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361 results for “January”
Outcome of the 3 Impact Workshops held by the ReMO COST Action between 21st and 28th January 2021
<p>These documents are the result of a series of three online workshops held by the Researcher Mental Health (ReMO) COST Action that focussed on the specific impact priorities of each of ReMO's three Working Groups:</p> <ul> <li> <p>Working Group 1: System level - the ERA working environment</p> </li> <li> <p>Working Group 2: Institutional level - Wellbeing Practices in Research Institutions</p> </li> <li> <p>Working Group 3: Local actors - Promoting researcher well-being on a practical level </p> </li> </ul> <p>These three-hours long impact workshops were based on the outcomes of a previous process to perform <a href="https://zenodo.org/record/5643588">SWOT analyses</a> and followed the guidelines and methodology that the ReMO community adapted from the "Impact+ Exercise” that was originally developed for European Commissions’ Erasmus plus projects (<a href="https://www.erasmusplus.org.uk/impact-and-evaluation">https://www.erasmusplus.org.uk/impact-and-evaluation</a>). In these workshops the following procedure was executed:</p> <ul> <li> <p>First, participants established their impact targets for the ReMO network: the <strong>network </strong>(itself), the <strong>researcher</strong>, <strong>stakeholders</strong>, and the research <strong>system</strong>. These targets were derived from the methodological guidelines of the “Impact+ Exercise”, but participants tailored them to the context of ReMO. </p> </li> <li> <p>Participants, randomly assigned to small discussion rounds for brainstorming, identified the most important issues and topics when it comes to researcher mental health and well-being. They also associated those issues and topics with the impact targets.</p> </li> <li> <p>Outcomes were discussed in a plenary session with duplications and similar items (issues and topics) being merged.</p> </li> <li> <p>Participants went through a voting procedure. Every participant could distribute three votes across all topics and issues identified by all participants. Votes were counted and topics were ranked based on the number of votes they received.</p> </li> <li> <p>Participants discussed the ranking and generated an action plan for the ReMO Action, which also served as a guide for research and intervention development to improve researcher mental health.</p> </li> <li> <p>As a result, the three Impact matrices for the three working groups were finalized and synthesized to a project level Impact matrix. </p> </li> </ul> <p>The process aimed to distill a number of key themes and actions, which are critical for developing a model of doctoral education and more general research culture embracing researcher mental health as a key priority. This process led to the publication of the <a href="https://zenodo.org/record/5559806">Researcher Mental Health and Well-being Manifesto</a>.</p> <p>The audience’s responses were anonymous, and they were recorded in a padlet, whose contents are available here.</p>
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2, filename: TNNXXX_YYYYMMDD) and Landsat-8 (L8, filename: LC08_L1GT_NNNNNN_YYYYMMDD_YYYYMMDD_01_T2) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included in supplementary datasets. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p>
January 2022 OCFL Meeting
<p>This month's community meeting included:</p> <ul> <li>Community updates.</li> <li>A demo of IvenioRDM</li> </ul> <p>Full notes can be found at <a href="https://github.com/OCFL/spec/wiki/2022.01.12-Community-Meeting">https://github.com/OCFL/spec/wiki/2022.01.12-Community-Meeting</a></p> <p>The next community meeting: Wednesday February 9th 11pm GMT / 6pm EST / 3pm PST | Thursday February 10th 10am AEDT</p>
LPL1 - January 2021
<p>Measurements of LPL1 (Güímar) in January 2021</p> <p>Tenerife</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
LPL5 - January 2021
<p>Measurements of LPL5 (Tejeda) in January 2021</p> <p>Tejeda, Gran Canaria</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009
<p>Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI.</p> <p>During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.</p>
LPL5 - January 2022
<p>Measurements of LPL5 (Tejeda) in January 2022</p><p>Gran Canaria</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL5">info</a></p>
LPL1 - January 2022
<p>Measurements of LPL1 (Teide NP) in January 2022</p><p>Tenerife</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL1">info</a></p>
SG - January 2022
<p>Measurements of SG (Global) in January 2022</p><p></p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/SG">info</a></p>
LPL4 - January 2022
<p>Measurements of LPL4 (Ilha do Corvo) in January 2022</p><p>Corvo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL4">info</a></p>
LPL3 - January 2022
<p>Measurements of LPL3 (Madeira East Coast) in January 2022</p><p>Madeira</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL3">info</a></p>
LPL8 - January 2022
<p>Measurements of LPL8 (Timanfaya NP) in January 2022</p><p>Lanzarote</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL8">info</a></p>
Brazilian Butterflies Collected December 2020 to January 2021
<p>Wing Photographs Acraeini, Heliconiini, Danaini, and Ithomiini. Butterflies Collected in the Brazilian Atlantic Forest between December 2020 to January 2021</p> <p>Author List: Érika C Pinheiro de Castro, Karina Lucas da Silva-Brandão, André Victor Lucci Freitas, Marcio Zikan Cardoso, Eva van der Heijden, Joana Meier, Ian Warren, and Chris Jiggins</p> <p> </p> <p>EN:</p> <p>This upload contains photographs taken by Eva Van der Heijden, in the Insect Evolution and Genomics Group at the University of Cambridge. This batch contains wild caught specimens collected in the Brazilian Atlantic Forest from December 2020 to January 2021 Butterflies from the following tribes can be found here: Acraeini, Heliconiini, Danaini, Ithomiini. Information on the individual IDs can additionally be found in the csv.</p> <p>Nomenclature</p> <p>CAMXXXXXX : unit ID corresponding to individual samples _v _d: ventral or dorsal</p> <p>Both JPGs and raw image files (CR2) have been uploaded.</p> <p>Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click <a href="http://heliconius.ecdb.io">here</a> for the database, and here for <a href="https://heliconius.zoo.cam.ac.uk/databases/">FAQ</a>)</p> <p>Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) and/or Erika Pinheiro de Castro (ecp38[at]cam.ac.uk)</p> <p> </p> <p>------------------------------------------------------</p> <p>PT:</p> <p> </p> <p>Este banco de dados contém fotos tiradas por Eva van der Heijden do grupo de pesquisa Insect Evolution and Genomics da Universidade de Cambridge.</p> <p>As fotos correspondem à asas de borboletas coletadas na Mata Atlântica brasileira entre dezembro de 2020 e janeiro de 2021.</p> <p> </p> <p>As fotos estão organizadas em pastas nomeadas de acordo com as tribos as quais as mesmas fazem parte: Acraeini, Heliconiini, Danaini e Ithomiini. As informações sobre a identificação da espécie de cada amostra estão no arquivo .csv</p> <p>Nomenclatura</p> <p>CAMXXXXXX : número de queda/ID de cada indivíduo _v _d: ventral ou dorsal</p> <p>Este banco de dados contém imagens no formato JPG e CR2. Mais informações sobre cada amostra (data de coleta, local de coleta, coordenadas geográficas, etc.) podem ser encontradas banco de dados de domínio público Earthcape (clique <a href="http://heliconius.ecdb.io/">aqui</a> para acessar o domínio,e <a href="https://heliconius.zoo.cam.ac.uk/databases/">aqui</a> para FAQ). Para mais informações, favor entre em contato com Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) e/ou Erika Pinheiro de Castro (ecp38[at]cam.ac.uk).</p>
Mesoscale stereo retrievals from Hunga Tonga-Hunga Ha'apai Eruption of 15 January 2022
<p>Stereo methods using GOES-17 and Himawari-8 applied to the Hunga Tonga-Hunga Ha'apai volcanic plume on 15 January 2022 show overshooting tops reaching 50-55 km altitude, a record in the satellite era. Plume height is important to understand dispersal and transport in the stratosphere and climate impacts. Stereo methods, using geostationary satellite pairs, offer the ability to accurately capture the evolution of plume top morphology quasi-continuously over long periods. Manual photogrammetry estimates plume height during the most dynamic early phase of the eruption and a fully automated algorithm retrieves both plume height and advection every 10 minutes during a more frequently sampled and stable phase beginning three hours after the eruption. Stereo heights are confirmed with Global Navigation Satellite System Radio Occultation (GNSS-RO) bending angles, showing that much of the plume was lofted 30–40 km into the atmosphere. Cold bubbles are observed in the stratosphere with brightness temperature of ~173K.</p>
Spectral characteristics of ionospheric disturbances over the Southwestern Pacific from the January 15, 2022 Tonga eruption and tsunami
<p>This dataset contains the filtered ionospheric total electron content values used in the manuscript of the same name.</p>
GPR-BMP-SPI: High spatial resolution multi-scale SPI datasets over China from January 1984 to December 2020
<p>The datasets include standard precipitation index (SPI) at 1-month, 3-month, 6-month, 9-month and 12-month scales over the main terrestrial lands of China from January 1984 to December 2020. The SPI datasets were produced by blending the information from meteorological stations, and precipitation products, as well as topographical and geographical variables based on Gaussian process regression (GPR) models.</p> <p>The meteorological station data are from the China Meteorological Data Service Centre. Five precipitation products are used: (1) CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final); (3) ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis; (3) FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; (4) PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record; (5) TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces.</p> <p>The maps of the difference of the confidence intervals (the upper prediction limit minus the lower prediction limit) at a significance level of 95% are also provided to show the spatial uncertainty of every single SPI map.</p> <p>The drought events were counted during 1984-2020 at annual and seasonal scales. The variables related to the drought events are presented in “Drought_Event.zip”.</p> <p>Reference: He, Q., Wang, M., Liu, K., Li, B., & Jiang, Z. (2023). Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. <em>Weather and Climate Extremes</em>, <em>40</em>, 100567.</p> <p> </p>
SPEAR_SST_data(January-July_initialization)
<p>Data for supporting the finding in the manuscript "Skillful forecasts of springtime CONUS tornado activity up to a year in advance" by Tseng et al. (2022)</p>
lcpz/lfb-datasets: January 2009 to June 2022
<p>Datasets for generating <a href="https://doi.org/10.1007/s42979-021-00523-w">CFSTP</a> and <a href="https://arxiv.org/abs/2105.00451">MARSC</a> instances.</p>
Data and scripts from Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020
<p>Raw data and scripts used in the publication "<em>Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020 separator commenting unavailable</em>" in Eurosurveillance in 2022.</p> <p> </p> <p>https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.6.2100406</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 14 January 2016
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 14 January 2016</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.