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57 results for “ssh”
SSH Centre | Learning about the project
<p>Prof Chris Foulds and Prof Rosie Robison, co-leads of SSH CENTRE project, explain in this video how SSH CENTRE will provide inputs to contribute to EU policies by translating Social Science and Humanities knowledge to climate-energy-mobility policy action.</p>
About SSH CENTRE - partner perspective: Marten Boekelo
<p>Marten Boekelo from DuneWorks describes the SSH CENTRE project and what his organisation will bring to the project.</p>
About SSH CENTRE - partner perspective: Violeta Cabello
<p>Violeta Cabello, project coordinator at Basque Centre for Climate Change (BC3) explains in this video how this research institute will contribute to achieve SSH CENTRE project objectives.</p>
СШ-36 Советский шлем (SSh-36) Soviet helmet
[Ru] СШ-36 — стальной шлем образца 1936 года. Первый стальной шлем советского производства, принятый на вооружение РККА. [En] SSh-36 - steel helmet of the 1936 model. The first Soviet-made steel helmet, adopted by the Red Army. Не забывайте на мой ютуб заходить, жду от вас подписочки и комментов! https://www.youtube.com/c/NOQUALITY96/videos Source: Objaverse 1.0 / Sketchfab
SSH CENTRE - Mini-reports: Focus groups on "100 Climate-Neutral and Smart Cities by 2030"
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality. </p><p>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.</p><p>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.</p><p>The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe. </p><p>Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes. </p>
TS003 Daily Mean SSH
Open the record for dataset details and reuse information.
NeurOST-SSH Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using only L3 SSH (not including SST).</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
Multilingual publishing in the SSH in Poland and attitudes towards English
<p>This dataset contains responses to a self-constructed questionnaire that was designed to provide the following information: What languages are used for research dissemination in various SSH disciplines in Poland? What are the main languages of the research cited by authors in these disciplines? When languages other than Polish are used for research dissemination, are the results published in national or international venues? What are the prevailing reasons for language choices? What is the position of English in SSH disciplines in Poland? What is the attitude of Polish SSH scholars towards the dominance of English as the international language of science?</p> <p>The questionnaire was written in Polish and consisted of 52 items arranged in three thematic lines: multilingual publication practices, the role of English in research dissemination, and attitudes to English as the global language of science.</p> <p>The data were collected in an online survey based on Google Forms. The link to the questionnaire was distributed via email among scholars affiliated with the social sciences and humanities units of 20 Polish universities which took part in the Excellence Initiative – Research University competition, a funding programme launched in 2019 by the Ministry of Science and Higher Education (<a href="https://www.gov.pl/web/science/the-excellence-initiative---research-university-programme">https://www.gov.pl/web/science/the-excellence-initiative---research-university-programme</a>).</p> <p>Time of data collection: 12 October 2020–16 November 2020.</p> <p>Volume of data and the response rate: 12,100 emails sent; 1,575 completed forms received (response rate about 13%); 50 forms removed (contradictory or random responses; this dataset is limited to speakers of Polish as the first language).</p> <p>The classification of fields and disciplines follows Polish regulations in force at the time of the study (Regulation of the Polish Minister of Science and Higher Education of 20 September 2018 on Classification of fields and disciplines of science and disciplines of the arts; Journal of Laws 2018, item 1818). Compared to the OECD classification, the main points of difference involve the status of history and archaeology, linguistics and literary studies, and economics and management as separate disciplines.</p> <p>The data collection was not funded by any external source.</p>
SSH-V10-EUROPE-NEMO Part 2
<p>Sea surface y-axis velocity field (V) for a region around United Kingdom, Ireland and part of continental Europe. Used to simulate Sea Surface Height.</p> <p>Part II. </p>
SSH-V10-EUROPE-NEMO Part 1
<p>Sea surface y-axis velocity field (V) for a region around United Kingdom, Ireland and part of continental Europe. Used to simulate Sea Surface Height.</p> <p>Part I</p>
SSH-U10-EUROPE-NEMO Part 1
<p>Sea surface x-axis velocity field (U) for a region around United Kingdom, Ireland and part of continental Europe. Used to simulate Sea Surface Height.</p> <p>Part I</p>
SSH-U10-EUROPE-NEMO Part 2
<p>Sea surface x-axis velocity field (U) for a region around United Kingdom, Ireland and part of continental Europe. Used to simulate Sea Surface Height.</p> <p>Part II</p>
NeurOST-SSH-SST Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using L3 SSH + L4 SST.</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
Tools_Visual Analysis_SSH Open Marketplace
<p>A dataset of visual-analysis-tools based on the <a href="https://marketplace.sshopencloud.eu/">SSH Open Marketplace</a> created for the <a href="https://zfmedienwissenschaft.de/online/open-media-studies-blog">Open-Media-Studies-Blog</a>.</p>
NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1
The NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset produced by NASA provide observations of sea surface height, or sea level, anomaly measured using radar altimeter satellites in the reference mission orbit. These include TOPEX/Poseidon, the Jason series, and Sentinel-6. The data begin in Oct 1992, with data from TOPEX/Poseidon, and continues to the present. In this data set all missions have been referenced to a common baseline, additional quality control has been performed, and errors with wavelengths around one orbital cycle have been reduced. <br>The data consist of along-track observations of sea surface height, collected approximately once per second (1 Hz), and are parsed into files containing one day’s worth of data per file. A flag variable is included to allow users to easily select only valid observations, and a variable containing sea surface height with the flag applied and a small amount along track smoothing (~20 km), is suggested for most users. <br>Additionally, a “basin” flag variable is provided, along with a table defining it. This allows users to easily select all observations from a specific body of water. The basin flag assigns a number to each point corresponding to a specific ocean basin or lake. A table is included with a text description of each basin number. A text version of that table is available (https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_name_table.txt). The basin definitions can be downloaded as a shape file from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_polygon_files.tar.gz, or as a kml file https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/NASA-SSH_Basins.kmz. <br>New data will be released approximately once per week, with a latency of a few weeks.
NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1
The NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Only Version 1 dataset produced by NASA provides 2-D maps of sea surface height, or sea level, anomaly once every 7 days. The grids are based on observations of sea surface height from the radar altimeter satellites in the reference mission orbits, including TOPEX/Poseidon, the Jason series, and Sentinel-6. The data begin in Oct 1992 and continue through the present. They are created using the NASA-SSH Along-Track Sea Surface Height from Standardized Reference Missions Version 1 dataset. <br>The grids consist of 10-days worth of observations, which covers approximately 1 complete repeat cycle of observations from the reference missions. The grids are produced on a 0.5-degree latitude and longitude grid, by taking a simple gaussian weighted spatial average with a width of 100 km. The grids are produced every 7 days to allow for easy interpolation in time. However, since they are created using 10-days of data, there is some overlap of information between adjacent time steps. The grids are also created using the basin flags to avoid mixing data from distinct ocean basins (for example, to avoid mixing observations from the Caribbean Sea with observations from the Pacific across the Isthmus of Panama). Connected basins are allowed to share data, however. This is accomplished by using a table of connections between basins. The basin connection table is available (https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_connection_table.txt). The basin definitions can be downloaded as a shape file from https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/basin_polygon_files.tar.gz, or as a kml file https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/web-misc/nasa-ssh/NASA-SSH_Basins.kmz. <br>A new grid will be released approximately once per week, with a latency of a few weeks.
State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning
<p>This is a data repository in support of the publication "State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning" by Manucharyan et al. (2020), Journal of Advances in Modeling Earth Systems. The zipped file contains 10-day-separated snapshots of surface and deep ocean streamfunctions from the two-layer quasigeostrophic model of ocean turbulence. The included Python scripts demonstrate the efficacy of Deep Learning in temporal interpolation and state estimation given partial observations of the surface ocean turbulence.</p> <p> </p>
Detrended SSH data set used for Rossby Wave Analysis, extraction at 39N from ORCA12.L46-MJM189 DRAKKAR simulation
<p>This data set corresponds to the detrended Sea Surface Heigh (SSH) simulated by the NEMO ocean circulation model, under the ORCA12.L46-MJM189 configuration, developped in the frame of the DRAKKAR project. This particular data set is an interpolation from the native numerical grid, covering the latitude 39N in the North Altantic ocean, for the period 1970 to 2015. The data are concatenated in a single file with 5-days average of SSH. This subset was used in Watelet et al. (2020) submitted paper, dealing with Rossby waves analysis.</p>
Source and result datasets for ""Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS"
<p>These files are the LRZip [0] compressed datasets used in the research paper 'Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS' [1]. The code can be found on Github [2].</p> <p>ls 2021-12-22-10\:49\:40<br> total 21M<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:38 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 7.7M May 25 12:10 domainfile.log.new.gz<br> -rw-r--r-- 1 sneef sneef 17K May 24 20:53 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 13M May 24 20:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 125 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 27K May 24 20:52 server.log.new.gz</p> <p>ls 2021-12-22-15\:04\:21<br> total 11G<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:43 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 4.2G May 23 12:43 certstream.log.new.gz<br> -rw-r--r-- 1 sneef sneef 3.3M May 23 12:37 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 6.0G May 23 12:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 104 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 6.3M May 23 12:37 server.log.new.gz</p> <p><br> ls results_certstream<br> total 17G<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 19:26 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 22:54 certstream_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 22:39 certstream_analysis_skipped_domains.txt<br> -rw-r--r-- 1 sneef sneef 4.5G May 23 13:21 certstream_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 450 May 23 13:22 certstream_counted_unique_domains_otherlines.csv<br> -rw-r--r-- 1 sneef sneef 602M May 23 13:22 certstream_counted_unique_domains_skipped_domains.csv<br> -rw-r--r-- 1 sneef sneef 3.6G May 23 13:22 certstream_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 6.1K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 39K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt_errors.txt<br> -rw-r--r-- 1 sneef sneef 3.1M Jun 8 19:20 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 94 Jun 8 19:26 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 37K May 23 13:41 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 45M May 23 13:41 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 22M May 23 13:41 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 2.1K Jun 8 23:09 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 23:09 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 1.4K Jun 8 23:09 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 23:08 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 2.6K May 23 13:41 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 76M May 23 13:41 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 206M May 23 13:41 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 3.4G May 23 13:41 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 7.0M May 23 13:41 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 423K May 23 13:41 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 3.9G May 23 13:40 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 6.0K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 259 Jun 8 00:51 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 305K Jun 8 00:24 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 1.4M May 23 22:41 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 63K May 23 22:41 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 74 May 23 22:41 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 9.1K May 23 22:41 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 239 May 23 22:41 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.7M May 23 22:41 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 1.7K May 23 22:41 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 17K May 23 22:41 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 79 May 23 22:41 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 70M May 23 22:41 serverlog_structured_data_structued_data.json</p> <p> </p> <p>ls results_tranco1m<br> total 79M<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 17:03 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 850 Jun 8 22:38 domainfile_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 domainfile_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 19M May 25 12:28 domainfile_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 4.0K May 27 03:03 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 16K Jun 8 16:32 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 83 Jun 8 17:04 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 226 May 25 12:28 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 66K May 25 12:28 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 31K May 25 12:28 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 0 Jun 8 22:38 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 900 Jun 8 22:38 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 915 Jun 8 22:38 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 909 Jun 8 22:38 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 844 Jun 8 22:38 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 829 Jun 8 22:38 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 27 May 25 12:28 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 589K May 25 12:28 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 269K May 25 12:28 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 18M May 25 12:28 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 58K May 25 12:28 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.8K May 25 12:28 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 4.4K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 241 Jun 8 11:45 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 5.4K Jun 7 23:58 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 2.0K Jun 7 15:57 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 69 Jun 7 15:57 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.5K Jun 7 15:57 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 129K Jun 7 15:57 serverlog_structured_data_structued_data.json<code> </code></p> <p><br> <br> [0] https://github.com/ckolivas/lrzip<br> [1] TBD<br> [2] https://github.com/gehaxelt/sshfp-dns-measurement</p>
SSH-EUROPE-NEMO
<p>SSH (Sea Surface Height) simulations using the software NEMO for a region around United Kingdom, Ireland and part of continental Europe. </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.