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zenodo44/100

Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"

Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018 There are 2 files in this upload: 1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron. 2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’. The subfolder ‘Functions’ contains functions called by the main codes. The main folder contains the following codes: Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1. Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2. Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3. Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4. Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1. Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.   The subfolder ‘Data’ contains the data structures used for the different figures: data_figure1.mat data_figure2.mat data_figure3.mat data_figure4_MECA.mat data_figure4_Activation.mat data_figure4_Inactivation.mat data_figureS2_Activation.mat data_figureS2_Inactivation.mat data_Axon.mat   The data structures contain the following fields: Mouse_Name : name of the mouse. Mouse_DateOfBirth: date of birth of the mouse (YMD). Mouse_Sex: sex of the mouse (F or M). Mouse_Genotype: genotype of the mouse. Mouse_Drug: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’). Mouse_Virus: virus injected if any. Cell_Counter; cell recorded in a given mouse. Cell_Type: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP). Cell_Depth: depth of the recorded cell relative to pia (µm). Cell_TargetedBrainArea: cortical area targeted (C2 column of the barrel cortex = C2). Cell_Fluorescence: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf Mouse_Genotype) with Cell_Fluorescence=TRUE is considered as a VIP neuron (cf Cell_Type). Sweep_Counter: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s). Sweep_Type: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’;  Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’;  ). Sweep_Start_Time: time at the beginning of the sweep recording (YMDHms). Sweep_WhiskerAngle: C2 whisker angular position extracted from simultaneous high-speed video filming (deg). Sweep_WhiskerAngle_SamplingRate: sampling rate of the whisker angle trace. Sweep_WhiskingOnset_Time: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s). Sweep_MembranePotential: membrane potential recording (mV) after cutting of the APs. Sweep_MembranePotential_SamplingRate: sampling rate of the membrane potential signal (pt.s-1). Sweep_CurrentInjected: current injected into the cell (pA). Sweep_CurrentInjected_SamplingRate: sampling rate of current signal (pt.s-1). Sweep_WhiskerStim_Name: whisker to which the magnetic stimulus was applied to (C2 or B2&C2). Sweep_WhiskerStim_Time: onset times of the whisker stimulus (s). Sweep_OptoStim_Power: light power applied for optogenetic manipulations (% of the max power). Sweep_OptoStim_Time: onset times of the light pulses for optogenetic manipulations (s). Cell_ID: unique cell identifier (= Mouse_Name+Cell_Counter). SpikeThreshold: spike threshold used to detect APs (mV). Trial_WhiskingOnset: data structure containing the cut signals used to compute averaged responses around whisking onset times. Trial_WhiskerStim: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times. Trial_WhiskerStim_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements. Trial_WhiskerStim_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements. Trial_Opto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times. Trial_OptoAndWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements. Trial_OptoAndWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements. Trial_OnlyWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements. Trial_OnlyWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements. Trial_OnlyOpto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Spatial distribution data set of wetlands in Baiyangdian Basin

As one of the wetland systems in the northern plain of China, Baiyangdian plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of the wetland ecosystem in Baiyangdian basin is also of great significance for the construction of the New Area and future scientific planning. Based on the 10 meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the network distribution map and water system distribution map of Baiyangdian basin wetland ecosystem in 2017 were drawn by artificial visual interpretation and machine automatic classification It provides the basis for the study of the connectivity (including hydrological connectivity and landscape connectivity). The boundary of Baiyangdian basin in this data set is from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2b remote sensing image provided by ESA on September 20, 2017. This data set uses the second, third, fourth and eighth bands of 10 meter resolution in the image, carries out radiation calibration, mosaic, mosaic and other preprocessing operations in SNAP and ArcGIS 10.2 software, and carries out supervised classification in ENVI 5.3 software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, mapping the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; obtaining river network of Baiyangdian basin by visual interpretation based on Google Earth high resolution satellite image (resolution 0.23m). The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (. SHP); Baiyangdian basin river network data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of the study area and the river 3 km buffer) (. tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, 866 km in length, distributed in Northeast southwest direction, mostly at the junction of forest land and cultivated land; and 337 km2 river channels in plain area, 1574 km in length. Baiyangdian basin is divided into eight types of land use / cover: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90 % in 2017. Among all wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of woodland is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the high altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of swamp, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are swamp and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of xiong'an new area and Baiyangdian basin.

opencc-by-4.0Jul 2022View details →

zenodo44/100

A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets

Includes data relevant for publication with DOI <a href="https://doi.org/10.1038/s42004-020-00422-w">10.1038/s42004-020-00422-w</a> plus a table with information about how the data were obtained and processed.

opencc-by-4.0Dec 2019View details →

zenodo44/100

Data Set of Extracted Summary Statistics from Equipment Sensor Data

This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause. The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).

opencc-by-4.0Jan 2021View details →

zenodo44/100

US_Horror_Trailers_Data_Set

This data set contains audio, colour, editing, and motion data for trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015. All trailers were pre-processed to trim MPAA tag screens and YouTube channel promotional materials, and cropped to remove letterbox blanking. The sample is described in the file US_Horror_Trailers_Sample_Summary.csv, which contains the title of the film promoted, the URL for the trailer on YouTube (all URLs were correct as of 27 January 2021), the dimensions of the video file after pre-processing, the number of frames after pre-processing, the native frame rate, and the running time of a trailer after pre-processing in seconds. For each trailer the following information is available: <ul> <li>Audio: the time contour of the normalized aggregated power envelope. One csv file per trailer with the naming convention: *_audio.csv</li> <li>Colour: three unnamed columns containing the average colour of each frame as an RGB triplet. One csv file per trailer with the naming convention: *_rgb.csv</li> <li>Motion: total magnitude and horizontal and vertical pixel displacement between consecutive frames. One csv file per trailer with the naming convention: *_motion.csv</li> <li>Editing: Shot length data in seconds. Shot length data is contained in the csv file US_Horror_Trailers_SL_Data.csv, with one column per data.</li> </ul> All variables are time ordered from frame/shot/window 1 to n even when no explicit time variable is provided. A guide to visualising the data in this data set is available online here: <a href="https://rpubs.com/nr62_rp33/visualizing_trailers">https://rpubs.com/nr62_rp33/visualizing_trailers</a>.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Data Set: fMRI Meta-Analyses

This data set is the result of a systematic search in PubMed and APA PsycINFO for fMRI meta-analyses. These records would be suitable for: <ul> <li>meta-meta analysis on fMRI meta-analyses</li> <li>research questions regarding neuroimaging meta-analysis methodology (especially for those interested in coordination-based meta-analyses (CBMA): activation likelihood estimation (ALE) using GingerALE software, multi-level kernel density analysis (MKDA), seed-based d mapping (SDM) or image-based meta-analyses.</li> </ul> NOTE: These data are the raw search results from PubMed and PsycINFO and have been deduplicated but have NOT been screened for any inclusion criteria. This means you may find records in these results that are not, in fact, meta-analyses but still have the search terms (below) present in the title or abstract of the paper.  The data set is available in three formats: .csv, .ris, and a <a href="https://www.zotero.org/groups/4150721/fmri_meta-analyses">Zotero shared library</a> Search documentation:  Search Date:  May 21, 2021 Conducted by: Meghan Testerman, Behavioral Sciences Librarian, Princeton University, mtesterman@princeton.edu   PubMed: 433 records identified PubMed Search query (exact): (meta-analysis[Title]) AND (fMRI[Title/Abstract])   PsycINFO: 289 records identified PsycINFO Search Query (exact): (TI meta-analysis) AND (TI fMRI OR AB fMRI) Total Results Pubmed (433) + PsycINFO (289) = 722 Deduplicates removed: 234 Unique records: 488

opencc-by-4.0Jun 2021View details →

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Dipole localisation predictions data set

This data set contains prediction of ten dipole localisation algorithms computed using a simulated artificial lateral line (potential flow).

opencc-by-4.0May 2021View details →

zenodo44/100

VocalSketch Data Set v1.0.4

This data set contains thousands of vocal imitations of a large set of diverse sounds. These imitations were collected from hundreds of contributors via Amazon's Mechanical Turk website. The data set also contains data on hundreds of people's ability to correctly label these vocal imitations, also collected via Amazon's Mechanical Turk. This data set will help the research community understand which audio concepts can be effectively communicated with this approach. We have released this data so the community can study the related issues and build systems that leverage vocal imitation as an interaction modality, such as search engines that can be queried by vocally imitating the desired sound.   This data set is a supplement to a paper. Please cite the following paper to reference this data set in a publication: Cartwright, M., Pardo, B. VocalSketch: Vocally Imitating Audio Concepts. In Proceedings of ACM Conference on Human Factors in Computing Systems (2015). http://dx.doi.org/10.1145/2702123.2702387   See https://github.com/interactiveaudiolab/VocalSketchDataSet for the latest updates to this data set.   Interactive Audio Lab: http://music.eecs.northwestern.edu

opencc-by-4.0Jan 2015View details →

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Data set from Pouzat and Chaffiol (2009) Journal of Neuroscience Methods 181:119.

1 1 1This is the data set of Cockroach first olfactory relay recordings used in Pouzat and Chaffiol (2009) Automatic Spike Train Analysis and Report Generation. An Implementation with R, R2HTML and STAR Journal of Neuroscience Methods 181: 119-1443. These data are also included in the R package STAR. The data are in HDF5 format.

opencc-by-4.0Jan 2015View details →

zenodo44/100

Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"

The file Data_Marxetal2014_AB.csv contains the data to the paper Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4. as comma-separated value (csv) file Each row contains the data of one trial, represented by the following columns 1 - number of the line 2 - subject ID 3 - experiment number 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original) 5 - number of targets 6 - SOA in ms 7 - serial position of first target (0 if absent) 8 - serial position of second target (0 if absent) 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine) 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine) 11 - response to "How many animals?" (detection) 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine) 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)

opencc-by-4.0Dec 2014View details →

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Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.

In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576 The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article. If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com Each row of the files contain data from one trial. Columns: Experiment 1 1 - Participant number 2 - Block number 3 - Trial number 4 - Target orientation (1: Upright; -1: Inverted) 5 - Stimulus width 6 - Stimulus height 7 - Response width 8 - Response height Experiment 2 1-8 Same as Experiment 1 9 - Condition: dynamic (1) or static (2) target Experiment 3 1 - Participant number 2 - Block number 3 - Trial number 4 - Walker orientation (1: upper walker upright, lower walker inverted; 2: upper walker inverted, lower walker upright) 5 - Condition (1: upper target larger (21%) than lower target; 2: upper target larger (10.5%) than lower target; 3: target sizes are identical; 4: lower target larger (10.5%) than upper target; 5: lower target larger (21%) than upper target) 6 - Inter stimulus interval (from end of walker presentation to onset of target circles) (1: 17ms; 2: 100ms) 7 - Response (1: upper target was larger; 2: lower target was larger)

opencc-by-4.0Feb 2017View details →

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Data set for the physical, chemical and biochemical modelling of the primary sedimentation tanks at the WWTP of Eindhoven

These files contain data about measurement campaigns on the primary sedimentation tanks of the WWTP of Eindhoven (The Netherlands) in 2013 and 2014 and the routinely collected data for 2011 till 2013. The data was processed in the PhD of Youri Amerlinck, entitled "Model refinements in view of wastewater treatment plant optimization: improving the balance in sub-model detail." http://www.biomath.ugent.be/biomath/publications/download/amerlinckyouri_phd.pdf WWTP of Eindhoven PST Routine Measurements 2011_2013.csv January 5, 2011 - June 14, 2013: Routine analysis for BOD5, COD, TKN, TP, PO4, TSS WWTP of Eindhoven PST Reduced Capacity 2013.csv June 24, 2013 - July 23, 2013 - September 9, 2013 Evaluation of reducing the capacity of the PST (including dosing of chemicals) for CODT, CODS, TP, PO4 ,TSS WWTP of Eindhoven PST measurement campaign full ASM 20140506.csv May 6, 2014: Full ASM fractionation BOD5, CODT, CODS, TSS, VSS TP, PO4 ,TN, NH4, NO3, pH WWTP of Eindhoven PST measurement campaign full ASM and Cations 20140902.csv September 2, 2014: Full ASM fractionation (repetition) and cation analysis (BOD10, CODT, CODS, TSS, VSS, TP, PO4 ,TN, NH4, NO3, pH - Ca, Mg, Na, K, Fe)

opencc-by-4.0Apr 2017View details →

zenodo44/100

Data set for "Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice"

Data set for: Auffret M, Ravano VL, Rossi GMC, Hankov N, Petersen MFA, Petersen CCH (2017) Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice. Neuroscience, http://dx.doi.org/10.1016/j.neuroscience.2017.04.004 There are 9 files in this data upload: <ol> <li>'2017_Auffret_Neuroscience.pdf' - this is a pdf version of the online publication.</li> <li>'Auffret_data.mat' - this is a Matlab data structure, which contains all the data for the publication.</li> <li>'Auffret_data.npy' - this is a Python data structure, which contains all the data for the publication. The Python data was generated from 'Auffret_data.mat' by 'DataViewer.py'.</li> <li>'Auffret_data.xlsx' - this is an Excel file, which contains all the data for the publication. This Excel file was generated from 'Auffret_data.mat'.</li> <li>'DataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'DataViewer.m'.</li> <li>'DataViewer.m' - this is a Matlab Code, which displays the data contained in 'Auffret_data.mat'.</li> <li>'DataViewer.py' - this is a Python Code, which generates 'Auffret_data.npy' from 'Auffret_data.mat', and displays an example trial.</li> <li>'FigureViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'FigureViewer.m'.</li> <li>'FigureViewer.m' - this is a Matlab Code, which analyses the data in 'Auffret_data.mat', and displays the results in the same way as the published figures (Auffret et al., 2017).</li> </ol>

opencc-by-4.0Apr 2017View details →

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Nitric oxide (NO) data set (60--160 km) from SCIAMACHY mesosphere--lower thermosphere limb scans

Overview Contains the nitric oxide (NO) number densities (in cm-3) from 60 km to 160 km retrieved from SCIAMACHY mesosphere--lower thermosphere (MLT, 50--150 km) limb scans. SCIAMACHY is a UV-visible-near-infrared spectrometer which flies on ESA's Envisat and was operational from 08/2002 to 04/2012 (see Burrows et al., 1995 and Bovensmann et al., 1999 and references therein). The Mesosphere--Lower Thermosphere (MLT) measurement mode was carried out from 07/2008 until the end of the mission for one day every 15 days. This data set comprises 84 days of SCIAMACHY MLT NO measurements, each containing about 15 orbits. The NO retrieval was carried out at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, and is described in Bender et al., 2013. We used the SCIAMACHY geo-located atmospheric spectra (SCI_NL__1P) version 8.02 provided by ESA via their data browser at https://earth.esa.int/web/guest/data-access/browse-data-products. The spectra were calibrated with ESA's `SciaL1C` command line tool available for download at https://earth.esa.int/web/guest/software-tools/content/-/article/scial1c-command-line-tool-4073. The SCIAMACHY NO data were compared to the results from ACE-FTS, MIPAS, and SMR in Bender et al., 2015, showing that all agree within the respective measurement uncertainties. Acknowledgements The development of the retrieval was funded by the Helmholtz-society under the grant number VH-NG-624. The SCIAMACHY project, which was initiated by Professor Burrows in 1984, was funded by the German Aerospace  Agency (DLR), the Netherlands Space Office NSO, formerly NIVR, and the Belgium ministry responsible for space.  ESA funded the Envisat project. Professor Burrows of University of Bremen is the Principal Investigator. He and his  research team comprising his colleagues in Bremen and international scientific collaborators led the scientific  support and development of SCIAMACHY and the scientific exploitation of its  data products. The SCIAMACHY instrument is developed by an industrial team headed by companies now known as Airbus SD on the German side and by Dutch Space on the Dutch side and included Belgium companies. The instrument and algorithm development is supported by the activities of the SCIAMACHY Science Advisory Group (SSAG), a team of scientists from various  international institutions: University of  Bremen (D), SRON (NL), SAO (USA), IASB (B), MPI Chemistry Mainz (D), KNMI (NL),  University of Heidelberg (D), IMGA (I), CNRS-LPMA (F). Operational data processing is being performed by ESA and DLR-DFD within the ENVISAT ground  segment. Support with respect to mission planning and operations is given by  the SCIAMACHY Operations Support Team (SOST). The relevant work at the University of Bremen is funded by the University and State of Bremen.

opencc-by-sa-4.0Jun 2017View details →

zenodo44/100

LAMASUS Land Use Management Data Set

The land use management classes developed as part of the LAMASUS project have been aggregated to a 1 km grid (INSPIRE) provided as three geopackages and NUTS2 regions, provided as shares in each grid cell and number of pixels in each region, respectively. The NUTS2 data can be joined to the NUTS2 shapefile (contained in NUTS0_2016_01M_3035_corrected.zip, including all NUTS levels), which is provided here as some corrections to the NUTS layer have been made. A list of the land use management classes is provided in the excel file. Note that these aggregated files are consistent with official statistics from the Forest Resources Assessment of the Food and Agriculture Organization (FRA-FAO) for forest areas and Eurostat for cropland and grassland areas.  <div> This dataset has been created as part of LAMASUS Project under the scope of Deliverable 2.1 titled "The LUM Geodatabase and Area Estimates of Land Use Change to 2018 ". The data is directly linked to the work described on pages 12-34, belonging to section 3. The Land Use Management (LUM) Geodatabase. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf.</a> </div>

opencc-by-4.0Aug 2024View details →

zenodo44/100

Fully differentiable, fully distributed River Discharge Prediction: data sets

This repository contains the data sets used in: Scholz et al. (2025). Fully differentiable, fully distributed River Discharge Prediction. <ul> <li><code>dem_1000.h5</code> based on EU-DEM v1.1, reprojected to RADOLAN grid: <a href="https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50">https://sdi.eea.europa.eu/catalogue/srv/api/records/3473589f-0854-4601-919e-2e7dd172ff50</a></li> <li><code>efas.h5</code> based on EFAS historical: <a href="https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview">https://ewds.climate.copernicus.eu/datasets/efas-historical?tab=overview</a></li> <li><code>era5_ssrd_neckar*.nc</code> based on ERA5 provided by ECMWF, reprojected to RADOLAN grid: <a href="https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5">https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</a></li> <li><code>radolan_neckar_*.h5</code> based on RADOLAN rw product provided by the Deutsche Wetterdienst: <a href="https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/">https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/</a></li> </ul> Due to copyright, the discharge data has to be downloaded manually from the Global Runoff Data Centre (<a href="https://grdc.bafg.de/">https://grdc.bafg.de/</a>), and then preprocessed with the provided <code>bafg_parser.py</code> python script. We use the following stations in our work: <ul> <li>6335290: STEIN</li> <li>6335291: GAILDORF</li> <li>6335565: BAD IMNAU</li> <li>6335600: ROCKENAU SKA</li> <li>6335601: LAUFFEN</li> <li>6335602: PLOCHINGEN</li> <li>6335603: ROTTWEIL</li> <li>6335604: KIRCHENTELLINSFURT</li> <li>6335620: MOSBACH</li> <li>6335660: PFORZHEIM</li> <li>6335665: DENKENDORF</li> <li>6335671: ALTENSTEIG</li> <li>6335675: MURR</li> <li>6335676: OPPENWEILER</li> <li>6335680: SCHWABSBERG</li> <li>6335681: UNTERGRIESHEIM</li> <li>6335690: NEUSTADT</li> </ul> To preprocess the discharge data, additionally the river network data "Fließgewässer (AWGN)" provided by the Landesanstalt für Umwelt Baden-Württemberg (LUBW) is required: <a href="https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1">https://rips-metadaten.lubw.de/trefferanzeige?docuuid=7251515f-6aed-4555-8319-ab6314155ab1</a>

opencc-by-4.0Oct 2024View details →

zenodo44/100

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

opencc-by-4.0Feb 2023View details →

zenodo44/100

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set   Sandip S. Dhomse  School of Earth and Enviro, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk  Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC12 (CF2Cl2)  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-12 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Dataset also includes two files containing daily mean zonal mean CFC-12 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (9132 days/64 latitudes): zmcfc12_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km) zmcfc12_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Data set for paper "Ramparts around lakes on Titan impact winds and methane evaporation"

Data and post-processing code used for the paper "Ramparts around lakes on Titan impact winds and methane evaporation", submitted to PSJ in 2024. Are made available:  - a list of the simulations (list_simulations_ramparts2D.pdf)  - the simulations' netCDF outputs (run-t##.nc.gz)  - the input files used to run the simulations (in input_files/)  - the post-processing python codes used to plot the figures (in post_processing_codes/)  - tables of latent heat flux and horizontal wind values (Tables_LH_and_Uwind.pdf)  - a gif of the horizontal wind in the reference run (u_wind_run-t04_speed.gif)  - a gif of the vertical wind in the reference run (w_wind_run-t04_speed.gif)

opencc-by-4.0Feb 2024View details →

zenodo44/100

MLFMF: Data Sets for Machine Learning for Mathematical Formalization

<h3>MLFMF</h3>MLFMF (Machine Learning for Mathematical Formalization) is a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. The MLFMF data sets provide solid benchmarking support for further investigation of the numerous machine learning approaches to formalized mathematics. With more than 250,000 entries in total, this is currently the largest collection of formalized mathematical knowledge in machine learnable format. In addition to benchmarking the recommendation systems, the data sets can also be used for benchmarking node classification and link prediction algorithms. <h3>The four data sets</h3>Each data set is derived from a library of formalized mathematics written in proof assistants <a href="https://agda.readthedocs.io/en/v2.6.4/">Agda</a> or <a href="https://lean-lang.org/">Lean</a>. The collection includes  <ol><li>the largest Lean 4 library <a href="https://github.com/leanprover-community/mathlib4">Mathlib</a>,</li><li>the three largest Agda libraries:<ul><li>the <a href="https://github.com/agda/agda-stdlib">standard library</a></li><li>the library of univalent mathematics <a href="https://github.com/UniMath/agda-unimath">Agda-unimath</a>, and</li><li>the <a href="https://github.com/martinescardo/TypeTopology">TypeTopology</a> library.</li></ul></li></ol>Each data set represents the corresponding library in two ways: as a heterogeneous network, and as a list of syntax trees of all the entries in the library. The network contains the (modular) structure of the library and the references between entries, while the syntax trees give complete and easily parsed information about each entry.The Lean library data set was obtained by converting .olean files into s-expressions (see the <a href="https://github.com/andrejbauer/lean2sexp">lean2sexp</a> tool).The Agda data sets were obtained with an <a href="https://github.com/andrejbauer/agda/tree/master-sexp">s-expression extension</a> of the official Agda repository (use either master-sexp or release-2.6.3-sexp branch).For more details, see our <a href="https://arxiv.org/abs/2310.16005">arXiv copy</a> of the paper.<h3>Directory structure</h3>First, the mlfmf.zip archive needs to be unzipped. It contains a separate directory for every library (for example, the standard library of Agda can be found in the stdlib directory) and some auxiliary files. Every library directory contains<ul><li>the network file from which the heterogeneous network can be loaded,</li><li>a zip of the entries directory that contains (many) files with abstract syntax trees. Each of those files describes a single entry of the library.</li></ul>In addition to the auxiliary files which are used for loading the data (and described below), the zipped sources of lean2sexp and Agda s-expression extension are present.<h4>Loading the data</h4>In addition to the data files, there is also a simple python script main.py for loading the data. To run it, you will have to install the packages listed in the file requirements.txt: tqdm and networkx. The easiest way to do so is calling pip install -r requirements.txt.When running main.py for the first time, the script will unzip the entry files into the directory named entries. After that, the script loads the syntax trees of the entries (see the Entry class) and the network (as networkx.MultiDiGraph object).Note. The entry files have extension .dag (directed acyclic graph), since Lean uses node sharing, which breaks the tree structure (a shared node has more than one parent node).<h3>More information</h3>For more information about the data collection process, detailed data (and data format) description, and baseline experiments that were already performed with these data, see our <a href="https://arxiv.org/abs/2310.16005">arXiv copy</a> of the paper.For the code that was used to perform the experiments and data format description, visit our github repository <a href="https://github.com/ul-fmf/mlfmf-data">https://github.com/ul-fmf/mlfmf-data.</a><h3>Funding</h3>Since not all the funders are available in the Zenodo's database, we list them here:<ol><li>This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-21-1-0024.</li><li>The authors also acknowledge the financial support of the Slovenian Research Agency via the research core funding No. P2-0103 and No. P1-0294.</li></ol>

opencc-by-4.0Oct 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record