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3,113 results for “extremes”
Extreme precipitation records in Antarctica [Dataset]
<p>This is the dataset associated to the research 'Extreme precipitation records in Antarctica' published in <em>International Journal of Climatology</em>.</p> <p>This repository contains:</p> <ul> <li>Precipitation extremes for each <em>model</em> at every grid point for a duration of <em>xxx</em> days. Files named: <ul> <li>[<em>model</em>]_PCP_max_[<em>xxx</em>]d.csv <ul> <li>Dimensions for ERA5: [lons, lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: mm</li> </ul> </li> </ul> </li> <li>Dimensions to plot the precipitation extremes: lons (longitudes), lats (latitudes) and duration. Files named: <ul> <li>[<em>model</em>]_PCP_max_lats.csv <ul> <li>Dimensions for ERA5: [lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_lons.csv <ul> <li>Dimensions for ERA5: [lons]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_duration.csv <ul> <li>Dimensions: [time]</li> <li>Units: days</li> </ul> </li> </ul> </li> <li>World Precipitation Records from 1 day. File named: <ul> <li>Max_WR_from1day.csv (first row duration [days]; second row precipitation [mm])</li> </ul> </li> </ul> <p> </p> <p><strong>How to cite</strong></p> <p>If you use this dataset, please cite the accompanying paper as:</p> <p>González-Herrero, S.,Vasallo, F., Bech, J., Gorodetskaya, I., Elvira, B., & Justel, A. (2023). Extreme precipitation records in Antarctica.International Journal of Climatology, 43(7), 3125–3138. <a href="https://doi.org/10.1002/joc.8020">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Complementary code</strong></p> <p>You can find the jupyter notebooks to complement the research in: <a href="https://github.com/sergigonzalezh/Extreme_PCP_Scaling_Antarctica">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
Increased impact of heat domes on 2021-like heat extremes in North America under global warming
<p>The key codes and processed data for the paper.</p>
Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein
<p>These data support the manuscript entitled "Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein" by Heller, Shukla, Figueiredo, and Hansen.</p><p>This data should be used with the code provided on GitHub at https://github.com/hansenlab-ucl/R2_IDP_small_mol. Once downloaded, this directory should be extracted using the following command:</p><p> tar -xzvf Data.tar.gz</p><p>The directory should be saved with the name 'Data' placed in the same directory as the GitHub README.md file.</p><p><strong>This dataset contains: </strong><br><i>Nuclear Magnetic Resonance (NMR) spectroscopy data files (.ft2 format) including: </i></p><p>* 1H 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of the protein, non-structural protein 5A, domains 2 and 3 (NS5A-D2D3), in 1H_1D_ft2_data/</p><p>* 1H pseudo-2D Diffusion Ordered SpectroscopY (DOSY) data of 5-fluoroindole (50 uM) with and without NS5A-D2D3 (75 uM) in 1H_DOSY_data/</p><p>* 1H-15N Heteronuclear Single Quantum Coherence (HSQC) measurements of NS5A-D2D3 (40 uM) in the absence and presence of 5-fluoroindole (160 and 320 uM) in 1H_15N_HSQC_ft2_and_metadata/</p><p>* 19F 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_1D_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-lattice, R1,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R1eff_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-spin, R2,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R2eff_ft2_data/</p><p><i>Circular Dichroism (CD) data files (.txt format) including: </i></p><p>* CD measurements of NS5A-D2D3 at increasing concentrations in CD_data/no_molecule/</p><p>* CD measurements of NS5A-D2D3 with and without the small molecule, 5-fluoroindole CD_data/with_molecule/</p><p><i>Metadata </i></p><p>* Metadata from the Biological Magnetic Resonance Data Bank (https://bmrb.io/) used to determine scaling factors for the calculation of chemical shift perturbations in 1H_15N_HSQC_ft2_and_metadata/</p>
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
The glacier loss day as indicator for extreme glacier melt in 2022
<p>Data and scripts to reproduce the plots in Voordendag, A., Prinz, R., Schuster, L., and Kaser, G.: Brief communication: Brief communication: The Glacier Loss Day as indicator for a record negative glacier mass balance in 2022, The Cryosphere, 2023</p> <p>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</p>
Extreme Weather Event database over Aotearoa New Zealand
<p><strong>The Aotearoa New Zealand (ANZ) Extreme Weather Events (EWE) database </strong>(EWE_database_V1.0.0.xlsx)<strong> is a comprehensive record of extreme weather events in ANZ. The events listed in this database have been carefully assessed and categorized based on their meteorological significance, considering their rarity and whether they broke records or triggered official weather warnings. Some of the metrics used to classify each event rely on subjective judgment and expert opinions. The database captures meteorologically significant events, including those that have caused substantial damage to properties or led to casualties, and, in some cases, includes supplementary information about their socioeconomic impacts. The information in the EWE database is primarily sourced from the Meteorological Service of New Zealand Ltd (MetService) and the National Institute of Water and Atmospheric Research (NIWA). Additional impact data have been added from various media sources, with insured loss data for some events sourced from the Insurance Council of New Zealand (ICNZ).</strong></p> <p>Note - For more information about the database and the other additional files, please look into the Metadata (Metadata_EWE_V.1.0.0.docx) and the supplementary document (Supplementary document on EWE_V.1.0.0.docx).</p>
Extreme Metal Vocals Dataset (EMVD)
<p><strong>Extreme Metal Vocals Dataset (EMVD)</strong></p> <p>Version 1.0, October 2023</p> <p> </p> <p><strong>Created by</strong></p> <p>Modan Tailleur (1,3), Julien Pinquier (2), Laurent Millot (1), Corsin Vogel (1), Mathieu Lagrange (3)</p> <ol> <li> <p>ENS Louis-Lumière, Saint-Denis, France</p> </li> <li> <p>IRIT, Université de Toulouse, CNRS, UT3 Toulouse, France</p> </li> <li> <p>Nantes Université, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France</p> </li> </ol> <p> </p> <p><strong>Publication</strong></p> <p>If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset:</p> <p>@misc{tailleur2024emvddatasetdatasetextreme,<br> title={EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal}, <br> author={Modan Tailleur and Julien Pinquier and Laurent Millot and Corsin Vogel and Mathieu Lagrange},<br> year={2024},<br> eprint={2406.17732},<br> archivePrefix={arXiv},<br> primaryClass={cs.SD},<br> url={https://arxiv.org/abs/2406.17732}, <br>}</p> <p> </p> <p><strong>Description</strong></p> <p>The Extreme Metal Vocals Dataset (EMVD) comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of distorted voices and 40 minutes of clear voice recordings. These vocal recordings are from 27 different singers and are provided without accompanying musical instruments or post-processing effects. The distortion taxonomy within this dataset encompasses four distinct distortion techniques and three vocal effects, all performed in different pitch ranges.</p> <p> </p> <p><strong>How to use</strong></p> <p>To get an example on how to use this dataset for deep learning applications, please follow the link to the companion website: <a href="https://github.com/modantailleur/ExtremeMetalVocalsDataset">https://github.com/modantailleur/ExtremeMetalVocalsDataset</a></p> <p> </p> <p><strong>Label Taxonomy</strong></p> <p>The label taxonomy is as follows (see our paper for further details):</p> <p>Techniques:</p> <ul> <li>Clear Voice: high, mid, low</li> <li>Black Shriek: high, mid</li> <li>Death Growl: mid, low</li> <li>Hardcore Scream: high, mid, low</li> <li>Grind Inhale</li> </ul> <p>Effects:</p> <ul> <li>Pig Squeal</li> <li>Deep Gutturals</li> <li>Tunnel Throat</li> </ul> <p> </p> <p><strong>Recording procedure</strong></p> <p>For the recording sessions, a mobile setup was selected to accommodate as many singers as possible. An SM58 microphone was employed, chosen for its prevalence as a microphone commonly used by metal singers during live performances. A closed-back headphone served for music playback and provided the singers with a monitor of their own voice if they desired to hear it during recording. An audio interface <em>Scarlett 6i6</em> by <em>Focusrite</em> was responsible for connecting the laptop, microphone, and headset.</p> <p>In some cases, singers were recorded remotely using their own equipment (a stage microphone and an audio interface) which are documented in the database. These singers were provided with a video tutorial and explanatory documents to facilitate their participation in the project, with the main author remotely guiding them. Each singer was instructed to sustain three vowels—[a] as in "cat," [i] as in "ship," and [u] as in "book"—for a duration of five seconds each. They were required to maintain a consistent pitch not only within each vowel but also across all vowels produced. After this, they were asked to perform for approximately 15 seconds using the same vocal technique, but this time with lyrics of their choosing. The lyrics had to remain the same across all technique categories. Each vocal technique was recorded across several registers (high, mid, and low) depending on their relevance to the specific technique. It's worth noting that the Grind Inhale technique, although producible in multiple registers, was recorded in only one register, as many singers deemed it potentially harmful to their voice. A musical loop was provided in the singers' headphones during each recording.</p> <p> </p> <p><strong>Grading system</strong></p> <p>Each vocalist in this study underwent a comprehensive assessment of their comfort level with each vocal technique across the various vocal registers, employing a ranking system ranging from 0 to 5. A rank of 0 signifies that they never use this technique and are not sufficiently comfortable to produce it, which ultimately results in missing data in the dataset. A rank of 3 indicates occasional use, and a rank of 5 signifies that they use it in every live performance.This dataset provides supplementary insights into the singers' practices. These include the typical microphone-to-mouth distance employed by each vocalist during recording, as well as their professional status within the field of singing. The majority of the recordings were conducted onsite, within the familiar confines of the vocalist's chosen location, whether it be their home or a professional studio, utilizing equipment provided by the authors. However, some recordings were independently done by the vocalists themselves, leveraging their personal microphones and audio interfaces. In such instances, the authors remotely guided the recording process to ensure consistency and quality. Detailed equipment specifications have been documented.</p> <p>As authors noticed that the singers auto-evaluation ranking wasn’t very effective, the main author provided grades to individual audio files created by the singers, ranging from 0 to 2. A 2 grade suggests that the technique closely represents the intended vocal technique, 1 indicates that it moderately represents the vocal technique, and 0 signifies that the technique does not adequately represent the vocal technique. Audio files rated as 0 should not be employed for deep learning applications, but they are retained within the dataset in case future re-evaluation of the audio files is desired. Notably, approximately 70\% of the dataset's audio files received grades of 2 or 1 from the authors and are thus suitable for being used in diverse applications.</p> <p> </p> <p><strong>metadata_files.csv</strong></p> <p><em>file_name : </em>the name of the audio file</p> <p><em>singer_id : </em>the id of each singer (from 1 to 27)</p> <p><em>type : </em>whether the distortion employed is a technique, an effect, or a distortion that doesn’t fit any specific category</p> <p><em>name : </em>the name of the technique or of the effect employed by the singer (‘-’ if it doesn’t fit in any category)</p> <p><em>range</em> : the range employed by the singer (‘High’, ‘Mid’, or ‘Low’)</p> <p><em>vowel</em> : the vowel employed by the singer. ‘a’ if vowel [a] as in "cat", 'i' if vowel [i] as in "ship," and 'u' if vowel [u] as in "book"</p> <p><em>authors_rank</em> : the rank given by the authors (2, 1 or 0)</p> <p><em>duration(s) </em>: duration (in seconds) of the audio file</p> <p> </p> <p><strong>metadata_singers.csv</strong></p> <p><em>singer_id</em> : the id of each singer (from 1 to 27)</p> <p><em>gender : </em>the gender of the singer (« M » if male, « F » if female)</p> <p><em>status : </em>whether the singer is professional or non-professional (« Professional », or « Non-professional »)</p> <p><em>recording : </em>whether the recording was made onsite, with the authors equipment, or if it was guided remotely (« Onsite » or « Guided »)</p> <p><em>distance_to_microphone(cm) : </em>the distance chosen by the singer to the microphone (in centimeters)</p> <p><em>microphone : </em>model of microphone that was used for the recording</p> <p><em>audio_interface : </em>audio interface used for the recordings</p> <p><em>DAW : </em>Digital Audio Workstation (DAW) used for recording the singer (Ex: ProTools, Reaper etc...)</p> <p><em>ClearVoice_High, …, TunnelThroat : </em>singer’s rank (from 0 to 5) from his auto-evalution on each technique performed in each range.<br> </p> <p><strong>split_kfolds.csv</strong></p> <p>For deep learning applications, a k-fold cross-validation with 4 folds was performed and stored in the «split_kfolds.csv » file, reserving 20% of the training data for validation.</p> <p><em>file_name : </em><em>the name of the audio file</em></p> <p><em>split0, …, split3</em> : for each split, wether the file belongs to the train subset (‘train’), the evaluation subset (‘eval’), the validation subset (‘valid’) or if it isn’t used for training (‘-’)</p> <p> </p> <p><strong>Feedback</strong></p> <p>Please help us improve EMVD by sending your feedback to:</p> <ul> <li>Modan Tailleur: <a href="mailto:modan.tailleur@gmail.com">modan.tailleur@gmail.com</a></li> </ul> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We want to thank Oriol Nieto, Geoffroy Peeters, Christophe d'Alessandro and Boris Doval for fruitful discussion. We particularly want to thank Joshua Smith for guidance for the design of the taxonomy. We also want to thank the 27 singers for bringing this dataset to life.</p>
Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the northern Chihuahuan Desert site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)
The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the northern Chihuahuan Desert site, dominated by black grama (Bouteloua eriopoda), located in the Sevilleta National Wildlife Refuge in central New Mexico.
Extreme Drought in Grasslands Experiment (EDGE): High frequency measurements from the southern Great Plains site, Sevilleta National Wildlife Refuge, NM, USA (2013-2023)
The Extreme Drought in Grasslands Experiment (EDGE) is distributed across six representative grassland ecosystems of the central United States. EDGE serves as an important research platform for understanding the resistance and resilience of these grassland ecosystems to extreme prolonged drought as well as to changes in precipitation seasonality. This data package contains high-frequency environmental sensor measurements from the southern Great Plains site, dominated by blue grama (Bouteloua gracilis), located in the Sevilleta National Wildlife Refuge in central New Mexico.
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
Compound flood potential from river discharge and storm surge extremes at the global scale
<p>This dataset presents the results presented in <a href="https://doi.org/10.5194/nhess-20-489-2020">Couasnon et al. (2019) - Measuring compound flood potential from river discharge and storm surge extremes at the global scale</a>. For more information about the methods, please refer to the paper. This dataset was created using as input <a href="https://zenodo.org/record/3552820#.XmIdoVxKhaQ">time series of discharge and maximum storm surge at river mouths globally from 1980 - 2014</a>.</p> <p>If using this data, please cite: </p> <p>Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.</p>
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity
<p>This dataset contains evaluation data of the paper "Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity".</p> <p><strong>Motivation</strong></p> <p>We address the question whether the assumptions of signal-dependent and constant motor noise in a full skeletal model of the human upper body, together with the objective of movement time minimization, can predict reaching movements.</p> <p>For evaluation of the learned policy, two tasks are defined in the paper: a Fitts' Law type task and an elliptic via-point task.</p> <p><strong>General description of the dataset</strong></p> <ul> <li><strong>Fitts' Law Type Task</strong> <ul> <li>This dataset incorporates detailed information of all 6500 synthesized movements generated in the Fitts' Law type task (following the ISO 9241-9 standard).</li> <li> <p>For each of the 10 task conditions differing in distance between targets ("dist<xxx>" in filename)<br> and ID ('ID<xxx>' in filename), there are two .csv-files:<br> - one with detailed trajectory information on a sample-to-sample basis ("ISO_SAMPLES" in filename), and<br> - one with aggregated movement information on an episode basis ("ISO_METRICS" in filename).</p> </li> <li> <p>In addition, for each task condition and each of the 13 movement directions in the Fitts' Law type task,<br> we include 6 figures: Position, Velocity, and Acceleration Profiles, as well as 3D movement path, Phasespace, and Hooke plots.<br> Apart from the 3D plots, all figures use centroid projections of the respective trajectory onto the vector between initial and target position.<br> The first integer in the file name denotes the movement direction number, starting with "0" for movements between the targets 1 and 2, "1" for movements between the targets 2 and 3 etc.<br> The file "6_distance0.35_ID2_policy2100000_phasespace.png", e.g., shows velocity plotted againt position for all 50 movements between the targets 7 and 8 in the task condition with ID 2 and 35cm diameter of the target circle (see Fig 2 in Paper).</p> </li> </ul> </li> <li> <p><strong>Elliptic Task</strong></p> <ul> <li> <p>This dataset also contains two CSV-files with data of the trajectory generated by the final policy in the elliptic task:<br> - one with detailed trajectory information on a sample-to-sample basis ("ELLIPSE_SAMPLES" in filename), and<br> - one with aggregated movement information on an episode basis, where a new episode starts<br> whenever the target on the ellipse given to the policy switches ("ELLIPSE_METRICS" in filename).</p> </li> </ul> </li> </ul> <p><strong>Description of the .csv-files</strong></p> <ul> <li><em>SAMPLES </em>Files <ul> <li>"time": time after reaching the initial target (target 1 in Fig 2) for the first time (in seconds)</li> <li>"elv_angle_pos" - "flexion_pos": angle of respective independent DOF (in radians) *</li> <li>"elv_angle_vel" - "flexion_vel": angular velocity of respective independent DOF (in radians/s) *</li> <li>"end-effector_xpos_x" - "end-effector_xpos_z": 3D position of end-effector in global coordinates (in meters) *</li> <li>"target_xpos_x" - "target_xpos_z": 3D position of target sphere in global coordinates (in meters) *</li> <li>"end-effector_xvelp_x" - "end-effector_xvelp_z": positional velocities of end-effector (in meters/s) *</li> <li>"target_xvelp_x" - "target_xvelp_z": positional velocities of target sphere (in meters/s) *</li> <li>"accsensor_end-effector_x" - "accsensor_end-effector_z": positional acceleration of end-effector (in meters/s^2) *</li> <li>"E_elv_angle" - "E_flexion": activation of respective independent DOF *</li> <li>"E_elv_angle_derivative" - "E_flexion_derivative": derivative of activation of respective independent DOF *</li> <li>"difference_vec_x" - "difference_vec_z": vector between the end-effector attached to the index finger and the target, pointing towards the target (in meters) *</li> <li>"centroid_vel_projection": projection of end-effector velocity towards target (in meters/s) *</li> <li>"target_width": radius (!) of the target sphere (in meters) *</li> <li>"A_elv_angle" - "A_flexion": action vector</li> <li>"thorax_tx_frc" - "wrist_hand_r3_frc": net external force at respective DOF (including dependent and fixed DOFs such as "thorax_tx" (thorax translation))</li> <li>"reward": reward obtained in this step</li> <li>"step_type": 0=initial step of episode, 1=intermediate step of episode, 2=terminal step of episode</li> <li>"target_switch": whether the target switched in this step</li> <li>"discount": internal value of tf-agents (does not correspond to the discount factor gamma, which is additionally applied!)</li> <li>"thorax_tx_pos" - "wrist_hand_r3_pos": angle of respective dependent DOF (in radians)</li> <li>"thorax_tx_vel" - "wrist_hand_r3_vel": angular velocity of respective dependent DOF (in radians)</li> </ul> </li> <li><em>METRICS </em>Files <ul> <li>Index: episode ID</li> <li>"Init_X" - "Init_Z": initial position in global coordinates (in meters)</li> <li>"Init_X" - "Init_Z": target position in global coordinates (in meters)</li> <li>"Init_Distance": distance between last target (i.e., desired initial position) and current target (in meters)</li> <li>"Target_Width_Diameter": target width diameter (in meters)</li> <li>"Movement_ID": Index of Difficulty of current movement (using the Shannon Formulation) (in bits)</li> <li>"target_accuracy": 1 - (<remaining distance to target center at the end of the episode>/<target radius>) if end-effector is inside target, 0 else</li> <li>"movement_time": duration of the episode (in seconds)</li> <li>"episode_successful": whether episode terminated successfully within the permitted 1.5 seconds</li> <li>"dist2target": remaining distance to target center at the end of the episode (in meters)</li> </ul> </li> </ul> <p>-------------------------------------<br> * included in state space</p>
Dataset for "Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?"
<p>Datasets belonging to the paper "Long-term extreme response of an offshore turbine: How accurate are<br> contour-based estimates?" by Haselsteiner, Frieling, Mackay, Sander and Thoben.</p> <p>Available are:<br> * A 1000-year time series of hourly environmental conditions<br> * 516 1-hour time series of the mudline overturning moment, simulated using openFAST</p> <p> </p>
Strong-field quantum control in the extreme ultraviolet using pulse shaping
<p>Dataset for supporting the findings of the paper 'Strong-field quantum control in the extreme ultraviolet using pulse shaping' (<span>https://doi.org/10.1038/s41586-024-08209-y</span>)</p>
Global dry and hot extreme events detection
<p>Workflow for the global detection of dry and hot extreme weather events. ERA5 is the ECMWF Reanalysis of the climate. PET is potential reference evapotranspiration. PEI is the daily difference between precipitation and evapotranspiration averaged over the preceding days (here 30, 90 and 180). Data cubes are stored in zarr format. Statistics are saved in a csv table.</p>
StageIV-IRC – A High-resolution Dataset of Extreme Orographic Quantitative Precipitation Estimates (QPE) Constrained to Water Budget Closure for Historical Floods in the Appalachian Mountains
<h2>Quantitative Flood Estimation (QFE) in complex terrain remains a grand challenge in operational hydrology due to the lack of accurate high-resolution Quantitative Precipitation Estimates (QPE) at spatial and temporal resolutions needed to capture the variability of orographic precipitation, and where radar-based QPE are available there are significant biases due to the geometry and constraints of radar operations. Here, we present a high-resolution (i.e. 250m, 5minute-hourly) QPE dataset for the most extreme (flood-producing) events from 2008 to 2024 for 26 gauged basins (in total 215 events) in the Appalachian mountains constrained to meet basin-scale water budget closure through inverse rainfall-runoff modeling to correct the Next Generation Weather Radar (NEXRAD) Stage IV analysis (4km resolution, hourly) using a fully-distributed uncalibrated hydrological model that leverages recent advances in hydrologic modeling in mountainous regions (e.g. improved river routing and initial soil moisture estimation) (Liao and Barros, 2024a and 2024b). The corrected Stage IV analysis is referred to as StageIV-IRC (Inverse Rainfall Correction). Previously, a subset of this dataset informed the construction of a generalized QPE error model (Liao and Barros, 2023), supporting the development of water budget closure constrained QPE and providing physics insights into orographic QPE uncertainties for various radar-based products at high resolution in complex terrain. The unique advantage of the StageIV-IRC QPE is that it achieves water budget closure at the storm-flood event scale within observational uncertainty of streamflow observations, that is the golden standard in hydrological modeling. The QPE dataset is publicly available at: <a href="https://doi.org/10.5281/zenodo.14028867">https://doi.org/10.5281/zenodo.14028867</a></h2> <p><strong> </strong></p>
Repo for Compound Continental Risk of Multiple Extreme Floods in the United States
Open the record for dataset details and reuse information.
Continuously fluctuating selection reveals extreme granularity and parallelism of adaptive tracking
<p>Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown. We generated high-frequency, genome-wide allele frequency data from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December, a period of ~12 generations. Adaptation throughout the fundamental ecological phases of population expansion, peak density, and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of allele frequency change to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveal fitness-relevance of standing variation that is likely to be masked by inference approaches based on static population sampling, or insufficiently resolved time-series data. We propose such fine-scaled temporally fluctuating selection may be an important force maintaining functional genetic variation in natural populations and an important stochastic force affecting levels of standing genetic variation genome-wide.</p>
Ecological forecasts for marine resource management during climate extremes
<p><span>Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. <span>We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members.</span> Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.</span></p>
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 1. Карта района иссΛеΑований. 1 – граница зон раститеΛьности; 2 – граница поΑзон раститеΛьности; 3 – места сбора материаΛа; I – южная тайга; II – среΑняя тайга; III – северная тайга; IV – крайнесеверная тайга; V – ΛесотунΑра; VI – южная тунΑра; VII – северная тунΑра. Fig. 1. Map of the studied area. 1 – boundary of vegetation zones; 2 – boundary of vegetation subzones; 3 – collection points; I – southern taiga; II – middle taiga; III – northern taiga; IV – extremely northern taiga; V – forest tundra; VI – southern tundra; VII – northern tundra.
ScienceDex guides
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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.