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IODP Expedition 353 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 359 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a vertical concentrated live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained. </p>
Impact of nonlinear hydrodynamic modelling on geometric optimisation of a spherical heaving point absorber
<p>Due to the amount of iterative computation involved, researchers involved in geometric optimisation of wave energy devices typically employ linear hydrodynamic models. However, the exaggerated motion of wave energy devices, aided by energy maximising control action, challenges the assumptions upon which linear hydrodynamic modelling relies. Furthermore, the optimal device geometry is also sensitive to the nature of the energy-maximisation controller employed, and to the set of wave conditions over which the optimisation is carried out.<br> <br> In order to focus on the essential issues, this study takes the simplest possible device for optimisation, a heaving sphere (with just one free parameter), but one which exhibits nonlinear hydrodynamic characteristics, due to the non-uniform cross-sectional area. The study examines the sensitivity to the inclusion of nonlinear Froude-Krylov forces. In addition, the sensitivity of the optimal device size to differences in the applied control algorithm is also studied, as are effects due to different representative sea state representations and performance evaluation criteria.</p>
WP3-CT2 Alpine Glaciers Disappearance Tipping Point
<p>Glaciers Length changes starting from small, medium, and large glaciers.</p>
Data and R-scripts for "Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot" (V2)
<p>Sustainable land system transformations are necessary to avert biodiversity and climate collapse. However, it remains unclear where entry points for transformations exist in complex land systems. Here, we conceptualize land systems along land-use trajectories, which allows us to identify and evaluate leverage points; i.e., entry points on the trajectory where targeted interventions have particular leverage to influence land-use decisions. We apply this framework in the biodiversity hotspot Madagascar. In the Northeast, smallholder agriculture results in a land-use trajectory originating in old-growth forests, spanning forest fragments, and reaching shifting hill rice cultivation and vanilla agroforests. Integrating interdisciplinary empirical data on seven taxa, five ecosystem services, and three measures of agricultural productivity, we assess trade-offs and co-benefits of land-use decisions at three leverage points along the trajectory. These trade-offs and co-benefits differ between leverage points: two leverage points are situated at the conversion of old-growth forests and forest fragments to shifting cultivation and agroforestry, resulting in considerable trade-offs, especially between endemic biodiversity and agricultural productivity. Here, interventions enabling smallholders to conserve forests are necessary. This is urgent since ongoing forest loss threatens to eliminate these leverage points due to path-dependency. The third leverage point allows for the restoration of land under shifting cultivation through vanilla agroforests and offers co-benefits between restoration goals and agricultural productivity. The co-occurring leverage points highlight that conservation and restoration are simultaneously necessary. Methodologically, the framework shows how leverage points can be identified, evaluated, and harnessed for land system transformations under the consideration of path-dependency along trajectories.</p>
Icons of the tipping points in the Earth System from the H2020 COMFORT project (820989)
<p>Triple threat processes and/or other forcings can lead to changes in the ocean happening fast and abruptly. These changes, referred to as “tipping points”, are critical thresholds in a marine system that, when exceeded, can lead to a significant change in the state of the system, which often can be irreversible. This product has been prepared with the financial support of Norges forskningsråd (Research Council of Norway) (309382) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT, Our common future ocean in the Earth system – quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points). The work reflects only the author’s/authors’ view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains.</p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
UAV data of post fire dynamics, Quesenbank, Harz, 2022 (orthomosaics, topography, point clouds)
<p>Unoccupied aerial vehicles (UAVs) were used to investigate a burnt forest site situated in the Harz National Park area near the location Schierke, east of the Brocken, called Quesenbank (<a href="https://www.google.com/maps/place/51%C2%B046'07.6%22N+10%C2%B041'30.2%22E/@51.7682386,10.6905312,489m/data=!3m1!1e3!4m5!3m4!1s0x0:0xd8d7f64a3ff9acf1!8m2!3d51.76877!4d10.69173">51.76877 °N, 10.69173 °E</a>) which is a spruce stand stand severely affected by bark-beetle and windfall. Most of the trees are dead such that they provide high fuel loads for potentially occurring wildfires. The small river Wormke crosses the Quesenbank and separates the survey area between a hiking path and the forest stand.</p> <p>During the 12.08.2022, inhabitants reported a <a href="https://www.ndr.de/nachrichten/niedersachsen/braunschweig_harz_goettingen/Waldbrand-im-Harz-Polizei-geht-von-Brandstiftung-aus,waldbrand882.html">fire</a> near Schierke which was quickly contained by the authorities. Two months after the fire, on 13.10.2022, a team of scientists from GAU Göttingen and TU Berlin was accompanied by a National Park representative for investigation. The site was surveyed by using modern UAVs and by sampling soil strata, ash and vegetation. This report will briefly describe UAV-based surveys and the derived data products.</p> <p>For an overview, see the <strong>report </strong>or download the Maps.zip folder.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>Projekt ”Postfeuerdynamik auf Brandflächen im Nationalpark Harz”</strong></p> <p>Dr. Simon Drollinger, Georg-August Universität Göttingen</p> <p>Marlene Düngelhoef, Nationalparkverwaltung Harz</p> <p>Thomas Glinka, Nationalparkverwaltung Harz</p>
Swiss Smart Meter Data - CKW 2021/2022 - anonymized individual metering points
<p>Cleaned Swiss smart meter data based on a collection from CKW AG (see <a href="http://opendata.ckw.ch">opendata.ckw.ch</a>)</p> <ul> <li>Duration: 2 years (1. Jan 2021 - 31. Dec 2022, CET timestamp)</li> <li>Location: Canton Lucerne, Switzerland</li> <li>Interval: 15 minutes</li> <li>Values: Active Energy (kWh) </li> <li>Meters in each year: 4959 (see filtered_IDs.csv for all IDs)</li> </ul> <p>The original dataset has been filtered based</p> <ul> <li>on missing data </li> </ul> <p>This means, all 4959 meters have consumption and reported values over the full duration of 2 years.<br> Files are available as space-saving parquet files per day in year. Number in filename is number of day within the year.<br> <br> Summary.zip contains summary statistics over all 112148 (unfiltered) meters counting observations (including duplicates) , and aggregating energy data (per month, and or hourly data), see overview.csv within summary.zip</p>
NPM3D dataset with instance labels used in paper "Toward Accurate Instance Segmentation in Large-scale LiDAR Point Clouds"
<p>NPM3D (https://npm3d.fr/paris-carla-3d) consists of mobile laser scanning (MLS) point clouds collected in four different regions in the French cities of Paris and Lille, where each point has been annotated with two labels: one that assigns it to one out of 10 semantic categories and another one that assigns it to an object instance. When inspecting the data, we found 9 cases where multiple tree instances had not been separated correctly (i.e., they had the same ground truth instance label). These cases were manually corrected using the CloudCompare software (https://www.cloudcompare.org), and 35 individual tree instances were obtained. Our variant of the dataset with 10 semantic categories and enhanced instance labels is publicly available.</p>
cMSSM parameter space points generated with SPheno and micrOMEGAS
<p>These two datasets were produced to be used in two lectures on Machine Learning for SUSY Model Building taught in <a href="https://indico.cern.ch/event/1214657/">pre-SUSY 2023 summer school</a> in Southampton. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/ml-for-model-building-susy-2023">here</a>.</p> <p>The datasets are as following:</p> <ul> <li>1 million points generated using SPheno only (so no Dark Matter relic density) for the cMSSM with the physical parameters randomly sampled from the table bellow. The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory</li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>The flattened SPheno outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> </ul> </li> <li>10 thousand points generated using SPheno, and which spectrum outputs was then fed to micrOMEGAS (MSSM model configured to accept low-scale slha files as input), with the physical parameters randomly sampled from the same table bellow. The columns are: <ul> <li>The same as above, in addition to</li> <li> 'Omega', 'dm_spin', 'dm_mass' obtained from the micrOMEGAS output, representing Dark Matter relic density, Dark Matter spin, Dark Matter mass, respectively.</li> </ul> </li> </ul> <p>The full list of columns can be seen in `column_names.txt` file.</p> <p>Versions:</p> <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>micrOMEGAS 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> <p>The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.</p> <p> </p>
Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island / Model dataset
<p>ECHAM6wiso and LMDZ6iso simulations, and python script analyzing model outputs, associated with the article :</p> <ul> <li>Amaelle Landais, Cécile Agosta, Françoise Vimeux, Olivier Magand, Cyrielle Solis, Alexandre Cauquoin, Niels Dutrievoz, Camille Risi, Christophe Leroy Dos Santos, Elise Fourré, Olivier Cattani, Bénédicte Minster, Frédéric Prié, Mathieu Casado, Aurélien Dommergue, Yann Bertrand, and Martin Werner (submitted to <a href="https://www.atmospheric-chemistry-and-physics.net/">Atmospheric Chemistry and Physics</a>, 2023) Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island.</li> </ul> <p>If you use the data or the python script, please cite the last version of this article available on <a href="https://www.egusphere.net/">https://www.egusphere.net/</a> or <a href="https://acp.copernicus.org/">https://acp.copernicus.org/</a>.</p> <p>Please also cite the articles related to the model simulations:</p> <ul> <li> <p>Risi, C., Bony, S., Vimeux, F., and Jouzel, J.: Water-stable isotopes in the LMDZ4 general circulation model: Model evaluation for present-day and past climates and applications to climatic interpretations of tropical isotopic records, Journal of Geophysical Research Atmospheres, 115, https://doi.org/10.1029/2009JD013255, 2010.</p> </li> <li> <p>Cauquoin, A. and Werner, M.: High-Resolution Nudged Isotope Modeling With ECHAM6-Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data, Journal of Advances in Modeling Earth Systems, 13, e2021MS002532, https://doi.org/10.1029/2021MS002532, 2021.</p> </li> <li> <p>Cauquoin, A., Werner, M., and Lohmann, G.: Water isotopes -- climate relationships for the mid-Holocene and preindustrial period simulated with an isotope-enabled version of MPI-ESM, Climate of the Past, 15, 1913–1937, https://doi.org/10.5194/cp-15-1913-2019, 2019.</p> </li> </ul>
An exploratory approach to estimate point emission sources
<p>The main purpose of this dataset is to improve the spatial and temporal distribution of the public power, refineries, manufacturing, and construction emissions in the European emission inventories using Portugal as a case study. More information about it can be found in the published article "An exploratory approach to estimate point emission sources" (<a href="https://doi.org/10.1016/j.atmosenv.2023.120026">https://doi.org/10.1016/j.atmosenv.2023.120026</a>). This dataset includes the following folders and files:</p> <p>1. Source_Info</p> <p> 1.1 Sources_Info_NFR_1_A_1_a.xlsx: this excel file includes the public plant names, situation, region, location, start, fuel used, power (MW), technology, treatment of gas effluents, stack height (m), and facility ID.</p> <p> 1.2 Sources_Info_NFR_1_A_1_b.xlsx: this excel file includes the refineries' names, situation, region, location, start, fuel power (MW), stack height (m), and facility ID.</p> <p> 1.3 Sources_Info_NFR_1_A_2.xlsx: this excel file includes the manufacturer's names, situation, region, NFR code, energy balance (PT_code), capital (€), stack height (m), production index code (PT_code), and facility ID.</p> <p>2. Spatial_Location</p> <p> 2.1. NFR_1_A_1_a.gdb: geodatabase with the public power locations and their facility ID (polygons and points shapefiles).</p> <p> 2.2. NFR_1_A_1_b.gdb: geodatabase with the refineries' locations and their facility ID (polygons and points shapefiles).</p> <p> 2.3. NFR_1_A_2.gdb: geodatabase with the manufacturer's locations and their facility ID (polygons and points shapefiles).</p> <p> 2.4. UrbanAreas.gdb: geodatabase with the Portuguese urban areas (polygons shapefiles).</p> <p>3. Temporal_Profiles</p> <p> 3.1. Hourly</p> <p> 3.1.1. Hourly_NFR_1_A_1_a.xlsx: excel file with the hourly profiles of the public power sites in Portugal's mainland (using wood waste, natural gas, hard coal, another thermal, “Central de Ciclo Combinado do Pego”, and “Central Termoeléctrica de Lares”) and Madeira island (using natural gas, fuel, and urban solid waste).</p> <p> 3.1.2. Hourly_NFR_1_A_2_construction.xlsx: excel file with the hourly profiles for construction activities by Portuguese municipalities (305 municipalities).</p> <p> 3.2. Monthly</p> <p> 3.2.1. Monthly_NFR_1_A_1_a.xlsx: excel file with the monthly profiles of the public power sites in the Azores (using fuel oil and diesel).</p> <p> 3.2.2. Monthly_ProductionIndex.xlsx: excel file with the monthly profiles of the Portuguese industries (including information for twenty-five types of activities).</p> <p>4. Emission factors</p> <p>4.1. EF_NFR1_A_1_a.xlsx: excel file with the emission factors for the public power sector.</p> <p>4.2. EF_NFR1_A_2.xlsx: excel file with the emission factors for the refinery activities.</p> <p>4.3. EF_NFR1_A_b.xlsx: excel file with the emission factors for the manufacturing and construction combustion activities.</p> <p>4.4 Fuels_to_EF.xlsx: excel file linking the fuels with available emission factors.</p> <p>5. Data_Sources.docx: this file provides the sources of the dataset.</p>
EW-ino scan points from "SModelS v2.3: enabling global likelihood analyses" paper
<p>Input SLHA and SModelS output (.smodels and .py) files from the paper "<a href="https://arxiv.org/abs/2306.17676">SModelS v2.3: enabling global likelihood analyses</a>". The dataset comprises 18544 electroweak-ino scan points and can be used to reproduce all the plots presented in the paper.</p> <ul> <li><strong>ewino_slha.tar.gz</strong> : input SLHA files including mass spectra, decay tables and cross sections</li> <li><strong>ewino_smodels_v23_combSRs.tar.gz</strong> : SModelS v2.3 output with combineSRs=True and combineAnas = ATLAS-SUSY-2018-41,CMS-SUS-21-002 (primary v2.3 results used in section 4, Figs. 2-6)</li> <li><strong>ewino_smodels_v23_bestSR.tar.gz</strong> : SModelS v2.3 output with combineSRs=False and combineAnas = ATLAS-SUSY-2018-41,CMS-SUS-21-002 (used only in Fig. 2)</li> <li><strong>ewino_smodels_v21.tar.gz</strong> : SModelS v2.1 output with combineSRs=False (used only in Fig. 2)</li> </ul> <p>Changes w.r.t. version 1: removed 13 SLHA input files, which had wrong neutralino2 decays due to a bug in <a href="https://github.com/BAllanach/softsusy">softsusy</a> 4.1.11; recomputed smodels_v23_combSRs results with sigmacut=1e-3 fb. See comments on <a href="https://scipost.org/submissions/2306.17676v2/">https://scipost.org/submissions/2306.17676v2/</a> for details.</p>
Dataset for: Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration
<p>Dataset containing raw and treated isotope-ratio mass spectrometry and laser absorption spectroscopy data, accompanying the manuscript "Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration". This dataset also contains O2 concentration and partial pressure data used for figures in the associated manuscript.</p>
Optimized stationary points on the potential energy surface of the reaction of atomic oxygen O(3P) with acrylonitrile
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P) + acrylonitrile potential energy surface (PES).</p> <p>The PES has been published in our article “A Computational Analysis of the Reaction of Atomic Oxygen O(<sup>3</sup>P) with Acrylonitrile”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2021</strong>, 12958, 339-350), that can be found in https://doi.org/10.1007/978-3-030-87016-4_25 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D) + CH2CHCN and CN + CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D) + CH<sub>2</sub>CHCN (acrylonitrile) and CN + CH<sub>2</sub>CHCN.</p> <p>The PES has been published in our article “A Theoretical Investigation of the Reactions of N(<sup>2</sup>D) and CN with Acrylonitrile and Implications for the Prebiotic Chemistry of Titan”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 246-259), that can be found in https://doi.org/10.1007/978-3-031-10562-3_18 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</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.