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4,283 results for “Database”
EoRNA, a barley gene and transcript abundance database
<p>A high-quality, barley gene reference transcript dataset (BaRTv1.0, Rapazote-Flores et al. 2019), was used to quantify gene and transcript abundances from 22 RNA-seq experiments, covering 843 separate samples. Using the abundance data we developed a Barley Expression Database (EoRNA* – Expression of RNA) to underpin a visualisation tool that displays comparative gene and transcript abundance data on demand as transcripts per million (TPM) across all samples and all the genes. EoRNA provides gene and transcript models for all of the transcripts contained in BaRTV1.0, and these can be conveniently identified through either BaRT or HORVU gene names, or by direct BLAST of query sequences. Browsing the quantification data reveals cultivar, tissue and condition specific gene expression and shows changes in the proportions of individual transcripts that have arisen via alternative splicing. TPM values can be easily extracted to allow users to determine the statistical significance of observed transcript abundance variation among samples or perform meta analyses on multiple RNA-seq experiments. * Eòrna is the Scottish Gaelic word for Barley</p>
Voice Conversion Challenge 2020 database v1.0
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. This repository contains the training and evaluation data released to participants, target speaker’s speech data in English for reference purpose, and the transcriptions for evaluation data. For more details about the challenge and the listening test results please refer to [4] and README file. </pre> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14.</pre>
East Asian calendar conversion database
<p>SQL dump (MySQL 5.x) from a database for converting East Asian (here: Chinese, Japanese, Korean) calendars. The data is also available for download at http://authority.dila.edu.tw/docs/open_content/download.php, where separate datasets for Chinese, Korean, and Japanese calendars are available. An interface for querying the data is here: http://authority.dila.edu.tw/time/.</p> <p>Using the Julian Day as common standard it allows mapping of East Asian calendar dates to the Julian, proleptic Gregorian, and Gregorian calendar. It is the currently largest and most detailed open access dataset for this purpose. The database was compiled between 2008 and 2011 at the Dharma Drum Institute of Liberal Arts, Jinshan, Taiwan. The data for the Japanese calendar is based on material provided by Takashi Suga.</p> <p>A publication describing the dataset is Marcus BINGENHEIMER, Jen-Jou HUNG, Simon WILES, Boyong ZHANG. “Modeling East Asian Calendars in an Open Source Authority Database.”<em> International Journal of Humanities and Arts Computing</em> Vol. 10-2, pp. 127-144. DOI: 10.3366/ijhac.2016.0164.</p>
The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research
<p>We present a database of acoustic transfer functions of the Hearpiece, an openly available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human subjects and a KEMAR with anthropometric ears for five reinsertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between subjects as well as variations due to reinsertion into the same ear. Also, the results show that KEMAR measurements represent a median human ear well for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications.</p>
REDB-BR: Rainfall Erosivity Database for Brazil
<p>This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset.</p> <p>It provides the R factor from the Universal Soil Loss Equation (USLE) in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset.</p> <p>The R factor was calculated trough 73 erosivity index regression equations, which mostly uses a relation between monthly precipitation and annual precipitation, the Modified Fournier Index (MFI), and represents a good approximation to locals with no sub-hourly data for long periods. </p> <p>The main product of REDB-BR is the R factor map, available also as a .tif raster. The database also includes the equations shapefile, Thiessen Polygons shapefile and the equations table. </p>
histoGe Database
<p>A database to be used by histoGe for making gamma spectroscopy analysis. Taken originally from http://nucleardata.nuclear.lu.se/toi/</p>
Database - A Calculus of Tracking: Theory and Practice
<p>A manually curated sample (Top 100 Alexa domains only) of a OpenWPM database obtained from Princeton Web Census (https://webtransparency.cs.princeton.edu/webcensus/). The sample is used to instantiate the model for the paper "<a href="https://petsymposium.org/2021/files/papers/issue2/popets-2021-0027.pdf">A Calculus of Tracking: Theory and Practice</a>" to appear in PETS 2021.</p> <p>Accepted manuscript: https://petsymposium.org/2021/files/papers/issue2/popets-2021-0027.pdf</p> <p>GitHub page: https://github.com/giorgioditizio/calculus_of_tracking</p> <p> </p>
Atmospheric River Database for the Himalayas
<p>Atmospheric Rivers (ARs) are long and narrow regions of intense moisture transport in the lower troposphere. The dataset comprises of Atmospheric Rivers that have happened over the Himalayan Basins from 1982 to 2018. It includes the dates and times, duration, intensity/magnitude, tracks, and categories of the ARs.</p> <p> </p> <p><strong>File Names and description:</strong></p> <p><strong>1. </strong><strong>ERA5_Persistant_Database2000km:</strong> This file includes the date, times, average Integrated Water Vapor Transport (IVT) magnitude (kg.m^-1s^-1), starting IVT, maximum IVT, and duration of ARs. These terms are explained below in greater details.</p> <p><strong>Column “Date”:</strong></p> <p>Gives the date and time (in Coordinated Universal Time UTC) of each AR timestep. The IVT data used to identify ARs is 6-hourly (00UTC, 06UTC, 12UTC and 18UTC).</p> <p><strong>Column “AR_ID”:</strong></p> <p>Each identified persistent AR, lasting for at least 18 hours, is given a unique ID, which remains same for all timesteps of the AR. This column gives the ID of ARs. The ID of an AR is based on the year in which the AR occurred, the letters “AR”, and the occurrence serial of the AR in the year. For example, the first AR in 1990 has ID 1980AR1. If the AR lasted for 10 timesteps, all 10 timesteps will have the same ID.</p> <p><strong>Column “Ind”:</strong></p> <p>This column gives the python index of IVT data in 6-hour yearly data, giving the date and time of each AR timestep. This column can be ignored since the same information is more directly available in “Date” column.</p> <p><strong>Column “AvgIVT”:</strong></p> <p>This column gives the average IVT magnitude (kg.m^-1s^-1) along the AR major axis, i.e., the gridcells that have maximum IVT along the AR track. For example, the first value corresponds to the average of all values from column <em>“0”</em> to column <em>“88”,</em> which give the IVT magnitude at each gridcell of the major axis of the first timestep.</p> <p> </p> <p><strong>Column “StartIVT”:</strong></p> <p>This column gives the IVT magnitude (kg.m^-1s^-1) at the initial gridcell on the first timestep when AR condition was identified.</p> <p><strong>Column “ARDuration”:</strong></p> <p>This column gives duration of the AR in hours; for example, an AR lasting for three timesteps will have the duration of 18 hours, an AR lasting for four timesteps will have duration of 24 hours.</p> <p><strong>Column “MaxIVT”:</strong></p> <p>This column gives the maximum of all IVT values (kg.m^-1s^-1) at the starting gridcells on each timestep of an AR.</p> <p><strong>Column “ARCat”:</strong></p> <p>This column gives category of the AR, based on IVT magnitude and duration of the ARs. Six categories have been defined, Cat0 denoting the weakest AR and Cat5 denoting the strongest AR. More details on this can be found in the accompanying paper.</p> <p><strong>Column “0” to the end.</strong></p> <p>These columns give the IVT magnitude (kg.m^-1s^-1) at each gridcell of the major axis of each AR timestep.</p> <p> </p> <p><em>Note that the cyclone dates were not available before 1982, so AR dates for 1979 to 1981 includes cyclonic IVT structures.</em></p> <p><strong>2. </strong><strong>ERA5_Persistant_Database_lats_2000km:</strong> The file gives the latitudes of grid points of maximum IVT, i.e., the latitude of major axes of ARs throughout their duration.</p> <p><strong><em>Columns “Date”, “AR_ID”, “Ind”, “AvgIVT”, “StartIVT”, “ARDuration”, “MaxIVT”, “ARCat” are the same as given above for “ERA5_Persistant_Database2000km.csv” file.</em></strong></p> <p><strong>Column “0” to end.</strong></p> <p>These columns give the latitude ( in degrees North) at each gridcell of the major axis of each AR timestep.</p> <p><strong>3. </strong><strong>ERA5_Persistant_Database_lons_2000km:</strong> The file gives the longitudes of grid points of maximum IVT, i.e., the longitudes of major axes of ARs throughout their duration</p> <p>Columns “Date”, “AR_ID”, “Ind”, “AvgIVT”, “StartIVT”, “ARDuration”, “MaxIVT”, “ARCat” are the same as given above for “ERA5_Persistant_Database2000km.csv” file.</p> <p><strong>Column “0” to end.</strong></p> <p>These columns give the longitude (in degrees East) at each gridcell of the major axis of each AR timestep</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances (MAT-Version)
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>This entry stores the measurements in the MAT format for use in Matlab/Octave. The measurements are identical to the once stored in the SOFA format available at <a href="https://doi.org/10.5281/zenodo.55418">https://doi.org/10.5281/zenodo.55418</a></p>
Water vapor database for atmospheric correction of Landsat imagery
<p>Atmospheric correction is a crucial preprocessing step for the analysis of optical satellite imagery like Landsat. Among the radiance-modifying gases, atmospheric water vapor is spatially and temporally variable, and cannot be measured reliably from the Landsat sensors. As such, atmospheric correction of Landsat data requires spatially and temporally explicit auxiliary information about atmospheric water vapor content.</p> <p>We have compiled a water vapor dataset that can be readily used to perform atmospheric correction of Landsat images. The dataset was generated by a global processing of the MODIS MOD05/MYD05 collection 6.1 products (<a href="https://doi.org/10.1029/2002JD003023">https://doi.org/10.1029/2002JD003023</a>; MODIS Atmosphere L2 Water Vapor Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: <a href="https://doi.pangaea.de/10.5067/MODIS/MOD05_L2.006">doi:10.5067/MODIS/MOD05_L2.006</a>, <a href="https://doi.pangaea.de/10.5067/MODIS/MYD05_L2.006">doi:10.5067/MODIS/MYD05_L2.006</a>). The dataset is comprised of daily global water vapor data for February 2000 to December 2020 for each land-intersecting Worldwide Reference System 2 (WRS-2) scene, as well as a monthly climatology that can be used if no daily value is available.</p> <p>The dataset was generated by the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE v. 2.0 / 3.6, <a href="https://github.com/davidfrantz/force">https://github.com/davidfrantz/force</a>, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. The water vapor dataset can be readily ingested into the FORCE Level 2 Processing System (Frantz et al. 2016, <a href="https://doi.pangaea.de/10.1109/TGRS.2016.2530856">doi:10.1109/TGRS.2016.2530856</a>) to perform atmospheric correction of Landsat imagery. This dataset is an update of <a href="https://doi.pangaea.de/10.1594/PANGAEA.893109">https://doi.pangaea.de/10.1594/PANGAEA.893109</a> and should be used from now on.</p>
Non-personalized HRIR databases with and without floor reflections
<p>Non-personalized HRIR databases in SOFA format [1] with and without floor reflections. Floor reflections were simulated with a plywood board between a Head-And-Torso Simulator (HATS) and a dodecahedral loudspeaker. These recordings were captured at the anechoic chamber of the University of Aizu.</p> <p><strong>Apparatus</strong></p> <ul> <li><strong>Head and Torso Simulator (HATS):</strong> 5128-C (Brüel & Kjær—B&K, Denmark).</li> <li><strong>Preamplifier:</strong> NEXUS preamplifier (B&K).</li> <li><strong>Audio interface:</strong> Babyface (RME, Germany).</li> <li><strong>Software used:</strong> ScanIR [2].</li> <li><strong>Loudspeaker:</strong> Self-built regular dodecahedral loudspeaker (7.2 kg). This could be circumscribed by a sphere of 25 cm in diameter. Drivers (P800K—FOSTEX, Japan) were attached to 3 mm acrylic plates.</li> <li><strong>Audio source: </strong>A one-second sine-sweep tone sampled at 96 kHz generated with ScanIR.</li> <li><strong>Floor simulation: </strong>Plywood board 181x91x1.2 cm weighing 13 kg (density ρ = 658 kg/m3 i.e., a relatively firm board).</li> <li><strong>Locations:</strong> 72 azimuths from 0º to 355º in steps of 5º (counterclockwise measured) with a combination of elevation φ = [±60º, ±30º, 0º] at a distance of 153 cm from the center of the HATS’ head to the center of the loudspeaker.</li> </ul> <p>Other details on the procedure and how this was used in our research are found in [3]. HRIRs were capture with and without the plywood board. they are called here “echoic” and “anechoic,” respectively. In addition to the original sampling rate, we include here resampled versions at 44.1 and 48 kHz.</p> <p><strong>Filenames</strong></p> <p>For both anechoic and echoic databases, download:<br> AizuEle@[<em>sample rate</em>].zip</p> <p>Other SOFA files:<br> AizuEle[<em>XXX</em>]@[<em>sample rate</em>].sofa, replace ‘<em>XXX</em>’ with ‘WIF’ for echoic recordings and with ‘WOF’ for anechoic ones.</p>
The World Loanword Database (WOLD) 2009
<p>Haspelmath, Martin & Tadmor, Uri (eds.) 2009. World Loanword Database. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://wold.clld.org)</p>
PhenoMiner database
<p>Phenotypes play a key role in inferring the complex relationships between genes and human heritable diseases. PhenoMiner is a research project aimed at the capture and encoding of phenotypes in the scientific literature. This should provide insights into the complex processes involved in human diseases as well as enabling semantic interoperability with existing biomedical ontologies such as those that describe human anatomy, genetics and behaviours.</p> <p>The PhenoMiner database contains the results of an FP7 Marie Curie fellowship project on text/data-mining technology - natural language processing, machine learning and conceptual analysis. It builds on insights gained from semantic parsing to extract structured information about phenotypes from whole sentences - in contrast to existing techniques which often apply string matching. The system exploits the wealth of scientific data locked within the scientific literature in databases such as PubMed Central and Europe PMC to extract the semantic vocabulary of phenotypes that scientists use. The system will provide scientists, clinicians and informaticians with the data and tools they need to gain new insights into Mendelian diseases.</p> <p>The database currently contains over 4800 phenotype terms automatically mined from full scientific articles and then associated to Online Mendelian Inheritance of Man (OMIM) disorders. All data is provided without manual filtering.</p> <p>Please contact the author for further information and comments/suggestions.</p> <p>- Nigel Collier (collier@ebi.ac.uk)</p>
The BDNYC Database
<p>This is the initial data release of the BDNYC Database which contains the astrometry, photometry, spectra, and metadata for the 198 M6-T8 dwarfs that comprise the sample of Filippazzo et al. (2015).</p>
glottolog-data: Glottolog database 2.7
<p>Hammarström, Harald & Forkel, Robert & Haspelmath, Martin & Bank, Sebastian. 2016. Glottolog 2.7. Jena: Max Planck Institute for the Science of Human History. (Available online at http://glottolog.org)</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>For details have a look at README.md.</p> <p>The same measurement can be downloaded as MAT files at <a href="https://doi.org/10.5281/zenodo.4459911">https://doi.org/10.5281/zenodo.4459911</a></p> <p>This dataset is further described in (see the PDF file)</p> <p>H. Wierstorf, M. Geier, A. Raake, S. Spors - A Free Database of Head-Related<br> Impulse Response Measurements in the Horizontal Plane with Multiple Distances.<br> In 130th AES Conv. 2011, eBrief 6.</p> <p> </p>
AntarcticaLC2000: The new Antarctic land cover database for the year 2000
<p>Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) was produced using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100,000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73,268.81 km<sup>2</sup> (0.537%), 225,937.26 km<sup>2</sup> (1.656%), and 13,345,460.41 km<sup>2</sup> (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality.</p>
IBP-database and environmental IBP sequences for functional analysis of microalgae
<p>Database for analysis of ice binding protein (IBP) sequences (Uhlig et al. (2015)):</p> <p>(1) DUF3494_seqs_Uniprot.fasta: full length sequences with DUF3494 domain used for the calculation of the backbone tree in the phylogenetic placement</p> <p>(2) env_IBPs.fasta: potential IBP sequences from one Arctic and five Antarctic sea ice metatranscriptomes (Sanger or 454)</p> <p>(3) DUF3494_substree_fig2a_UniprotIDs.txt: UniProtIDs for subtree in Fig 2a</p> <p>(4) DUF3494_confirmed_IBPactivity_UniprotIDs.txt: UniProtIDs for sequences with confirmed IBP function of the protein</p> <p>If using this dataset please cite the following publication: Uhlig, C., Kilpert, F., Frickenhaus, S., Kegel, J.U., Krell, A., Mock, T., Valentin, K., Beszteri, B., (2015) The significance of antifreeze proteins for eukaryotic microbial communities of Arctic and Antarctic sea ice, The ISME Journal, 9, 2537–2540, doi:10.1038/ismej.2015.43</p>
DigiMedFor Forest Management Map Database
<p>Geodatabase of Forest Management Map created in DigiMedFor project.</p>
Svalbard Surge Database 2024 (RGI2000-v7.0-G-07)
<p>We have developed a new database of surge-type glaciers in Svalbard by combining existing compilations and reviewing studies examining their dynamics. Our database is based upon the Global Land and Ice Measurements from Space (GLIMS) database (König et al. 2014), which is now incorporated into RGI 7.0 (RGI 7.0 Consortium 2023) and consists of 1,583 glaciers in Svalbard. Therefore, the first five fields come from the RGI 7.0 database:</p> <ul> <li><strong>rgi_id</strong>: Glacier ID from RGI database.</li> <li><strong>glims_id</strong>: Glacier ID from GLIMS database.</li> <li><strong>cenlon</strong>: Longitude of glacier centre point.</li> <li><strong>cenlat</strong>: Latitude of glacier centre point.</li> <li><strong>glac_name</strong>: Name of glacier.</li> </ul> <p>Our compilation of existing Svalbard-wide glacier surge databases is sourced from several studies: Lefauconnier and Hagen (1991) [LH1991]; Hagen et al. (1993) [H1993]; Sevestre and Benn (2015) [SB2015]; Farnsworth et al. (2016) [F2016]; Kääb et al. (2023) [KA2023]; and Koch et al. (2023) [KO2023]. The compilation of LH1991 only covers eastern Svalbard and is focused on marine-terminating glaciers but is included as it contains several important details on surge characteristics. H1993 is the original database of glaciers across Svalbard and similarly contains details of historical surges. The current RGI 7.0 database defines the “surge status” of each glacier according to Sevestre and Benn (2015): no evidence of surging (0); possible surge (1), probably surge (2), and observed surge (3). Where the SB2015 database does not have corresponding evidence from one of the other compilations, we determine the glaciers surge status to be ‘undefined’ and do not include it in the S_All field. The F2016 compilation was manually translated into the RGI 7.0 database. The glacier names described in F2016 often referred to tributaries which are now combined into single glacier catchments (e.g., Nuddbreen / Strongbreen), hence we manually combined these entries. The recent compilations from KA2023 and KO2023 were manually transcribed from tables in PDF files. The subsequent eight fields document each compilation:</p> <ul> <li><strong>SB2015</strong>: Surge database from Sevestre and Benn (2015). [0-3]</li> <li><strong>F2016</strong>: Surge database from Farnsworth et al. (2016). [0-1]</li> <li><strong>H1993</strong>: Surge database from Hagen et al. (1993). [0-1]</li> <li><strong>LH1991</strong>: Surge database from Lefauconnier and Hagen (1991). [0-1]</li> <li><strong>KA2023</strong>: Surge observations from Kääb et al. (2023). This data set is based on manual surge identification in annual Sentinel-1 interferometric wide-swath (IW) satellite radar backscatter differences between 2017 and 2022 (Kääb et al. 2023). This has been updated in this database (version 3) by mapping more recent surges from winter-to-winter differences 2022-2023, 2023-2024, and 2024-2025 using new IW data. Before 2017, no Sentinel-1 IW data are available over Svalbard, and we use 2015-2016 and 2016-2017 extended wide-swath data (EW) instead, acknowledging that these coarser data (compared to IW) might lead to less detailed surge identification, or overlooking of surges of small glaciers or surges accompanied by only limited backscatter changes. Based on these additional data, we are also able to update some surge information contained in the original KA2023, for instance concerning surge start and end years, and by adding the last year of strongly enhanced backscatter (before backscatter reduction). The new 2015-2025 backscatter-derived surge inventory over Svalbard contains now 40 surging glaciers (the 2017-2022 KA2023 contained 26 surging glaciers). [0-1]</li> <li><strong>KO2023</strong>: Surge observations from Koch et al. (2023). [0-1]</li> <li><strong>Other</strong>: Surge observations from other literature sources. [0-1]</li> <li><strong>S_Direct</strong>: All surges that have been directly observed. [0-1]</li> <li><strong>S_Indirect</strong>: All surges that have been indirectly observed e.g. from palaeo-glaciological analysis. [0-1]</li> <li><strong>S_All</strong>: All surges that have been either directly observed or inferred from the palaeo-glaciological record. [0-1]</li> </ul> <p>Contemporary and palaeo-glaciological evidence of surges is generally limited to the period ~1850–present, which broadly corresponds to the end of the LIA through to the modern-day. Where multiple surges have been recorded, we separate these using “;” in the database, and use “n/a” where the details of the surge have not been recorded. Surges have been classified as a binary 0 (not surge-type) or 1 (surge-type), with the exception of the Sevestre and Benn (2015) database as described above.</p> <p>The subsequent eight fields document (if known) the following characteristics for each glacier in Svalbard:</p> <ul> <li><strong>S_Onset</strong>: Surge Onset (Year)</li> <li><strong>S_Term</strong>: Surge Termination (Year)</li> <li><strong>S_Act_Vel</strong>: Max Active-Phase Velocity (m/d)</li> <li><strong>S_Qui_Vel</strong>: Mean Quiescence Velocity (m/d)</li> <li><strong>S_Term_Ch</strong>: Terminus Change (m)</li> </ul> <p>Here, we use 'n/a' for glaciers with no evidence of surging, whilst 'Not observed' is used where we have not observed any of the above characteristics for a glacier with evidence of surging.</p> <p>The final column contains references to where surges have been reported.</p> <p>Included in this version is also a version of the RGI for Svalvard with the new database included.</p> <div> </div>
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.