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40,091 results for “Record”
Supplementary data for: "The influence of reef isostasy, dynamic topography, and glacial isostatic adjustment on the Last Interglacial sea-level record of Northeastern Australia"
<p>This repository contains supplementary information and data for the paper: ""The influence of reef isostasy, dynamic topography, and glacial isostatic adjustment on the Last Interglacial sea-level record of Northeastern Australia", submitted to Communications Earth & Environment.</p> <p>This version (1.1) was produced to answer comments from reviewers.</p>
Acoustic recording under landfast sea ice near glacier
<p>A hydrophone was deployed in February 2022 underneath landfast sea ice in Tempelfjorden, Svalbard. The hydrophone was located approximately 2 km from the glacier. Several major events were recorded by vibrations sensors on the ice next to the hydrophone. Source triangulation identified that the events were coming from the glacier wall. This dataset includes the recording of one of the events (with the event starting at about 0:14), and a sample thereof.</p>
1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History
<p><strong>Context. </strong>This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 million cells.</p> <p><strong>Content.</strong> The data published in this repository correspond to a first release, i.e. a diachronic slice of one register every 8 to 9 years. Unfortunately, the remaining data are currently under embargo. Their publication will take place as soon as possible, and at the latest by the end of 2023. In the meantime, the data presented here correspond to a large subset of 2,844 pages, which already allows to investigate most research hypotheses.</p> <p><strong>Description. </strong>The population censuses, digitized by the <a href="https://www.lausanne.ch/vie-pratique/culture/bibliotheques-et-archives/archives.html">Archives of the city of Lausanne</a>, continuously cover the evolution of the population in Lausanne throughout the 19th century, starting in 1805, with only one long interruption from 1814 to 1831. Highly detailed, they are an invaluable source for studying migration, economic and social history, and traces of cultural exchanges not only with Bern, but also with France and Italy. Indeed, the system of tracing family origin, specific to Switzerland, allows to follow the migratory movements of families long before the censuses appeared. The bourgeoisie is also an essential economic tracer. In addition, censuses extensively describe the organization of the social fabric into family nuclei, around which gravitate various boarders, workers, servants or apprentices, often living in the same apartment with the family.</p> <p><strong>Production. </strong>The structure and richness of censuses have also provided an opportunity to develop automatic methods for processing structured documents. The processing of censuses includes several steps, from the identification of text segments to the restructuring of information as digital tabular data, through Handwritten Text Recognition and the automatic segmentation of the structure using neural networks. Please note that the detailed extraction methodology, as well as the complete evaluation of performance and reliability is published in:</p> <ul> <li>Petitpierre R., Rappo L., Kramer M. (2023). <em>An end-to-end pipeline for historical censuses processing</em>. International Journal on Document Analysis and Recognition (IJDAR). doi: <a href="https://doi.org/10.1007/s10032-023-00428-9">10.1007/s10032-023-00428-9</a></li> </ul> <p><strong>Data structure.</strong> The data are structured in rows and columns, with each row corresponding to a household. Multiple entries in the same column for a single household are separated by vertical bars ⟨|⟩. The center point ⟨·⟩ indicates an empty entry. For some columns (e.g., street name, house number, owner name), an empty entry indicates that the last non-empty value should be carried over. The page number is in the last column.</p> <p><strong>Liability. </strong>The data presented here are not curated nor verified. They are the raw results of the extraction, the reliability of which was thoroughly assessed in the above-mentioned publication. We insist on the fact that for any reuse of this data for research purposes, the implementation of an appropriate methodology is necessary. This may typically include string distance heuristics, or statistical methodologies to deal with noise and uncertainty.</p>
New Data Types in Data Management and Archiving [Webinar recording]
<p>New Data Types in Data Management and Archiving workshop focused on the management, archiving and access to new types of data (NDTs), i.e. administrative, transactional and social media data. The program consisted of four presentations tackling various issues related to handling the NDTs in data repositories and sharing these data in the community of social researchers. Martin Vávra (CSDA) was speaking about current capacities among CESSDA SPs for handling NDTs, Brian Kleiner (FORS) was talking about the coordinated approach to handling NDTs CESSDA SPs. Yevhen Voronin (GESIS) gave a presentation about social media data sharing in social research and Pascal Jurgens (Johannes Gutenberg University Mainz) was speaking about Social Science in the Embattled Digital Age: Adversarial Creation, Use and Sharing of New Data Types. The speakers’ presentations were followed by the panel discussion, where audience members were encouraged to participate and brought in their own experiences of archivists, data managers and researchers. The event was a part of the CESSDA training activities.</p> <p>The video is available on the <a href="https://www.youtube.com/watch?v=j13GsqwDO2Q">CESSDA Training YouTube channel.</a></p>
Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130
<p>The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products.</p> <p>This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from:</p> <p>GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq</p> <p>The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using</p> <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> <p>with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 .</p> <p>the first 10 lines of gbifid.tsv.gz as extracted via</p> <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> <p>are:</p> <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> <p>Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via:</p> <p>https://gbif.org/occurrence/2997162320</p> <p>and</p> <p>https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771</p> <p>respectively.</p> <p>This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them. </p> <p>In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it. </p> <p>Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .</p>
Azcorra2023 - Raw fiber photometry recordings
<p>Raw data from fiber photometry recordings of different subtypes (Vglut2+, Calb1+, Anxa1+ and Aldh1a1+ as well as DAT+) SNc dopamine neurons labelled with GCaMP6f, as used in Azcorra et al. Nat Neuro 2023. Metadata for these recordings (recording location, mouse sex...) can be found in the pre-processed dataset (see below).</p> <p>The code used to pre-processed this data to get DF/F is available on GitHub (<a href="https://github.com/DombeckLab/Azcorra2023/releases/tag/Azcorra2023">https://github.com/DombeckLab/Azcorra2023/releases/tag/Azcorra2023</a>) and Zenodo (DOI: 10.5281/zenodo.7872052, <a href="https://zenodo.org/record/7872052">https://zenodo.org/record/7872052</a>). We have also made the pre-processed data available on Zenodo (DOI: 10.5281/zenodo.7871982, <a href="https://zenodo.org/record/7871982">https://zenodo.org/record/7871982</a>). The code necessary to analyze this data and generate the figures shown in the manuscript is is found in that same GitHub repository as the pre-processing code above.</p>
MPIC OMI Total Column Water Vapour (TCWV) Climate Data Record
<p>The upload contains a long-term data set of 1°x 1° monthly mean total column water vapour (TCWV) retrieved in the visible "blue" spectral range from global measurements of the Ozone Monitoring Instrument (OMI). The TCWV data set covers the time range from January 2005 to December 2020.</p>
Ecological consumption and production [Webinar recording]
<p>The aim of the webinar was to present data on ecological consumption and production that are available at a European level and more specifically in the Czech Republic. The presenters also talked about what the data tell us about how people think about the environmental consequences of food production.</p> <p>Firstly, Martin Vávra demonstrated how to search for data on ecological consumption and pro-environmental behaviour in various data repositories (e.g. CSDA and GESIS). His data discovery demonstration was followed by the presentation by Jan Vávra who talked about his research study on ecological production and self-supplementation. He showed how widespread gardening is in the Global North and what role it plays in individuals' lives and the economies of nation states. The data he presented are available via the CSDA data catalogue. The final presentation was on “Environmental behaviour of Czech households and individuals with a focus on food waste” and it involved the issue of ecological consumption. Researcher Radka Hanzlová introduced the “Food for the future” research project. It was based on a survey of Czech people’s opinions on food waste and other aspects of ecological consumption that took place in 2022.</p> <p>Following the presentations, a few points were raised in the discussion, namelly 1) the surprisingly low percentage of alleged vegetarians in the presented population sample of the Public Opinion Research Centre, 2) the sociodemographic background of people buying organic food and 3) the difficulty to compare across borders due to different methodologies being used in the research.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=nH7aH7dAi84&t=1101s"> the CESSDA Training YouTube channel</a>.</p>
Making Social Science Research Transparent [Webinar recording]
<p>High-quality data have the potential to be reused in many ways. Archiving and publishing your data properly is at the core of making your data FAIR and will enable both your future self as well as others to get the most out of your data. Recently, more and more scientific journals are implementing open data policies, leading to researchers' dilemmas about where, when and how to publish the data. Consequently, the way that social science research is conducted and disseminated is gradually changing. A crucial element of that change is research transparency. Introduction to the topic took place in the first part of the event.</p> <p>In the second part, panellists presented in-depth the processes, policies and tools implemented for facilitating transparent research in the social sciences. They discussed the processes that need to be in place for an open research cycle, the role of data archives and repositories in sharing research data and materials, tools for reproducing research findings in practice, collaborations between archives and social science journals, and implementing Transparency and Openness Promotion Guidelines in different social science disciplines.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=B-phrIMETGk"> the CESSDA Training YouTube channel</a>.</p>
Data management in the social sciences in Macedonia [Webinar recording]
<p>This webinar aimed to introduce social science researchers to the basic principles of data management, including the creation of a Data management plan, which is an important tool for planning the research project.</p> <p>The webinar consisted of three parts. The first part introduced researchers with the basic principles of data management including the benefits of adopting Data management plans (DMPs). The DMP follows the research projects’ life cycle, starting with the initial phases of Planning and Organization and documentation of research data. This part also included a presentation of best practices for creation of appropriate structure of folders and data files, as well as instructions for their naming, documentation and organization.</p> <p>The second part of the webinar focused on the following three phases of the project life cycle: Data processing, Preservation and Protection. Contemporary social science presumes the respect of high level ethical standards during the handling of research data, in accordance with legal rules and best practices in this area.</p> <p>The last part of the webinar was dedicated to the phases of Publication - familiarizing the researchers with the possibilities of data preservation and publishing; and Data discovery - discussing the ways and means to acquire social science data, including the secondary use of data produced by other researchers.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=n05WTs58CMY"> the CESSDA Training YouTube channel</a>.</p>
Research Data Management and data protection in the Social Sciences [Workshop recording]
<p>This online workshop organized by The Austrian Social Science Data Archive (AUSSDA) focused on the Research Data Management basics, Data Management Plans and common data protection issues in the Social Sciences.</p> <p>The first part of the workshop was dedicated to RDM basics and Data Management Plans (DMPs). In many projects, DMPs are mandatory deliverables that need to be submitted at the beginning of a project and are updated throughout the project life cycle. During the workshop, it was explained which aspect funders expect to be part of DMPs in Social Sciences and how researchers can benefit from (writing) these documents.</p> <p>In the second part of the workshop, data protection issues that are common in Social Sciences were addressed and how they can be handled. In particular, differences in the curation of quantitative and qualitative data need in order to comply with data protection regulations in general and AUSSDA deposit guidelines in particular. Presentation on how AUSSDA scans quantitative data for potential data protection violations using STATA and gives participants the opportunity to test the code on their own data and devices.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=DhiL9J-Iwqg"> the CESSDA Training YouTube channel</a>.</p> <p> </p>
Occurrence records used to develop a climatic suitability model for emerald ash borer in DDRP
<p>Presence records used to calibrate and validate a climatic suitability model for emerald ash borer in the DDRP platform (Degree-Days, Risk, and Phenological event mapping) (Barker et al. 2023). The first sheet ("Records") of the Excel file provides the range (native or invaded), continent, country, state or province, locality, latitude, and longitude of origin for each record. The "Coords_est" column indicates whether the coordinates were estimated from city- or county-level information (1 = yes, 0 = no). The year in which the record was collected is provided if known. The second sheet of the Excel file ("References") provides a list of references for each record source.</p>
Anonymisation for data sharing in practice [Online Workshop. Recording]
<p>The goal of this event was to show trainers the tools they need to teach the fundamentals of data anonymisation and disclosure control in training sessions while also giving them hands-on experience with current open source technologies (sdcMicro). Some of the concepts and techniques presented, included k-anonymity, top/bottom coding and aggregation with practical examples and recommendations on incorporating anonymisation into research designs.</p> <p> </p> <p>The video is available on<a href="https://www.youtube.com/watch?v=JeJ6OOxXZwo&t=328s"> the CESSDA Training YouTube channel</a>.</p> <p> </p> <p> </p>
How to Ensure Researchers Share Their FAIR Data: Practical Tips and Tools [Online Workshop, Recording]
<p>The online hands-on workshop was aimed at trainers and support staff covering critical elements of data sharing and available tools and resources for supporting Open Science including:<br> • Open Science resources and Data Management Planning<br> • Consent and Ethical considerations<br> • Legislation and Licence frameworks<br> The objectives of the workshop were i) to raise awareness of key tools and resources available for Open Science training ii) to enable a platform to exchange ideas regarding key training topics and iii)n to provide training materials and worksheets for future reuse.<br> The workshop consisted of presentations, demos, a roundtable discussion on ethical considerations, a showcase of licence frameworks at different European archives and an exercise with all participants fostering an exchange of experiences focused on learnt lessons.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=uztTCRFRZHg"> the CESSDA Training YouTube channel</a>.</p>
Journal and Data Archive Collaboration Forum [online event recording]
<p>The availability of research data underlying articles published in journals is becoming a common practice in scientific communication. The European Commission and other funders of scientific research have set high expectations for scientists towards openness and availability of scientific work and results. Scientific publishers, through journals and scholarly publications are the main point of realising open science in practice.<br> <br> This event was part of the continuous Journals Outreach initiative (<a href="https://www.cessda.eu/Training/Journals-outreach">https://www.cessda.eu/Training/Journals-outreach</a>), bringing together CESSDA service providers (SPs) with Social Science & Humanities Journals. <strong>Its target audiences were publishers, editors, researchers, and CESSDA Service providers. </strong>The event was also an opportunity for publishers/journals to highlight new initiatives in research data services linked to scientific publications.<br> <br> The video is available on<a href="https://www.youtube.com/watch?v=zCKoyzLifkg"> the CESSDA Training YouTube channel</a>.</p>
Thinking about Vector Symbolic Architectures (video recording)
<p>Video recording of the keynote presentation "Thinking about Vector Symbolic Architectures" given on 2023-06-15 at the <a href="https://sites.google.com/ltu.se/midnightvsa/home?authuser=0">Midnight Sun Workshop on Vector Symbolic Architectures</a> in Luleå, Sweden.</p> <p><strong>Abstract</strong></p> <p>Vector Symbolic Architectures are defined in terms of a very small set of operators acting on a vector space. The task of the VSA researcher is to discover the implications that follow from the definition in terms of the systems that can be implemented with VSAs. The VSA definitions are the researcher’s raw materials, but they also need tools to transform those raw materials into useful hypotheses and system designs. One important tool for a researcher is a conceptual framework, which specifies how the researcher thinks about VSAs and relates them to the other things they know. It is the researcher’s mental model of how VSAs work. The primary requirement for a conceptual framework is that it is productive; it should make it easy for the researcher to generate interesting hypotheses and designs. These hypotheses and designs don’t have to be correct, just plausible. Beating them into shape is a different part of the research process. Most VSA research papers contain a statement of the VSA definition. Very few mention the researcher’s conceptual framework. In this talk I will sketch out my conceptual framework - how I think about Vector Symbolic Architectures - in the hope that it might be interesting and useful to other researchers.</p>
Magnetic Tape Recorder Dataset
<p>This repository contains the datasets collected and used in the research project:</p> <p>O. Mikkonen, A. Wright, E. Moliner and V. Välimäki, “Neural Modeling Of Magnetic Tape Recorders,”<br> in <em>Proceedings of the International Conference on Digital Audio Effects (DAFx)</em>,<br> Copenhagen, Denmark, 4-7 September 2023.</p> <p>A pre-print of the article is available in <a href="https://arxiv.org/abs/2305.16862">arXiv</a>.<br> The code is open-source and published in <a href="https://github.com/01tot10/neural-tape-modeling">GitHub</a>.<br> The accompanying web page can be found from <a href="http://research.spa.aalto.fi/publications/papers/dafx23-neural-tape/">here</a>.</p> <p><strong>Overview</strong></p> <p>The data is divided into various subsets, stored in separate directories. The data contains both <em>toy data</em> generated using a software emulation of a reel-to-reel tape recorder, as well as <em>real data</em> collected from a physical device. The various subsets can be used for training, validating, and testing neural network behavior, similarly as was done in the research article.</p> <p><strong>Toy and Real Data</strong></p> <p>The <em>toy data</em> was generated using <a href="https://github.com/jatinchowdhury18/AnalogTapeModel/">CHOWTape</a>, a physically modeled reel-to-reel tape recorder. The subsets generated with the software emulation are denoted with the string `CHOWTAPE`. Two variants of the toy data was produced: in the first variant, the fluctuating delay produced by the simulated tape transport was disabled, and in the second kind, the delay was enabled. The latter variants are denoted with the string `WOWFLUTTER`.</p> <p>The <em>real data</em> is collected using an Akai 4000D reel-to-reel tape recorder. The corresponding subsets are denoted with the string `AKAI`. Two tape speeds were used during the recording: 3 3/4 IPS (inches per second) and 7 1/2 IPS, with the corresponding subsets denoted with '3.75IPS' and '7.5IPS' respectively. On top of this, two different brands of magnetic tape were used for capturing the datasets with different tape speeds: Maxell and Scotch, with the corresponding subsets denoted with 'MAXELL' and 'SCOTCH' respectively.</p> <p><strong>Directories</strong></p> <p>For training the models, a fraction of the inputs from <a href="https://zenodo.org/record/3824876">SignalTrain LA2A Dataset</a> was used. The training, validation, and testing can be replicated using the subsets:</p> <ul> <li>ReelToReel_Dataset_MiniPulse100_AKAI_*/ (hysteretic nonlinearity, real data)</li> <li>ReelToReel_Dataset_Mini192kHzPulse100_AKAI_*/ (delay generator, real data)</li> <li>Silence_AKAI_*/ (noise generator, real data)</li> <li>ReelToReel_Dataset_MiniPulse100_CHOWTAPE*/ (hysteretic nonlinearity, toy data)</li> <li>ReelToReel_Dataset_MiniPulse100_CHOWTAPE_F[0.6]_SL[60]_TRAJECTORIES/ (delay generator, toy data)</li> </ul> <p>For visualizing the model behavior, the following subsets can be used:</p> <ul> <li>LogSweepsContinuousPulse100_*/ (nonlinear magnitude responses)</li> <li>SinesFadedShortContinuousPulse100*/ (magnetic hysteresis curves)</li> </ul> <p><strong>Directory structure</strong></p> <p>Each directory/subset is made of up of further subdirectories that are most often used to separate the training, validation and test sets from each other. Thus, a typical directory will look like the following:<br> ```<br> [DIRECTORY_NAME]<br> ├── Train<br> │ ├── input_x_.wav<br> │ ...<br> │ ├── target_x_.wav<br> │ ...<br> └── Val<br> │ ├── input_y_.wav<br> │ ...<br> │ ├── target_y_.wav<br> │ ...<br> ├── Test<br> │ ├── input_z_.wav<br> │ ...<br> │ ├── target_z_.wav<br> │ ...<br> ```</p> <p>While not all of the audio is used for training purposes, all of the subsets share part of this structure to make the corresponding datasets compatible with the dataloader that was used.</p> <p>The input and target files denoted with the same number `x`, e.g. `input_100_.wav` and `target_100_.wav` make up a pair, such that the target audio is the input audio processed with one of the used effects. In some of the cases, a third file named `trajectory_x_.npy` can be found, which consists of the corresponding pre-extracted delay trajectory in the `NumPy` binary file format.</p> <p><strong>Revision History</strong></p> <ul> <li>Version 1.1.0 <ul> <li>Added high-resolution (192kHz) dataset for configuration (SCOTCH, 3.75 IPS)</li> </ul> </li> <li>Version 1.0.0 <ul> <li>Initial publish</li> </ul> </li> </ul>
ATLAS Backward Trajectory Dataset for the Palau Atmospheric Observatory Balloon-borne ozonesonde record 2016-2019
<p>The ATLAS Backward Trajectory Dataset for the Palau Atmospheric Observatory (PAO) Balloon-borne ozonesonde record V1.0 provides backward trajectory data in NetCDF format calculated by the transport module of the Lagrangian Chemistry and Transport Model ATLAS (Wohltmann and Rex, 2009; Wohltmann et al., 2010) for coinciding Electrochemical Concentration Cell (ECC) ozonesonde measurements from the PAO located in Koror, Palau (7.3420° N, 134.4722° E), in the Tropical West Pacific (TWP) from 2016- October 2019 (Müller 2020, Müller et al. 2023). 30-days backward trajectories with a time step of 10 minutes were initialized at the time of an ozonesonde measurement at the PAO for every tenth ozonesonde reading within a profile and for a total number of 138 soundings (= days) and between 0 and 20 km altitude.<br> The model was driven by 3D wind fields, temperatures and diabatic heating rates from the ECMWF ERA5 reanalysis dataset (1.125° x 1.125°) with a 3 hour temporal resolution (compare Hoffmann et al., 2019). The model uses a hybrid vertical coordinate (zeta) which gradually transforms from "pressure" at the surface to "potential temperature" in the stratosphere (see Wohltmann and Rex, 2009). The corresponding vertical velocities change from vertical winds in pressure coordinates to diabatic heating rates.</p> <p> </p> <p>The Palau ECC record is currently being continued, will be available in the SHADOZ database in SHADOZ standard format in the near future, and can for now be found here: https://zenodo.org/record/6920648.</p> <p><strong>Please email katrin.mueller@awi.de and let us know what your intended purpose for the use of the data is. You will then receive updates if an improved version becomes available.</strong></p>
Meteors recorded with allsky cameras in 2016
<p>This dataset consists of videos of meteors, classified by the citizen science project "Flashes of the Universe" through Zooniverse and videos taken in 2016 by the camera network of the Instituto de Astrofísica de Canarias in collaboration with the Universidad Politécnica de Madrid (UPM) and the Agrupación Astronómica de Madrid Sur (AAMS).</p> <p>With the collaboration of The Spanish Foundation for Science and Technology (FECYT) - Ministry of Science and Education.</p>
Climate Record of Wintertime Wave-Affected Marginal Ice Zones in the Atlantic Arctic based on CryoSat-2,2010-2022
<p>This record contains data related to article "A 12-Year Climate Record of Wintertime Wave-Affected Marginal Ice Zones in the Atlantic Arctic based on CryoSat-2 ". Wave-affected marginal ice zone (MIZ) is an integral part of the sea ice cover, and a key region for air-ice-sea interactions in polar regions. We develop a novel retrieval algorithm for wave-affected MIZs based on the delay-Doppler radar altimeter onboard CryoSat-2 (CS2). CS2 waveform power and the waveform stack statistics are used to determine the MIZ locations. Based on the CS2 data since 2010, we generate a record of wave-affected MIZs in the Atlantic Arctic, spanning 12 winters between 2010 and 2022.</p> <p>The dataset contains two parts. First, the txt file contains retrieved MIZ locations obtained from each single track. Each column of data is represented by the corresponding CS2 product name, year, month, day, hour, regional flag (1:GS region,2:NS region,3:BS region), the start longitude and latitude of the marginal ice zone on each track, the end longitude and latitude of the marginal ice zone.</p> <p>Second,the gridded dataset is based on the along-track MIZ retrieval results, and it records the presence of the MIZ on the monthly basis. The latitude-longitude grid is adopted, with 2 degrees of longitude spacing and 1 degree of latitude spacing. Each file contains the following variables: the time, the MIZ flag, and the location flag. In this new version, we provide files in both NetCDF and GeoTIFF formats for users to download.</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.