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Koasidis et al._SDG FCMs_2022_DATASET
<p>This dataset contains the underlying data (Monte Carlo Fuzzy Cognitive Mapping inputs, lambda parameters, and outputs) for the manuscript Koasidis et al., submitted to Journal of Cleaner Production in November 2022.</p>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
Van de Ven et al 2022_Mitigation_DATASET
<p>This dataset contains the underlying scenario protocol, input (socioeconomic assumptions), and output (model results) for the study by Van de Ven et al. on post-Glasgow climate action and feasibility gap</p>
Karamaneas et al_RSETR_2022_DATASET
<p>This dataset contains the LEAP and OSeMOSYS output data for the manuscript Karamaneas et al., submitted to Renewable & Sustainable Energy Transition in September 2022.</p>
Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.
<p>Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
CLDF dataset with phoneme inventories from the "Journal of the IPA", aggregated by Baird et al. (2021)
<p>Cite the source of the dataset as:</p> <blockquote> <p>Baird, L., Evans, N., & Greenhill, S. J. (2021). Blowing in the wind: Using 'North Wind and the Sun' texts to sample phoneme inventories. Journal of the International Phonetic Association, 1–42. doi:10.1017/s002510032000033x</p> </blockquote>
Rafael et al., 2018: Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy (Dataset)
<p>Dataset containing 1um polystyrene beads, urine samples, urine samples mixed with ecoli and homogenous ecoli. Dataset is the post-processing version of the images to remove static background. Original model was trained on the post-processed images exclusiviely.</p>
Supplemental Files for Eckstein and Bates et al., Cell (2024)
<p>synister_fw_mat571_t11_synapses.feather - synapse level transmitter predictions for the FAFB dataset<br>========================================================================================</p> <p>Columns:id : pre-synapse ID<br>pre : neuron ID of pre-synaptic neuron<br>post : neuron ID of post-synaptic neuron<br>pre_pt_position_{x, y, z} : coordinates of pre-synapse in nm<br>nts_11.* : predicted score for being one of the six neurotransmitter types (or "neither")</p> <p> </p> <p>hemibrain-v1.2-tbar-neurotransmitters.feather.bz2 - synapse level transmitter predictions for the hemibrain dataset<br>========================================================================================</p> <p>Columns:x, y, z : voxel coordinates of pre-synapse<br>conf : confidence value of synapse detection<br>sv : supervoxel ID of the pre-synapse<br>roi : name of brain region of pre-synapse<br>roi_label : numerical label of brain region of pre-synapse<br>body : body ID of the pre-synaptic neuron<br>nts_8.* : predicted score for being one of the six neurotransmitter types (or "neither")</p> <p> </p> <p>Neuprint_Synapses_manc_v1.ftr - synapse level transmitter predictions for the MANC dataset<br>========================================================================================</p> <p>From Takemura et al. 2023, made with the network and methods described in this repository: https://www.biorxiv.org/content/10.1101/2023.06.05.543757v1</p> <p>Columns:x, y, z : voxel coordinates of pre-synapse<br>conf : confidence value of synapse detection<br>sv : supervoxel ID of the pre-synapse<br>roi : name of brain region of pre-synapse<br>roi_label : numerical label of brain region of pre-synapse<br>body : body ID of the pre-synaptic neuron</p> <p> </p> <p>synister-master.zip - code for training and prediction with the 'synister' pipeline for neurotransmission<br>========================================================================================</p> <p>A clone of out GitHub repository at the time of paper release, found at: https://github.com/funkelab/synister, e738e22.</p> <p>Contains README with in-depth description.</p> <p> </p> <p>supplemental_data_1.csv - original ground truth data by identified cell type<br>========================================================================================</p> <p>A .csv file that provides each</p> <p>cell_type : cell type we used to generate our ground truth data,</p> <p>known_nt : the transmitter it is has been reported to express from the literature</p> <p>known_source : the study that made this report and</p> <p>evidence : the type of evidence it contained</p> <p>Note that we used this data to build our original ground truth, but have since made a wider effort to annotate known transmission for validation, reflected in Supplemental data 3 and 4, columns known_nt and known_source.</p> <p> </p> <p>supplemental_data_2.csv - original ground truth data by individual connectomic neuronal reconstruction<br>========================================================================================</p> <p>A .csv file that indicates each neuronal reconstruction used to generate our ground-truth data. Presynapses from each reconstruction were used.</p> <p>id : a unique identifier, which is the root ID for a flywire neuron, a skeleton ID for a FAFB-Catmaid neuron and a bodyid for a HemiBrain neuron,</p> <p>known_nt : the designated transmitter from the literature</p> <p>cell_type : its cell type and</p> <p>dataset : the dataset from which the neuron comes.</p> <p>FAFB-Catmaid reconstructions can be found on virtual fly brain: https://fafb.catmaid.virtualflybrain.org/. Flywire reconstructions can be found at: https://ngl.flywire.ai/. HemiBrain reconstructions can be found at: https://neuprint.janelia.org/?dataset=hemibrain. We used version 630 in this study.</p> <p> </p> <p>supplemental_data_3.csv - <br>========================================================================================</p> <p>A .csv file in which each row is a single identified HemiBrain reconstruction. Columns provide:</p> <p>bodyid : each neuron’s unique identifier </p> <p>cell_type : morphological cell type</p> <p>pre : number of output synapses</p> <p>cropped : whether or not the neuronal reconstruction is cut off by the HemiBrain volume</p> <p>conf_nt : neuron-level transmitter prediction</p> <p>conf_nt_p : the confidence score for that prediction, which was calculated using the synapse-level confusion metrics and with synapse filtering</p> <p>top_nt, top_nt_p : neuron-level transmitter prediction with no synapse filtering for axon/dendrite</p> <p>{transmitter name} : the proportion of synapse-level transmitter predictions that “voted” for each transmitter </p> <p>segregation_index : the segregation index, a measure of neuronal polarity</p> <p>projection_score : the projection score, a measure of the distance between axon and dendrite</p> <p>ito_lee_hemilineage, hartenstein_hemilineage : the hemilineage to which this neuron belongs</p> <p>in_ground_truth : whether or not presynapses from this neuron were included in our ground-truth data</p> <p> </p> <p>supplemental_data_4.csv - <br>========================================================================================</p> <p>A .csv file where each row is a single identified FAFB-FlyWire reconstruction. Columns provide:</p> <p>root_id_630 : each neuron’s unique identifier from the 630 materialization of the FAFB-FlyWire dataset used in this paper</p> <p>root_id_783 : each neuron’s unique identifier from the new 783 materialization of the FAFB-FlyWire dataset used in this paper</p> <p>cell_type : morphological cell type</p> <p>conf_nt : neuron-level transmitter prediction</p> <p>conf_nt_p : the confidence score for that prediction, which was calculated using the synapse-level confusion metrics and with synapse filtering</p> <p>top_nt, top_nt_p : neuron-level transmitter prediction with no synapse filtering for axon/dendrite</p> <p>{transmitter name} : the proportion of synapse-level transmitter predictions that “voted” for each transmitter </p> <p>segregation_index : the segregation index, a measure of neuronal polarity</p> <p>projection_score : the projection score, a measure of the distance between axon and dendrite</p> <p>ito_lee_hemilineage, hartenstein_hemilineage : the hemilineage to which this neuron belongs</p> <p>side, morphology group, flow, cell class, cell sub class : other metadata from Schlegel et al. 2023, https://zenodo.org/records/8077335</p> <p>in_ground_truth : whether or not presynapses from this neuron were included in our ground-truth data</p> <p>notes : extra notes on the cell type designation, including alternate names</p> <p>pos_x, pos_y, pos_z : because the root id for neurons is changing as neurons are edited in an active connectome project we also supply the position of a point in the neuron to help identify it in FAFB-FlyWire voxel space</p> <p>nucleus_id : an ID for the nucleus segmentation</p> <p> </p> <p>supplemental_data_5.zip - <br>========================================================================================</p> <p>A .zip archive containing .png files depicting each of the 183 brain hemilineages we have used from the FAFB-FlyWire dataset. Neurons in each hemilineage are colored by their neuron-level transmitter predictions, hemilineage names given in the file name. Hemilineage labels for the FAFB-FlyWire dataset are fully reported in Schlegel et al. 2023, https://zenodo.org/records/8077335.</p> <p> </p> <p>supplemental_data_6.csv - <br>========================================================================================</p> <p>A .csv containing summary results for our 183 central brain secondary hemilineages. Columns provide:</p> <p>ito_lee_hemilineage : each hemilineage’s name </p> <p>hartenstein_hemilineage : and lineage-associated tract</p> <p>left, right, center : the number of neurons on the left and right hemispheres (left, right, center),</p> <p>lineage_type : the lineage type i.e. Type I or II</p> <p>{transmitter} : the number of neurons predicted for each transmitter</p> <p>majority_nt : and the majority vote of the hemilineage.</p> <p>Note, the same information is given for HemiBrain hemilineages, with ‘hemibrain ’ added to the column name.</p> <p> </p> <p>supplemental_data_7.csv - <br>========================================================================================</p> <p>A .csv in which each row is a described cell type of the fly brain. Cell type annotations were drawn from Schlegel et al. 2023, neurons with no cell type or hemibrain type annotated are not included. Columns provide:</p> <p>cell_type : each cell type’s name</p> <p>cell_type_nt : predicted transmitter for the cell type (cell type nt)</p> <p>cell_type_confidence : the cell type level transmitter confidence score</p> <p>{transmitter}_{dataset}_confidence : the sum of neuronlevel transmitter predictions across all FAFB-FlyWire reconstructions for each transmitter</p> <p>{transmitter}_{dataset }_n : the number of neurons with that confidence,</p> <p>known_nt : the transmitters reported to be used by the cell type in the literature</p> <p>known_nt_source : the citations for that information </p> <p>ito_lee_hemilineage, hartenstein_hemilineage, morphology_group, flow, cell_class, cell_sub_class : morphological annotations from Schlegel et al. 2023</p> <p>cell_type_nt : the cell type level transmitter is determined by the highest {transmitter} cell type {dataset} confidence</p> <p>cell_type_nt_conf : The cell type level transmitter confidence score (cell type nt) was calculated using our prediction confusion matrices.</p> <p>Results for both FAFB-FlyWire (‘ fafb’) and HemiBrain (‘hemibrain’) datasets are given.</p> <p> </p> <p>supplemental_data_8.tar.gz<br>========================================================================================</p> <p>Contains PNG files of 2D images cropped around synapses, together with their<br>counterfactual translations into different neurotransmitter types and<br>highlights of the most important areas for classification.There is one PNG file for each pair of neurotransmitters, named<br>`{source}_{target}.png`. E.g., the file `ach_dop.png` contains translations of<br>real cholinergic synapses into counterfactual dopaminergic synapses.Each PNG shows images in six columns, which are:1: original raw + classifier scores<br>2: counterfactual raw + classifier scores<br>3: counterfactual raw, with original raw in mask + classifier scores<br>4: mask, shown on original + relative classifier change<br>5: mask, shown on counterfactual<br>6: difference in mask area</p> <p>Paper: https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6</p>
Extra data to accompany code in GitHub burntfields_punjab, both used in Walker et. al. 2022, Detecting crop burning in India using satellite data
<p>Supplementary data files to accompany GitHub code 'burntfields_punjab' supporting Walker et. al. (2022) Detecting crop burning in India using satellite data [<a href="https://arxiv.org/abs/2209.10148">available here</a>] and Jack et. al. (2024) Money (not) to burn: Payments for ecosystem services to reduce crop residue burning).</p> <p>Includes custom Sentinel-2 cloud masks and data from Sentinel-2 Spectral Mixture Analysis to highlight Char (burning) based on general concept and methods from Daldegan et. al (2019). Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment 232, 111340. </p> <p>Note: Bands in weekly BASMA layers are: 0 = green vegetation, 1 = Non-productive vegetation and bare soil, 2 = Char (burned).</p> <p>further details are provided at: <a href="https://github.com/klwalker-sb/burntfields_punjab">https://github.com/klwalker-sb/burntfields_punjab</a> (archived at: <a href="https://doi.org/10.5281/zenodo.11225292" target="_blank" rel="noopener">DOI: 10.5281/zenodo.11225292</a>)</p>
Result data related to Tröndle et al (2024): Rebuilding Ukraine's energy supply in a secure, economic, and decarbonised way
<p>This dataset contains the result data of all the scenarios ran in the scientific article "Rebuilding Ukraine’s energy supply in a secure, economic, and decarbonised way".</p> <p>The results of the main five scenarios of the study are available as PyPSA result files:</p> <ul> <li>nuclear-and-renewables-high.nc: A scenario with nuclear in the mix and high economic growth assumption.</li> <li>nuclear-and-renewables-low.nc: A scenario with nuclear in the mix and low economic growth assumption.</li> <li>only-renewables-high-low-bio.nc: A scenario with only renewables, high economic growth assumption, and only 10% of assumed biomass potential.</li> <li>only-renewables-high.nc: A scenario with only renewables and high economic growth assumption.</li> <li>only-renewables-low.nc: A scenario with only renewables and low economic growth assumption.</li> </ul> <p>See the PyPSA documentation for more information: <a href="https://pypsa.readthedocs.io" target="_blank" rel="noopener">https://pypsa.readthedocs.io</a>.</p> <p>The results of the 330 global sensitivity analysis runs are available as summary files in CSV format:</p> <ul> <li>gsa-capacities-energy-gwh.csv: The installed energy storage capacities for each scenario.</li> <li>gsa-capacities-power-gw.csv: The installed generation capacities for each scenario.</li> <li>gsa-lcoe.csv: The levelised cost of electricity for each scenario.</li> </ul>
Early Cretaceous climate model output from the Kiel Climate Model (from Steinig et al. 2024)
<h1>README</h1> <p>This directory contains climate model output data for 36 different Early Cretaceous (Aptian/Albian) simulations performed with the Kiel Climate Model (KCM; ECHAM5/NEMO). Each zip file contains climatological mean values (monthly mean for the atmosphere; annual mean for the ocean) averaged over the last 100 model years of each simulation in netCDF format. A detailed description of the model setup, boundary conditions and integration strategy is given in the associated publication ("Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis" in Climate of the Past; <a href="https://doi.org/10.5194/egusphere-2023-2732">https://doi.org/10.5194/egusphere-2023-2732</a>). Differences in the paleogeographies are limited to the South Atlantic and Southern Ocean regions, but the output is available globally.</p> <h2>Available Models</h2> <p>Table 1: Overview of available model simulations sorted by the four main boundary condition differences discussed in the associated paper:</p> <table> <tbody> <tr> <th>Model ID</th> <th>Opening Stage</th> <th>CO2</th> <th>Drake Passage Depth</th> <th>Walvis Ridge Depth</th> </tr> </tbody> <tbody> <tr> <td>DC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>C51</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C53</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C54</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C61</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C63</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C64</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>WC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WI1</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI2</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI3</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI4</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI5</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI6</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> </tbody> </table> <h2>Available Variables</h2> <p>Each model zip file contains a <code>means</code> directory with the climatological mean output files for the atmosphere and ocean.</p> <h3>Atmosphere</h3> <p>Files starting with <code><Model ID>_mm_*</code> contain the monthly mean climatologies of 2D atmosphere variables. Each file represent a single variable for the following list of variables:</p> <p>Table 2: Overview of available atmospheric variables. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>aclcov</td> <td>total cloud cover</td> </tr> <tr> <td>evap</td> <td>evaporation</td> </tr> <tr> <td>precip</td> <td>total precipitation</td> </tr> <tr> <td>slp</td> <td>mean sea level pressure</td> </tr> <tr> <td>tau_x</td> <td>zonal wind stress</td> </tr> <tr> <td>tau_y</td> <td>meridional wind stress</td> </tr> <tr> <td>temp2</td> <td>2m air temperature</td> </tr> <tr> <td>tsw</td> <td>surface temperature of water</td> </tr> <tr> <td>uwnd</td> <td>10m u-velocity (winds)</td> </tr> <tr> <td>vwnd</td> <td>10m v-velocity (winds)</td> </tr> </tbody> </table> <h3>Ocean</h3> <p>Ocean variables are split across 4 individual files following the staggered grid of the NEMO ocean model. Naming structure follows <code><Model ID>.mean_grid_{T|U|V|W}.nc</code></p> <p>Table 3: Overview of available ocean variables on the T-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>iowaflup</td> <td>Ice=>ocean net freshwater</td> </tr> <tr> <td>sobowlin</td> <td>Bowl Index</td> </tr> <tr> <td>sohefldo</td> <td>Net Downward Heat Flux</td> </tr> <tr> <td>soicealb</td> <td>Ice Albedo</td> </tr> <tr> <td>soicecov</td> <td>Ice Cover</td> </tr> <tr> <td>soicetem</td> <td>Ice Surface Temperature</td> </tr> <tr> <td>somixhgt</td> <td>Turbocline Depth</td> </tr> <tr> <td>somxl010</td> <td>Mixed Layer Depth 0.01</td> </tr> <tr> <td>sorunoff</td> <td>Runoffs</td> </tr> <tr> <td>sosalflx</td> <td>Surface Salt Flux</td> </tr> <tr> <td>sosaline</td> <td>Sea Surface Salinity</td> </tr> <tr> <td>soshfldo</td> <td>Shortwave Radiation</td> </tr> <tr> <td>sosheig</td> <td>Sea Surface Height</td> </tr> <tr> <td>sosstsst</td> <td>Sea Surface temperature</td> </tr> <tr> <td>sowaflcd</td> <td>concentration/dilution water flux</td> </tr> <tr> <td>sowaflep</td> <td>atmos=>ocean net freshwater</td> </tr> <tr> <td>sowaflsp</td> <td>solid precipitation from atm</td> </tr> <tr> <td>sowafltp</td> <td>total PE flux from atm</td> </tr> <tr> <td>sowaflup</td> <td>Net Upward Water Flux</td> </tr> <tr> <td>vosaline</td> <td>Salinity</td> </tr> <tr> <td>votemper</td> <td>Temperature</td> </tr> </tbody> </table> <p> </p> <p>Table 4: Overview of available ocean variables on the U-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sozotaux</td> <td>Wind Stress along i-axis</td> </tr> <tr> <td>vozocrtx</td> <td>Zonal Current</td> </tr> <tr> <td>vozoeivu</td> <td>Zonal EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 5: Overview of available ocean variables on the V-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sometauy</td> <td>Wind Stress along j-axis</td> </tr> <tr> <td>vomecrty</td> <td>Meridional Current</td> </tr> <tr> <td>vomeeivv</td> <td>Meridional EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 6: Overview of available ocean variables on the W-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>soleaeiw</td> <td>eddy induced vel. coeff. at w-point</td> </tr> <tr> <td>soleahtw</td> <td>lateral eddy diffusivity</td> </tr> <tr> <td>voddmavs</td> <td>Salt Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeavm</td> <td>Vertical Eddy Viscosity</td> </tr> <tr> <td>votkeavt</td> <td>Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeevd</td> <td>Enhanced Vertical Diffusivity</td> </tr> <tr> <td>votkeevm</td> <td>Enhanced Vertical Viscosity</td> </tr> <tr> <td>voverctz</td> <td>Vertical Velocity</td> </tr> <tr> <td>voveeviw</td> <td>Vertical EIV Velocity</td> </tr> </tbody> </table> <p> </p> <p>Publication: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.: Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2732, 2023.</p> <p>Source: This dataset is available via <a href="https://doi.org/10.5281/zenodo.11386835">Zenodo DOI: 10.5281/zenodo.11386835</a>.</p> <p>Authors: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.</p> <p>License: This work is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0 </a>.</p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Data used for doi.org/10.1029/2012JD018338 : Baars et al, 2012: Aerosol profiling with lidar in the Amazon Basin during the wet and dry season
<p><span>This is the data whoch ahs been used for the publication:</span></p> <p><span><span>Baars, H.</span></span><span>, <span>A. Ansmann</span>, <span>D. Althausen</span>, <span>R. Engelmann</span>, <span>B. Heese</span>, <span>D. Müller</span>, <span>P. Artaxo</span>, <span>M. Paixao</span>, <span>T. Pauliquevis</span>, and <span>R. Souza</span> (<span>2012</span>), <span>Aerosol profiling with lidar in the Amazon Basin during the wet and dry season</span>, <em>J. Geophys. Res.</em>, <span>117</span>, D21201, doi:<a title="Link to external resource: 10.1029/2012JD018338" href="https://doi.org/10.1029/2012JD018338" target="_blank" rel="noopener">10.1029/2012JD018338</a>.</span></p> <p><span>For each of the Figures in the Publication the underlaying data is provided in a respective folder.</span></p> <p><span>The raw data (i.e,. the analyzed lidar data for several case during the one-year campaign in 2008) is provided separately.</span></p> <p><span>As the time of data creation is more than 10 years ago, the data description does not comply to current standards. Thus, in case of any questions, please contact the first author.</span></p> <p> </p> <p> </p> <p> </p>
Data for Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation
<p>Numerical dataset for the data presented in Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation, manuscript submitted to Icarus journal.</p>
Data and code for Bauer et al. (2024) J Veg Sci
<p>Data and code of for:</p> <p>Bauer M, Huber J, Kollmann J (2024) Beta diversity of restored river dike grasslands is strongly influenced by uncontrolled spatio-temporal variability. – <em>Journal of Vegetation Science </em>35, e13293. <a href="https://doi.org/10.1111/jvs.13293" target="_blank" rel="noopener">https://doi.org/10.1111/jvs.13293</a></p> <p><a href="https://github.com/markus1bauer/2023_danube_dike_survey/blob/main/README.md" target="_blank" rel="noopener">GitHub README</a></p>
Dautan et al 2024 " Gut-Initiated Alpha Synuclein Fibrils Drive Parkinson's Disease Phenotypes: Temporal Mapping of non-Motor Symptoms and REM Sleep Behavior Disorder"
<p><span>Parkinson’s disease (PD) is characterized by progressive motor as well as less recognized non-motor symptoms that arise often years before motor manifestation, including sleep and gastrointestinal disturbances. Despite the heavy burden on the patient’s quality of life, these non-motor manifestations are poorly understood. To elucidate the temporal dynamics of the disease, we employed a mice model involving injection of alpha-synuclein (αSyn) pre-formed fibrils (PFF) in the duodenum and antrum as a gut-brain model of Parkinsonism. Using anatomical mapping of αSyn PFF propagation and behavioral and physiological characterizations, we unveil a correlation between post-injection time the temporal dynamics of αSyn propagation and non-motor/motor manifestations of the disease. We highlight the concurrent presence of aggregates in key brain regions, expressing acetylcholine or dopamine and their functions in sleep duration, wakefulness, and particularly REM-associated atonia corresponging to REM behavioral disorder-like symptoms. This study presents a novel and in-depth exploration into the multifaceted nature of PD, unraveling the complex connections between α-synucleinopathies, gut-brain connectivity, and the emergence of non-motor phenotypes.</span></p>
Data for "Excitation of Low- and High-frequency Magnetosonic Whistler Waves Associated with SLAMS in the Terrestrial Foreshock" by Yao et al.
<p>The database includes the plasma data used in instability analyses and the theoretical analysis results based on the linear model.</p> <h3>Captions:</h3> <div><strong>Plasma_input.mat</strong> file is the plasma data used in the instability analyses, which is used to plot Figure 2a-2d.</div> <ul> <li><strong><em>B</em></strong>: magnetic field strength</li> <li><strong><em>n</em></strong>: plasma number density</li> <li><strong><em>Te_para</em></strong>: parallel electron temperature</li> <li><strong><em>Te_perp</em></strong>: perpendicular electron temperature</li> <li><strong><em>Tp</em></strong>: proton temperature</li> </ul> <div> </div> <div><strong>WWs_theo_predictions.mat</strong> file is the calculation result of linear growth rate and wave frequency. The results are used to plot Figure 2e and 2f.</div> <ul> <li><strong><em>f_theo</em></strong>: wave frequency in theoretical predictions</li> <li><strong><em>gamma_theo</em></strong>: growth rate in theoretical predictions</li> </ul>
Data and codes for Orozco-Ruiz et al. "Quantum control without quantum states"
<p>Simulation data and analytic structure constants for the paper "Quantum control without quantum states" by Modesto Orozco-Ruiz, Nguyen Le, and Florian Mintert.</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.