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195 results for “Active Regions”

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edi52/100

Bonanza Creek LTER: Active Layer Depth or Permafrost Presence for the Regional Site Network

The initial goal (2000-2013) of these data was to define the presence/absence of permafrost within 2.5m of the surface in the regional site network. Efforts were focused mainly on sites where this was not easily deduced. The final subset of sites (2015 � present) are distributed across the 3 ecoregions of the RSN and primarily in older aged wet sites. The permafrost distribution in interior Alaska is discontinuous and dynamic; susceptible to fire and climate disturbances. Therefore, sites included in this long-term monitoring dataset may cease to be monitored as permafrost degrades and disappears or may be monitored again if permafrost is reestablished.

openOpenNov 2025View details →

zenodo48/100

Plasma-Prescribed Active Region Static Extrapolation Dataset

A repository of extrapolations using RBF-FD Magnetohydrostatic techniques with imposed plasmas, based on the SHARP solar image photospheric magnetic field library.

openmit-licenseSep 2024View details →

zenodo48/100

Gridded active layer thickness across northern permafrost regions from 2003 to 2020

<ol><li>This dataset provides the annual gridded active layer thickness (ALT) across northern permafrost regions (NPR) at 1 km resolution for the period 2003–2020. The ALT in permafrost regions refers to the upper layer of soil that thaws and refreezes annually as a result of seasonal temperature variations. It is a critical parameter in permafrost studies because it determines the depth to which plant roots can penetrate and influences various ecological and engineering processes. This dataset was produced based on the relationship between available ALT site measurements (2966 site-years) and satellite observations of annual gridded predictors, including vegetation, temperature, soil, and topography, using a Random Forest (RF) approach. The annual ALT map for the NPR was generated using the ensemble mean of ALT from the ten best RF predictions. Extensive uncertainty analysis was also conducted. </li><li>The dataset can be viewed at https://liuzh833.users.earthengine.app/view/altv1</li></ol>

opencc-by-4.0Nov 2023View details →

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Identifying Coronal Mass Ejection Active Region Sources: An automated approach - Catalogue results

Catalogue of Coronal Mass Ejection (CME) active region sources. Includes a database version and a simplified .csv version. For full details, refer to the source code at <a href="https://github.com/JulioHC00/cmesrc">https://github.com/JulioHC00/cmesrc</a>. We include a README file for each describing each column. We also include the raw data used to generate the catalogue so that results may be reproduced following the steps detailed in <a href="https://github.com/JulioHC00/cmesrc">https://github.com/JulioHC00/cmesrc</a>. This is a collection of data from other works and we provide it only to allow the results to be reproduced Below, we detail the data sources for the raw_data folders ============================== RAW DATA SOURCES ============================== DIMMINGS FOLDER Data is from Solar Demon, .csv was provided by Emil Kraaikamp through private communication. <blockquote> Solar Demon – an approach to detecting flares, dimmings, and EUV waves on SDO/AIA images Emil Kraaikamp, Cis Verbeeck J. Space Weather Space Clim. 5 A18 (2015) DOI: 10.1051/swsc/2015019 </blockquote> HARPNUM_TO_NOAA FOLDER Obtained from http://jsoc.stanford.edu/doc/data/hmi/harpnum_to_noaa/all_harps_with_noaa_ars.txt LASCO FOLDER This CME catalog is generated and maintained at the CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA. Downloaded from https://cdaw.gsfc.nasa.gov/CME_list/ MVTS FOLDER Data from <blockquote> Angryk, R.A., Martens, P.C., Aydin, B. et al. Multivariate time series dataset for space weather data analytics. Sci Data 7, 227 (2020). https://doi.org/10.1038/s41597-020-0548-x </blockquote> Available at the Harvard Dataverse <blockquote> Angryk, Rafal; Martens, Petrus; Aydin, Berkay; Kempton, Dustin; Mahajan, Sushant; Basodi, Sunitha; Ahmadzadeh, Azim; Xumin Cai; Filali Boubrahimi, Soukaina; Hamdi, Shah Muhammad; Schuh, Micheal; Georgoulis, Manolis, 2020, "SWAN-SF", https://doi.org/10.7910/DVN/EBCFKM, Harvard Dataverse, V1 </blockquote> The DT_SWAN folder contains the same data but with extra columns obtained directly from the Joint Science Operations Center (JSOC) through the python package drms.

opencc-by-4.0Jul 2024View details →

edi48/100

CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).

The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.

openCC0Aug 2025View details →

zenodo44/100

Dataset for "Helicity proxies from linear polarisation of solar active regions"

The \(\alpha\) effect is believed to play a key role in the generation of the solar magnetic field. A fundamental test for its significance in the solar dynamo is to look for magnetic helicity of opposite signs in the two hemispheres, and at small and large scales. However, measuring magnetic helicity is compromised by the inability to fully infer the magnetic field vector from observations of solar spectra, caused by what is known as the \(\pi\) ambiguity of spectropolarimetric observations. We decompose linear polarisation into parity-even and parity-odd E and B polarisations, which are not affected by the \(\pi \) ambiguity. Furthermore, we study whether the correlations of spatial Fourier spectra of B and parity-even quantities such as E or temperature T are a robust proxy for magnetic helicity of solar magnetic fields.  We analyse polarisation measurements of active regions observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics observatory. Theory predicts the magnetic helicity of active regions to have, statistically, opposite signs in the two hemispheres. We then compute the parity-odd EB and TB correlations, and test for systematic preference of their sign based on the hemisphere of the active regions. We find that: (i) EB and TB correlations are a reliable proxy for magnetic helicity, when computed from linear polarisation measurements away from spectral line cores, and (ii) E polarisation reverses its sign close to the line core. Our analysis reveals Faraday rotation to not have a significant influence on the computed parity-odd correlations. The EB decomposition of linear polarisation appears to be a good proxy for magnetic helicity independent of the \(\pi\) ambiguity. This allows us to routinely infer magnetic helicity directly from polarisation measurements. The full article can be found at https://arxiv.org/abs/2001.10884

opencc-by-4.0Jun 2020View details →

zenodo44/100

Total O3 columns at polar regions: TOMCAT/SLIMCAT passive and active tracers and merged SAOZ-MSR2 dataset

Passive and active total ozone columns simulated by the chemical transport model TOMCAT/SLIMCAT (Chipperfield, 1999) and the merged dataset of total ozone from Système d'Analyse par Observation Zénithale (SAOZ, Pommereau and Goutail, 1988) ground-based instruments and Multi-Sensor Reanalysis (MSR2, van der A et al., 2010, 2015) for the polar stations described in the Table here below. Table. Arctic and Antarctic stations included in the study: station name and ID, latitude, longitude and measurement periods of SAOZ and MSR2 datasets. <table> <tbody> <tr> <td> Station (ID) </td> <td> Lat, Lon </td> <td> SAOZ dataset </td> <td> MSR2 dataset </td> </tr> <tr> <td> Eureka, Nunavut (EU) </td> <td> 80.1°N, 86.4°W </td> <td> 2005-2020 </td> <td> 1990-2022 </td> </tr> <tr> <td> Ny-Alesund, Svalbard (NY) </td> <td> 78.9°N, 11.9° E </td> <td> 1991-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Thule, Greenland (TH) </td> <td> 76.5°N, 68.8°W </td> <td> 1999-2003, 2005-2016 </td> <td> 1990-2022 </td> </tr> <tr> <td> Scoresbysund, Greenland (SC) </td> <td> 70.5°N, 22.0°W </td> <td> 1991-2017, 2019-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Sodankyla, Finland (SK) </td> <td> 67.4°N, 26.6° E </td> <td> 1991-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Sondre Stromfjord, Greenland (SS) </td> <td> 67.0°N, 50.6°W </td> <td> 2018-2022 </td> <td> 1990-2022 </td> </tr> <tr> <td> Zhigansk, Russia (ZH) </td> <td> 66.8°N, 123.4° E </td> <td> 1992-2013 </td> <td> 1990-2022 </td> </tr> <tr> <td> Salekhard, Russia (SA) </td> <td> 66.5°N, 66.7°E </td> <td> 2002-2016 </td> <td> 1990-2022 </td> </tr> <tr> <td> Marambio, Antarctica (MB) </td> <td> 64.2°S, 56.7°W </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Dumont d’Urville, Antarctica (DD) </td> <td> 66.7°S, 140.0°E </td> <td> 1989-2021 </td> <td> 1989-2021 </td> </tr> <tr> <td> Rothera, Antarctica (RO) </td> <td> 67.6°S, 68.1°W </td> <td> 2007-2021 </td> <td> 1989-2021 </td> </tr> <tr> <td> Syowa, Antarctica (SW) </td> <td> 69.0°S, 39.6°E </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Neumayer, Antarctica (NM) </td> <td> 70.7°S, 8.3°W </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Terra Nova, Antarctica (TN) </td> <td> 74.8°S, 164.5°E </td> <td> - </td> <td> 1989-2021 </td> </tr> <tr> <td> Concordia, Antarctica (DO) </td> <td> 75.1°S, 123.4°E </td> <td> 2007-2021 </td> <td> 1989-2021 </td> </tr> <tr> <td> Halley, Antarctica (HB) </td> <td> 75.6°S, 26.8°W </td> <td> - </td> <td> 1989-2021 </td> </tr> </tbody> </table>   Each file corresponds to the whole winter time data set of the station identified by its ID: O3_PassActSLIMCAT_CompositeMSR2SAOZ_ID.txt 1st column: Year 2nd column: Day of Year (DoY) 3rd column: Passive ozone column (without chemistry) modelled by TOMCAT/SLIMCAT (O3pasS). 4th column: Active ozone column (with full chemistry) modelled by TOMCAT/SLIMCAT (O3actS). 5th column: merged data from SAOZ observations and MSR2 (O3comp) Passive and active tracers of SLIMCAT were normalized to O3comp data in the beginning of the winter. NaN is used when there is no observation either from SAOZ instrument or MSR2 dataset or from the model.   References Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. Roy. Meteor. Soc., 132, 1179–1203, <a href="https://doi.org/10.1256/QJ.05.51">https://doi.org/10.1256/QJ.05.51</a>, 2006. Pommereau, J.-P., and Goutail, F.: O3 and NO2 ground-based measurements by visible spectrometry during arctic winter and spring 1988, Geophys. Res. Lett., 15, 891–894, <a href="https://doi.org/10.1029/GL015i008p00891">https://doi.org/10.1029/GL015i008p00891</a>,1988. van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Multi sensor reanalysis of total ozone, Atmos. Chem. Phys., 10, 11277–11294, <a href="https://doi.org/10.5194/acp-10-11277-2010">https://doi.org/10.5194/acp-10-11277-2010</a>, 2010. van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012, Atmos. Meas. Tech., 8, 3021–3035, <a href="https://doi.org/10.5194/amt-8-3021-2015">https://doi.org/10.5194/amt-8-3021-2015</a>, 2015.

opencc-by-4.0Apr 2023View details →

zenodo44/100

The Plasma-Prescribed Active Region Static Extrapolation Dataset

The Plasma-Prescribed Active Region Static Extrapolation (PARSE) Dataset consists of approximately seven thousand magnetohydrostatic extrapolations of solar active regions for use in statistical or machine learning applications. The extrapolations are based on the Spaceweather HMI Active Region Patch (SHARP) library (doi <a href="https://doi.org/10.1007/s11207-014-0529-3">10.1007/s11207-014-0529-3</a>), and the magnetohydrostatic extrapolation is performed by the routine detailed in Mathews et al 2022 (doi <a href="https://doi.org/10.1016/j.jcp.2022.111214">10.1016/j.jcp.2022.111214</a>).

opencc-by-4.0Aug 2023View details →

zenodo40/100

Samples of solar flares classes, active regions and time of occurrence

This dataset contains samples of solar flares measurements of classes X, M, C and B. For clarification, the flares are classified as follows: <ul> <li>Class X: flares \(>10^{-4} watts/m^{2}\)</li> <li>Class M: \(10^{-5} watts/m^{2} <\)flares \(<10^{-4} watts/m^{2}\)</li> <li>Class C: \(10^{-6} watts/m^{2}<\)flares\(<10^{-5} watts/m^{2}\)</li> <li>Class B: \(10^{-7} watts/m^{2}<\)flares\(<10^{-6} watts/m^{2}\)</li> </ul> This dataset was assembled with data from https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares The date (yyyy-mm-dd hh:mm:ss) the authors assembled the data is 2017-11-14 13:48:37 The original data source is the National Oceanic & Atmospheric Administration (NOAA), U.S. Departement of Commerce. Data description: <ul> <li>Class: Class of the flare: X, M, C or B</li> <li>Date: Date of occurence in yyyy-mm-dd format.</li> <li>AR: Active Region ID attributed by NOAA.</li> <li>Begin: time the flare begins in hh:mm:ss format.</li> <li>Max: time the flare reaches its max value in hh:mm:ss format.</li> <li>End: time the flare vanishes in hh:mm:ss format.</li> </ul> The dataset has 2,256 tuples divided as follows: <ul> <li>171 tuples with X class flares data (7.58%).</li> <li>572 tuples with M class flares data (25.35%).</li> <li>767 tuples with C class flares data (34%).</li> <li>746 tuples with B class flares data (33.07%).</li> </ul> The data collected refer to the period between August 25, 1996 and May 29, 2017.

opencc-by-4.0Nov 2017View details →

zenodo40/100

Рис. 4. Фотографии жиΛых гнезΑ в заказнике «Амурский» на искусственных гнезΑовых треногах (сΛева — «активное», справа — «засеΛенное») Fig. 4. Photos of inhabited nests in the Amursky wildlife reserve that are located on artificial nesting structures ("active" on the left and "inhabited" on the right) in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019

Рис. 4. Фотографии жиΛых гнезΑ в заказнике «Амурский» на искусственных гнезΑовых треногах (сΛева — «активное», справа — «засеΛенное») Fig. 4. Photos of inhabited nests in the Amursky wildlife reserve that are located on artificial nesting structures ("active" on the left and "inhabited" on the right)

opencc-by-4.0Feb 2021View details →

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Figure 2 in Data on nocturnal activity of Darevskia rudis (Bedriaga, 1886) (Sauria: Lacertidae) in Central Black Sea Region, Turkey

Figure 2: The major nocturnal activities of Darevskia rudis. (a: leaving the shelter, b: mobility in its habitat, c: foraging, d: fronting to artificial light source and e: feeding).

opencc-by-4.0Dec 2018View details →

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Figure 5. Spectrum region HSQC 1H-13C in Molecular docking studies and evaluation of the antiretroviral activity and cytotoxicity of the species Lafoensia pacari Saint-Hilaire

Figure 5. Spectrum region HSQC 1H-13C (A) and HMBC 1H-13C (B) with region of aromatics compounds of the acid wash water acetate subfraction (88.83%) of L. pacari obtained in methanol-d4.

opencc-by-4.0Dec 2022View details →

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Raw data for Figures in: LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions, Ferraioli et al., Nucleic Acids Res. 2024

These datasets represent raw data for the preparation of Figures in: Ferraioli S, Sarigol F, Prakash C, Filipczak D, Foisner R, Naetar N. (2024) LAP2alpha facilitates myogenic gene expression by preventing nucleoplasmic lamin A/C from spreading to active chromatin regions. Nucleic Acids Res.2024 Sep 4:gkae752. doi: 10.1093/nar/gkae752.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Active Regions with Multiple NOAA Numbers, 2011-2019

This is a list of active regions which have been identified as lasting for multiple rotations, and their subsequent-rotation NOAA number designation. For more information on the creation of this dataset, please refer to (ApJ link coming).

opencc-by-4.0Aug 2023View details →

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Active Region Magnetograms for Solar Flare Prediction: Reduced Resolution Dataset Images

This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.jq2bvq898.  These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).  These data are the reduced resolution (224x224 pixels) images in .png format.

opencc-by-4.0Mar 2023View details →

dryad40/100

Active region magnetograms for solar flare prediction: Reduced resolution dataset

In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of reduced resolution images (see usage notes below).

opencc-zeroApr 2023View details →

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Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1307 through 1505

This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n.  These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO).  These data are the full sized images (600x600 pixels) for active regions (ARs) 1307 through 1505 in .fits format.

opencc-by-4.0Apr 2023View details →

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Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1064 through 1306

opencc-by-4.0Apr 2023View details →

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Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1506 through 1707

opencc-by-4.0Apr 2023View details →

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Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1708 through 1918

opencc-by-4.0Apr 2023View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

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

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

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

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

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

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

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

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record