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Small example Eiger 2X 16M data set from Diamond Light Source I04 revised for HDRMX Gold Standard Discussion
<p>Revised useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. Data recorded is from a thaumatin crystal by Graeme Winter, The original dataset is <a href="https://zenodo.org/record/3385862">https://zenodo.org/record/3385862</a> which contains two Eiger MX datasets, Therm_6_1 and Them_6_2, each with a data file and two versions each of the metadata -- a "..._master.h5" file and a "....nxs" file. The former are the usual Eiger metadata files using exposed external links to connect the metadata to the date, and the latter are HDF5-1.10 VDS files. This revision has the same data as the original Therm_6_2 data, but now includes with the "master.h5" file a "..._master_rev.h5" file and with the ".nxs" file a "..._rev.nxs" VDS file.</p> <p>The purpose to the changes in the "..._rev" files is to provide a supporting example for the HDRMX discusssion of a new proposed Eiger "gold standard" to improve the ability to process Eiger MX data collected at one facility at other facilties, by ensuring that sufficient metadata is stored with all datasets.</p> <p>The changes were made by the following script</p> <pre>cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 export LD_LIBRARY_PATH=$HOME/lib export HDF5_PLUGIN_PATH=$HOME/lib export PATH=$HOME/bin:$PATH h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/name -d /entry/instrument/name -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/source -d /entry/source -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_rev.nxs -s /entry/instrument/source -d /entry/source -f ref export end_time=`h5dump -d "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py import numpy as np import os end_time=os.environ['end_time'] fvds = h5py.File('Therm_6_2_rev.nxs','r+') fmaster = h5py.File('Therm_6_2_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() fvds_entry_instrument_name=(fvds['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] fvds.close() fmaster.close() quit() EOL $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_master_rev.h5 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_rev.nxs </pre> <p>The revised cnxvalidate and definitions are available on github</p> <p><a href="https://github.com/HDRMX/cnxvalidate.git">https://github.com/HDRMX/cnxvalidate.git</a></p> <p><a href="https://github.com/HDRMX/definitions.git">https://github.com/HDRMX/definitions.git</a></p> <p> </p> <p> </p>
Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"
<p>Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (<a href="https://doi.org/10.1016/j.electacta.2019.135022">10.1016/j.electacta.2019.135022</a>).</p>
Human sequence alignment data set used for analysis of SPDI algorithm and tools
<p>Collection of alignment segments produced on October 30, 2019. The ADS currently consists of over 2,680,000 pairwise alignment segments generated from over 350,000 distinct input sequences. </p> <ul> <li> <p>Old assembly to current Genome Reference Consortium (GRC) <a href="http://f1000.com/work/citation?ids=111899&pre=&suf=&sa=0">(Church et al., 2011)</a> primary assemblies (e.g. GRCh36(hg18) or GRCh37(hg19) with GRCh38(hg38))</p> </li> </ul> <ul> <li> <p>Patches, alternative loci, or pseudoautosomal regions (PAR) to GRC primary assembly</p> </li> <li> <p>RefSeq <a href="http://f1000.com/work/citation?ids=2599029&pre=&suf=&sa=0">(O’Leary et al., 2016)</a> and select GenBank <a href="http://f1000.com/work/citation?ids=6183037&pre=&suf=&sa=0">(Benson et al., 2018)</a> transcripts to selected RefSeq genomic regions, also known as RefSeqGene (NG), a member of the Locus Reference Genome (LRG) collaboration <a href="http://f1000.com/work/citation?ids=3225699&pre=&suf=&sa=0">(Dalgleish et al., 2010)</a>.</p> </li> <li> <p>Current RefSeq transcripts (NM/NR/XM/XR) and RefSeq genomic regions (NG) to the latest Assembly</p> </li> <li> <p>Previous versions of NG and RefSeq transcripts (NM/NR) to GRC primary assembly</p> </li> </ul>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
REINVENT Decarbonisation Innovations Database [Data set]
<p>This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance.</p> <p>For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available <a href="https://static1.squarespace.com/static/59f0cb986957da5faf64971e/t/5d6e590ef37f240001b5ea5d/1567512873422/D2.1+Decarbonisation+innovations+database.pdf">here</a>. </p> <p>The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: <a href="https://www.reinvent-project.eu">www.reinvent-project.eu</a>. </p>
Data set (2016) Bamboo-dominated forest in Amazonia
<p>Ziccardi, Leonardo G, de Alencastro, Paulo M L, Figueiredo, Evandro O, Fearnside, Philip M (2019) A spatial dataset of bamboo-dominated forest mensuration collected in 2016 in southeastern Amazonia, Acre, Brazil. </p>
Data set for "Continuous-variable tomography of solitary electrons" Fletcher et al. DOI: 10.1038/s41467-019-13222-1
<p>Data set used to generate the figures for "Continuous-variable tomography of solitary electrons"</p> <p> </p>
Data set for the manuscript 'Studying the different coupling regimes for a plasmonic particle in a plasmonic trap'
<p>This repository includes data set and Matlab scripts, which support the manuscript entitled 'Studying the different coupling regimes for a plasmonic particle in a plasmonic trap', published in Optics Express. We include the data set necessary to reproduce the results of the paper in the 'RawData.zip' file. We provide Matlab scripts and functions in the 'PostProcessing.zip' file to process the raw data. We also attach HTML documents explaining the data and how we process them. </p> <p><strong>Raw data visualization with python.html</strong>: This is the first HTML file containing all the information to understand and visualize the raw data. It is generated by Jupyter Notebook, and it includes python scripts to visualize the raw data.</p> <p><strong>Post-processing raw data using Matlab.html</strong>: This is the second HTML file, which gives you a guideline to the data processing routines with the explanations of the Matlab scripts and functions. </p> <p><strong>RawData.zip</strong>: the data set used to produce the results in the manuscript. </p> <p><strong>PostProcessing.zip</strong>: Matlab scripts and functions for data post-processing.</p> <p><strong>python.zip</strong>: python files</p> <p>Note: This version update includes the additional data set for the revision of the manuscript. </p>
Data set used in "Diving into science and conservation recreational divers can monitor reef assemblages"
<p>This data set was used in the manuscript "Diving into science and conservation: recreational divers can monitor reef assemblages", accepted for publication in Perspectives in Ecology and Conservation. It is composed by reef species (fishes and turtles) counts in different size classes performed by recreational and experience scientific divers following a sampling protocol. Additionally there is information about recreational divers diving experience, underwater waste register and the scores for their evaluation about conducting scientific data collection.</p>
Data Sets ''Omeprazole and Proteinoids in Neuron Models''
<p>Data Sets ''Omeprazole and Proteinoids in Neuron Models''</p>
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Simulated data set of chimeric transposable elements.
<p>This dataset is composed of 9,000 sequences of transposable elements (TEs) of 20,000 bp. Three cases of transposable elements with artifacts at the ends, with another chimeric TE or with simple repeats were considered for the generation of the sequences. The sequences of the first case consist of a DNA fragment + first TE + DNA fragment + second TE + DNA fragment. The sequences of the second case consist of a first TE + second TE + repeat of the first TE. The third case sequences consist of a microsatellite that is repeated in tandem by placing the extracted TE at position 10,000 (in the middle), occupying both sides to the ends. All TEs used in this dataset were taken from Dfam, for the species Drosophila melanogaster.</p> <p>The identifier of each sequence has the information about the case, TE Dfam identifier, TE initial position inside the sequence, and the TE length, all separated by "_". For example:</p> <p>Caso1_DF000001548.2_6926_5126</p> <p>File description:</p> <p>dataset.zip: The fasta file containing all the sequences</p> <p>features_data.npy.zip: A numpy file containing the numerical representation of the four TE+Aid plots generated for the sequences presented in the dataset.zip file. This data is actually a numpy array with dimensions 9000x256x256x3x4</p> <p>labels_data.numpy.zip: A numpy file containing the starting and ending position (normalized between 0 and 1) of each TE presented in the dataset.zip file.</p> <p>The last two files were generated to train a neural network for trimming out automatically artifacts in DNA sequences.</p>
Applicability of the inverse dispersion method to measure emissions from animal housings - data set & R scripts
<h2>Data availability</h2> <p>Provided are:<br>- raw data of the instruments<br>- R Scripts to reproduce the findings in the publication<br>- R outputs</p> <h2>Scripts</h2> <p>In total, there are 10 scripts provided, of which most of them are needed to reproduce the data in the publication.</p> <p>Below, a brief explanation of the content of the different scripts.</p> <ul> <li>01_Datatreatment_01_Weatherstation.r ## This script reads in the weather station data and makes it ready for further use.</li> <li>01_Datatreatment_02_Sonics.r ## This script reads in the 3D ultrasonic data and makes it ready for further use.</li> <li>01_Datatreatment_03_GasFinder.r ## This script reads in the GasFinder data and makes it ready for further use.</li> <li>01_Datatreatment_04_MFC_Pressuresensor.r ## This script reads in the mass flow controller (MFC) and pressure sensor data and makes it ready for further use.</li> <li>02_Calculation_01_bLS.r ## This script is made to run the bLSmodelR and tailored to the number cruncher of the University of Applied Sciences BFH. The code should also work on your computer but you have to adopt the number of cores.</li> <li>02_Calculation_02_Concentration.r ## This script treats the unprocessed concentration data. It removes false concentrations, applies an intercalibration, and makes the data ready for further use.</li> <li>02_Calculation_03_Emissions.r ## This script calculates emissions and makes it ready for further use.</li> <li>02_Calculation_04_contourXYZ_Plume.r ## This script calculates the plume contours in the XY and XZ plane. This script is not necessary to reproduce the findings of the publication.</li> <li>03_Apply_filter.r ## This script applies the quality filtering and makes the data ready for further use.</li> <li>04_Plots_Tables.r ## With this script one can recreate all the plots and values in the tables of the publication, the supplement, and the initial submission.</li> </ul> <p>Note, for the geometry, there is no script provided. The coordinates of the different sensors and the source are solely provided as R output.</p> <h3>Naming of instruments</h3> <p>The instruments in the publication have different names than in the scripts. In some scripts the final names are also provided but throughout the evaluation the original device names are used. Only in the script 04_Plots_Tables.r are the final names introduced. Below is an overview of what original name corresponds to the final name of the devices:</p> <h4><strong>GasFinder instruments called 'OP' in the publication</strong></h4> <ul> <li>OP-UW = GF26</li> <li>OP-2.0h = GF17</li> <li>OP-5.3h = GF18</li> <li>OP-6.8h = GF16</li> <li>OP-12h = GF25</li> </ul> <p><strong>3D ultrasonic anemometer instruments called 'UA' in the publication</strong></p> <ul> <li>UA-UW = SonicC</li> <li>UA-2.0h = SonicA</li> <li>UA-5.3h = Sonic2</li> <li>UA-6.8h = SonicB</li> </ul> <p><strong>Source</strong><br>In some of the scripts, the source might be called 'Schopf' which is a local term for 'shed'.</p> <h2>Note</h2> <p>This code was written by Marcel Bühler (minor code chunks were originally written by Christoph Häni) and is intended to reproduce the findings of the linked publication. Please feel free to use and modify it (e.g., use it to run different dispersion models), but attribution is appreciated.</p> <h2>Disclaimer</h2> <p>I do not guarantee that everything works. It might be that not all variables were changed to English for better understanding correctly. Unfortunately, it is not possible to provide all the catalogs of the bLS run, as the total size is several 100s of GB. In case you run the bLS model on your own, the result will have a minimal difference, as no bLS run produces the same result twice. This should, however, not alter the findings.</p> <h2>Contact</h2> <p>In case you have questions, please contact Marcel Bühler (mb@bce.au.dk). In case this does not work, Christoph Häni might also be able to help (christoph.haeni@bfh.ch).</p>
Out of the blue: Detection of a unique highly pathogenic avian influenza virus of subtype H7N5 in Germany: Data sets on phylogenetic analyses
<div> <p><span>In June 2024, a highly pathogenic avian influenza virus of subtype H7N5 was detected in a single laying hen holding in Germany. Closest relatives of the eight viral genome segments were among recent low pathogenic (LP) viruses from Asia and Europe. No further detections of this unique virus or its presumed LP precursor have been made in poultry or wild birds.</span></p> </div>
Experimental raw data sets associated with certified reference material BAM-P115 (titanium dioxide) for comparison of nitrogen and argon sorption, available in the universal adsorption information format (AIF)
<p>These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277.<br>The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin.<br>It is recommended to use the following requirements for the molecular cross-sectional area:<br>Nitrogen: 0.1620 nm²<br>Argon: 0.1420 nm²</p> <p>Expected specific surface area for nitrogen (BET): 140 to 154 m²/g<br>Expected specific surface area for argon (BET): 129 to 135 m²/g</p> <p>Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material.<br>The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon.<br>These data are stored in the universal AIF format (adsorption information format), which allows flexible use of the data.</p>
Experimental raw data sets associated with certified reference material BAM-P114 (titanium dioxide) for comparison of nitrogen and argon sorption, available in the universal adsorption information format (AIF)
<p>These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277.<br>The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin.<br>It is recommended to use the following requirements for the molecular cross-sectional area:<br>Nitrogen: 0.1620 nm²<br>Argon: 0.1420 nm²</p> <p>Expected specific surface area for nitrogen (BET): 24 to 25 m²/g<br>Expected specific surface area for argon (BET): 20 m²/g</p> <p>Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material.<br>The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon.<br>These data are stored in the universal AIF format (adsorption information format), which allows flexible use of the data.</p>
Data Set for Nickel-Catalyzed Enantio- and Diastereoselective Synthesis of Fluorine-Containing Vicinal Stereogenic Centers
<p>Raw NMR, HPLC and (HR)MS data for the article entitled "Nickel-Catalyzed Enantio- and Diastereoselective Synthesis of Fluorine-Containing Vicinal Stereogenic Centers" published in ACS Central Science. Folder names in "Characterizations" correspond to compound names as given in the article and/or supporting information. </p>
Data set from the design of experiment assessment rubric, from university students / future teachers
<p>Using an assessment rubric comprising three levels of success, we evaluated six aspects of designing an experiment, which we call dimensions. This way, specific challenges were identified in two of the dimensions: forming a hypothesis and manipulating the experimental variables, where students often failed to succeed and get lower scores.</p> <p>The data presented here were collected from four (4) diagnostic worksheets, which were made available to students at the beginning of the semester, where a theoretical introduction to the course is given, with reference to learning theories and their use in the teaching of Physics. Therefore, data were collected prior to discussions about the inquiry-based approach, experimental investigations, and the scientific content, such as the concepts and phenomena mentioned in the worksheets. </p> <p>For the present study, we slightly modified and used a rubric that has been proposed to assess the DoE by primary and secondary school students (Lefkos et al., 2011). The modified rubric, has already been tested in a pilot study with fruitful results (Lefkos, 2024).</p> <p>Lefkos, I. (2024). An Assessment Rubric for Future Teachers’ Ability to Design Experiments. In C. Fazio & P. Logman (Eds.), <em>Challenges in Physics Education</em> (pp. 105–117). Springer. https://doi.org/10.1007/978-3-031-48667-8_7</p> <p>Lefkos, I., Psillos, D., & Hatzikraniotis, E. (2011). Designing experiments on thermal interactions by secondary-school students in a simulated laboratory environment. <em>Research in Science & Technological Education</em>, <em>29</em>(2), 189–204. https://doi.org/10.1080/02635143.2010.533266</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
Summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI WGS data set
<p>These are the summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI Whole-Genome Sequencing data set from the European subcohort (n=1,017).</p> <p>Case - control definitions:<br>Ex (file: DeCOI_EUR_Ex_RVAS.tsv.gz): 272 cases (required mechanical ventilation or died because of COVID-19 = WHO score 6-10); 362 controls (not hospitalized = WHO score 1-3)<br>B1 (file: DeCOI_EUR_B1_RVAS.tsv.gz): 655 cases (at least hospitalized = WHO score 4-10); 362 controls (not hospitalized = WHO score 1-3)</p> <p>Column description:<br>CHROM: Chromosome of the respective gene<br>ID: Column of the format: [Ensemble Gene ID].[mask which was used to select variants].[upper alle frequency cut-off]<br>ALLELE0: The reference allele<br>ALLELE1: The alternative allele and the effect allele - here all variants which pass allele frequency and mask filters (see ID-column)<br>A1FREQ: Total frequency of Allele 1<br>A1FREQ_CASES: Frequency of Allele 1 in cases<br>A1FREQ_CONTROLS: Frequency of Allele 1 in controls<br>N: Count of individuals that were used in the analysis<br>N_CASES: Count of cases that were used in the analysis<br>N_CONTROLS: Count of controls that were used in the analysis<br>TEST: Test-mode of regenie - here an additive model was used<br>BETA: Estimated effect size given as beta<br>SE: Standard error of BETA<br>LOG10P: Negative decadic logarithm of the p-value</p> <p>Method:<br>Please refer to our accompanying manuscript for a detailed description. We conducted gene-based collapsing analysis using regenie 3.2.4 in logistic regression mode (without step 1). Covariates used were sex, age, age*age, age*sex and the first 10 principal components derived from common variants.</p> <p>Contact for further information:<br>dac_decoi_hostgenetics@listen.uni-bonn.de</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.