alvarezmorales@york.ac.uk; lidiaalvarezmorales@gmail.com ------------------------------------------------- Funding was provided by the European Union’s Horizon 2020 research and innovation programme (http://dx.doi.org/10.13039/501100007601) under the Marie Sklodowska-Curie grant agreement No 797586

opencc-by-4.0Sep 2020View details →

A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids - Dataset

This is the umbrella archive that contains all the datasets and code associated with: A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids     Vito RT Zanotelli     Matthias Leutenegger     Xiao‐Kang Lun     Fanny Georgi     Natalie de Souza     Bernd Bodenmiller Mol Syst Biol. (2020) 16: e9798 <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a> Please cite this article if you re-use any of the data or code. Datasets: <ul> <li>Brightfield plate images: Contains all plate acquisitions for the individual sphere plates before pooling <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991929">10.5281/zenodo.3991929</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991931">10.5281/zenodo.3991931</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991925">10.5281/zenodo.3991925</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991927">10.5281/zenodo.3991927</a>  </li> </ul> </li> </ul> </li> <li>Slidescan images: Contains all fluorescent slidescan images of the cuts derived from the pooled spheroid sample blocks <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991921">10.5281/zenodo.3991921</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991923">10.5281/zenodo.3991923</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.4066430">10.5281/zenodo.4066430</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991919">10.5281/zenodo.3991919</a>  </li> </ul> </li> </ul> </li> <li>Spillover acquisitions: Contains all spillover acquisitions associated with the two experiments: <ul> <li>4 cellline experiment: <ul> <li>p173/p176: <a href="http://doi.org/10.5281/zenodo.3991945">10.5281/zenodo.3991945</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161/p165: <a href="http://doi.org/10.5281/zenodo.3991947">10.5281/zenodo.3991947</a>  </li> </ul> </li> </ul> </li> <li>Imaging mass cytometry acquisitions: <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991937">10.5281/zenodo.3991937</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991939">10.5281/zenodo.3991939</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991933">10.5281/zenodo.3991933</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991935">10.5281/zenodo.3991935</a>  </li> </ul> </li> </ul> </li> <li>Processed data set: <ul> <li>4 cellline experiment: <a href="https://doi.org/10.5281/zenodo.3991942">10.5281/zenodo.3991942</a></li> <li>Overexpression experiment: <a href="http://doi.org/10.5281/zenodo.4271917">10.5281/zenodo.4271917</a></li> </ul> </li> </ul> Code: A snakemake pipeline to reproduce the analyses from this raw data can be found at: <a href="http://github.com/BodenmillerGroup/SpheroidPublication">http://github.com/BodenmillerGroup/SpheroidPublication</a> A version already containing all the containers can be found at: <a href="http://doi.org/10.5281/zenodo.4071861">10.5281/zenodo.4071861</a>

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The next frontier: Human settlements in the marine environment

Human settlements have impacted most terrestrial environments on Earth. With a rapidly growing global population, surging demands for resources, and unprecedented alteration to Earth's ecosystems, the conservation of our only suitable habitat is a contemporary zeitgeist. Moreover, humanity's nature for enterprise and exploitation continuously pushes the frontiers of exploration, while the governance associated with expansion beyond terra firma is uncertain and rife with conflict. Though the notion of human expansion into the oceans is becoming more orthodox, we have largely overlooked Earth's oceans as space for human habitat. Here, we build on the case for humanity's future over the marine environment and discuss how exploring this frontier will result in advances for industries, economies, and exploration. The "Blue Acceleration" could transform the nascent phenomenon of marine cities into reality, and we support this notions with a global marine spatial plan to assess the suitability of marine settlements. Our analysis shows that every oceanic basin supports regions for sustainable human settlements. More broadly, we discuss the implications of a human-marine frontier and the benefits that will ensue for science, humanity and our planet. Finally, we argue that the human-marine frontier should be an immediate steppingstone for future exploration, innovation and discovery.

opencc-zeroSep 2020View details →

opencc-by-4.0Oct 2020View details →

Dataset for semantic segmentation of the laboratory model of manufacturing environment

This dataset includes images and labels used for semantic segmentation of the laboratory model of the manufacturing environment, at the University of Belgrade - Faculty of Mechanical Engineering. The dataset is gathered by using mobile robot RAICO (Robot with Artificial Intelligence based COgnition) and its stereo visual system made from two Basler acA1920-25uc cameras with Fujinon lens DF6HA-1B. The dataset includes close to 430 images with a resolution of 640x360. Images are acquired by both cameras at different mobile robot poses in the laboratory model of a manufacturing environment. Five classes are introduced in the dataset, machines 1 to 4, and a background class. The exact names of the classes are: classNames = ["Machine_1", "Machine_2", "Machine_3", "Machine_4", "Background"]; while the labels of the classes (RGB values of label images) are: labelIDs = [ ...     000 000 255; ... % "Machine 1"     000 255 255; ... % "Machine 2"     255 255 000; ... % "Machine 3"     255 000 000; ... % "Machine 4"     255 255 255; ...   % "Background"     ]; Image and label pairs are entitled 1 to 430, and e.g. label 5 corresponds to images 5. This dataset was developed with the support of the Science Fund of the Republic of Serbia, Grant No. 6523109, AI - MISSION4.0, 2020-2022.

Very high rate GPS observables from UCOE, UJUR, TNGF and OXUM continously operating stations, 17-19 Feb 2020. Dataset for the study "Assesment of Morelian meteoroid impact on Mexican environment" Part 3

Archived data is Trimble raw T02 format from Trimble NetR9 receivers. Complete metadata for the stations can be obtained at the TLALOCNet archive at http://tlalocnet.udg.mx. This dataset includes high rate (5 and 50hz) versions of  the UCOE, UJUR, TNGF and OXUM continous GPS station observables that is otherwise available as a 15 sec recording on the TLALOCNet archive (http://tlalocnet.udg.mx). Any use of this dataset or parts of it must give the proper acknowledgement , including the Zenodo assigned DOI as follows: This material is based on GPS data provided by the Trans-boundary, Land and Atmosphere Long-term Observational and Collaborative Network (TLALOCNet; Cabral-Cano et al., 2018) jointly operated by Servicio de Geodesia Satelital (SGS) at the Instituto de Geofísica-Universidad Nacional Autónoma de México in collaboration with and the GAGE facility operated by UNAVCO Inc. We gratefully acknowledge Armando Carrillo-Vargas and all the personnel from SGS and UNAVCO Inc. for station maintenance, data acquisition, IT support and data curation and distribution. TLALOCNet and related GPS operations at SGS are supported by the Consejo Nacional de Ciencia y Tecnología (CONACyT) projects 253760, 256012 and 2017-01-5955, UNAM-Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT) projects IN104213, IN111509, IN109315-3, IN104818-3, NSF grant 2025104 and supplemental support from UNAM-Instituto de Geofísica. UNAVCO's initial support for TLALOCNet (now part of NOTA) was performed under EAR-1338091 and is currently supported by the National Science Foundation and the National Aeronautics and Space Adminsitration under NSF Cooperative Agreement EAR-1724794.

opencc-by-4.0Oct 2020View details →

Data and analysis scripts associated with the paper 'Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history''

Eva Bons, Christine Leemann,  Karin J. Metzner, Roland R. Regoes This repository contains all the data and analysis scripts associated with the paper 'Long-term experimental evolution of HIV-1 reveals effects of environment and mutational history' See the readme after unpacking the .zip for a description of the files

Figs 41–50 in New fossil genus and new extant species of diatoms (Stephanodiscaceae, Bacillariophyceae) from Pleistocene sediments in the Neotropics (Guatemala, Central America): adaptation to a changing environment?

Figs 41–50. Discostella gabinii Paillès & Sylvestre sp. nov., Lake Petén-Itzá (Guatemala); LM valve views. 41–42. Modern specimens of D. gabinii sp. nov. from Cenote Juarez. 43–44. Modern specimens of D. gabinii sp. nov. from Lake Amatitlan. 45–50. Type material of fossil lacustrine diatom D. gabinii sp. nov. 45. Holotype (MNHN, slide PC060873). 48–50. A shadow line is visible in large specimens. Scale bar = 10 µm.

Figs 25–32 in New fossil genus and new extant species of diatoms (Stephanodiscaceae, Bacillariophyceae) from Pleistocene sediments in the Neotropics (Guatemala, Central America): adaptation to a changing environment?

Figs 25–32. Type material of fossil lacustrine diatom Cyclocostis rolfii Paillès gen. et sp. nov.; SEM external valve views. 25. Valve view of concentrically undulated marginal area and tangentially undulated central area; punctuated striae become in the central area rows of larger areolae arranged in a stellate pattern. 26. Valve surface with scattered papillae; the external opening of the single valve face fultoportula is located on the raised part (white arrowhead). 27. Marginal area showing the external openings of marginal fultoportulae, collared but with no projections (white arrowheads). 28. Side view of marginal area showing striation, papillae, external openings of marginal fultoportulae (mfp – two white arrows), and the cingulum consisting of an open valvocopula and several copulae (white arrow). 29. Detail of the central area with large areolae; external areolae are bigger and occluded by volae in places where ribs are fusing. 30. Broken valve view showing the different striation between the margins and the center, the steep transversal undulation and the valve thickness. 31. Marginal area with the external openings of marginal fultoportulae (white arrowheads), papillae, and the cingulum. 32. Broken valve view showing the simple structure of anastomosing ribs covered by a finely perforated silica layer. Scale bars: 25 = 5 µm; 26, 28–32 =2 µm; 27 = 1 µm (27).

Fig. 59 in New fossil genus and new extant species of diatoms (Stephanodiscaceae, Bacillariophyceae) from Pleistocene sediments in the Neotropics (Guatemala, Central America): adaptation to a changing environment?

Fig. 59. Diagram showing the succession of Stephanodiscaceae Glezer & Makarova in Pleistocene sediments (0–84 ka) from Lake Petén-Itzá (Guatemala).

Figs 1–24 in New fossil genus and new extant species of diatoms (Stephanodiscaceae, Bacillariophyceae) from Pleistocene sediments in the Neotropics (Guatemala, Central America): adaptation to a changing environment?

Figs 1–24. Type material of fossil lacustrine diatom Cyclocostis rolfii Paillès gen. et sp. nov., Lake PeténItzá (Guatemala); LM girdle view (1) and valve views (2–24). 4. Holotype (MNHN, slide PC0608731). 7–8. Valve surface strongly tangentially undulated, forming an S shape. Scale bar = 10 µm.