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361 results for “January”
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 30 January 2018.
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 30 January 2018.</p>
Data for continuous and discrete measurements of carbonate parameters in a productive coastal region in the Northwestern Pacific (36°09'13.5''N, 129°24'04.9''E) from January to September in 2019 and from March to December in 2020
<p><span>Photosynthetic organisms shift the dynamics of surface pCO<sub>2</sub> driven by the sea surface temperature change (thermodynamic driver) by assimilating C from seawater. Here we measured net C uptake </span><span>in a macroalgal habitat</span> <span>of coastal Korea for two years (2019–2020) and found that the macroalgal habitat</span> <span>contributed </span><span>5.8 g</span><span> C m</span><sup><span>-</span><span>2</span></sup><span> month</span><sup><span>-</span><span>1</span></sup><span> of </span><span>the net C uptake during the growing period (the cooling period, September</span><span>-</span><span>May). This massive C uptake changed the thermodynamics-driven seasonal dynamics such that the air</span><span>-</span><span>sea equilibrium of </span><span>pCO<sub>2</sub></span><span> was pushed into disequilibrium. T</span><span>he </span><span>surface </span><span>pCO<sub>2</sub></span><span> dynamics during the cooling period were </span><span>mostly influenced by the seasonal decrease in temperature and the proliferation of macroalgae, while the dynamics </span><span>during the warming period </span><span>(the stagnant period, </span><span>June</span><span>-</span><span>August) </span><span>closely followed that predicted based solely on the change in sea surface temperature only </span><span>(thermodynamic driver)</span><span>.</span><span> In contrast to the phytoplankton-dominated offshore waters (where phytoplankton populations are large in spring and summer), the impact of coastal water macroalgae on surface </span><span>pCO<sub>2</sub></span><span> dynamics was most pronounced during the cooling period, when the magnitude of </span><span>pCO<sub>2</sub></span><span> change was as much as twice that resulting from temperature change. Our study shows that</span><span> t</span><span>he distinctive features of the macroalgal habitat—in particular the seasonal temperature extremes (~18°C difference), the </span><span>active macroalgal metabolism,</span><span> and anthropogenic </span><span>nutrient</span><span> inputs—collectively influenced</span><span> the seasonal decoupling of seawater and air </span><span>pCO<sub>2</sub></span><span> dynamics</span><span>.</span></p>
Geographic Zone Report for AR.docx: a summary of REEF surveys for Alligator Reef from 1 January 1993 to 7 February 2016.
<p>Geographic Zone Report for AR.docx: a summary of REEF surveys for Alligator Reef from 1 January 1993 to 7 February 2016. from </p> <p>Starck, W.A., Estapé C.J. & Morgan Estapé, A. (2017) The fishes of Alligator Reef and environs in the Florida Keys: a half-century update. Journal of the Ocean Science Foundation, 27, 74–117.<br> </p>
Huntingtin intrabody and nanobody literature review 10th January 2018
<p>Huntingtin structure-function open lab notebook project. </p> <p>To kick off the new year, I have been doing some reading on huntingtin intrabodies and nanobodies and have written this up into an informal literature review. These are small Ig domain proteins which specifically bind different regions of huntingtin. All of those which I have written about, bind the exon 1 of huntingtin, some with specificity for the N17 region, whereas others bind the polyproline or proline rich domains.</p> <p>Thanks to Prof Ray Truant, who kindly provided me with some nanobody clones, I have already expressed and purified iVHH4. I plan to clone, express and purify Happ1 and VL12.3, both of which are single Ig domain proteins, and along with iVHH4, test binding to my huntingtin protein samples and see if they help stabilize my samples.</p> <p>http://labscribbles.com/2018/01/10/huntingtin-intrabody-and-nanobody-literature-review/ </p>
HMGB1 and iVHH4 interaction analyses with huntingtin by pull-down - 15th January 2018
<p>Huntingtin structure function open lab notebook project. </p> <p>Funding Acknowledgment: The SGC is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada through Ontario Genomics Institute [OGI-055], Innovative Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766], Janssen, Merck KGaA, Darmstadt, Germany, MSD, Novartis Pharma AG, Ontario Ministry of Research, Innovation and Science (MRIS), Pfizer, São Paulo Research Foundation-FAPESP, Takeda, and Wellcome.</p> <p> </p>
Occurrence of equatorial plasma bubbles (EPBs) over the Indian region on 15 January 2022 and their plausible connection to the Tonga volcano eruption
<p><span>This study focuses on the causes for the generation of equatorial plasma bubbles (EPBs) over the Indian subcontinent and their correlation with atmospheric-ionospheric disturbances resulting from the eruption of the Tonga volcano on 15 January 2022. Concurrent ionosonde observations obtained from Tirunelveli (8.67<sup>o</sup>N, 77.81<sup>o</sup>E) and Prayagraj (25.41<sup>o</sup>N, 81.93<sup>o</sup>E) show the presence of spread-F traces in ionograms. Notably, the EPBs are also accompanied by plasma blobs (PBs), with their pronounced occurrence during midnight at Prayagraj and Tirunelveli. Analysis of in situ electron density observations obtained from the Swarm B and C satellites reveals substantial plasma density depletions associated with EPBs. An intriguing observation is the intensification of Pre-Reversal Enhancement (PRE) immediately preceding the onset of spread-F at Tirunelveli due to enhanced eastward F region zonal winds by Tonga Volcano, as seen in the satellite observations. Furthermore, the isofrequency analysis from Tirunelveli shows the presence of gravity wave-like oscillations in the equatorial F-region over India. The investigation of Total Electron Content (TEC) obtained from a Pseudo Random Number (PRN)-14 over Indian longitudes suggests the presence of two dominant modes of Traveling Ionospheric Disturbances (TIDs) with speeds ~452 m/s and ~406 m/s having periods in the range of ~65–75 minutes. These observations reaffirm that volcano triggered atmospheric/ionospheric disturbances can propagate long</span><span> distances for several hours</span><span> and can provide necessary seeding conditions for the generation of EPBs.</span></p>
Raw temperature measurements from SmartSantander sensors reported between January 1st 2021 and July 31st 2022
<p>This is a smart city domain dataset, and more specifically a environmental one generated within the framework of the SmartSantander research testbed.</p> <p>It contains raw temperature measurements reported by SmartSantander sensors deployed in the spanish city of Santander, covering a period of 17 months between January 1st 2021 and July 31st 2022, and comprising more than 24 million data points. The dataset includes not only the temperature dimension but also spatial and temporal information, as well as the specific device identifier and some labels to differentiate between static/mobile and indoor/outdoor devices.</p> <p>As is common with large-scale sensor deployments, there are occasional sensor malfunctions, which have deliberately not been filtered out of this raw dataset.</p>
Month-to-month proportion of LSD-vaccinated animals at regional level (NUTS3) in south-eastern Europe since January 2016 until November 2017 and reported LSD outbreaks
<p>The European Food Safety Authority (EFSA), under request of the European Commission, performed an epidemiological analysis of the lumpy skin disease (LSD) epidemics based on the data collected from the affected and at-risk Member States and non-EU countries in south-eastern Europe. The video at the link below shows the month-to-month proportion of LSD-vaccinated animals at regional level (NUTS3) since January 2016 until November 2017 and reported LSD outbreaks per month (red dots are the new outbreaks each month, grey dots are past outbreaks).</p> <p> </p> <p>*This designation is without prejudice to positions on status and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo Declaration of Independence.</p>
TESS-JANUARY-2019
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). January 2019</p>
"Minha Origem" Percussion break at Ilê Aiyê's Beleza Negra, January 28, 2023. Senzala do Barro Preto. Salvador, Brazil.
<p>The short clip shows the percussion break that took place during "Minha Origem" as performed by Ilê Aiyê's annual Beleza Negra on January 28, 2023. Video by author, Cody Case, with permission from Ilê Aiyê who possesses all videos. This fieldwork footage was funded by a Fulbright-Hays DDRA fellowship and received IRB and Brazil Ethics committee approval to record videos of public performances, including bloco authorization provided by founder and president Antônio Carlos dos Santos for research purposes. </p>
Shift in Circulating Human Adenovirus Species Leading to Nationwide Outbreak - China, January 2023-August 2024
<p><span>The list of pathogens detected by the tNGS used in this study</span></p>
Comparison of Meteorological Data: Chios and Kardamyla, January – March 2024
<p>A comparison of the meteorological data from two nearby locations on Chios Island, <strong>Chios </strong>(town) and <strong>Kardamyla</strong>, for the months of January, February, and March 2024, reveals some interesting climatological patterns. The data includes temperature, rainfall, wind speed, and wind direction, providing a valuable overview of the weather conditions in these Aegean locations.</p> <h3>Temperature</h3> <p>The average temperature shows a steady increase in both areas over the three months:</p> <ul> <li><strong>Kardamyla</strong>: <ul> <li>January: 12.4°C</li> <li>February: 13.4°C</li> <li>March: 13.9°C</li> </ul> </li> <li><strong>Chios town</strong>: <ul> <li>January: 11.9°C</li> <li>February: 13.0°C</li> <li>March: 14.1°C</li> </ul> </li> </ul> <p>The temperatures in both areas are quite similar, with Chiostown recording slightly higher average temperatures in February and March compared to Kardamyla. This might be due to differences in local topography and exposure to wind patterns.</p> <h3>Rainfall</h3> <p>Rainfall varies across the three months, with significant precipitation recorded in both locations:</p> <ul> <li><strong>Kardamyla</strong>: <ul> <li>January: Total rainfall 87.6 mm (maximum daily: 19.2 mm)</li> <li>February: Total rainfall 70.4 mm (maximum daily: 49.4 mm on February 12)</li> <li>March: Total rainfall 63.2 mm (maximum daily: 28.2 mm on March 5)</li> </ul> </li> <li><strong>Chios town</strong>: <ul> <li>January: 54.8 mm</li> <li>February: 82.8 mm (maximum daily: 43.6 mm on February 12)</li> <li>March: 69.6 mm (maximum daily: 25.4 mm on March 5)</li> </ul> </li> </ul> <p>Both locations experienced heavy rainfall in February, with Chiostown recording a maximum daily rainfall of 43.6 mm on the 12th, close to the 49.4 mm recorded in Kardamyla on the same day. This indicates a shared weather event, likely an intense storm system affecting both areas.</p> <h3>Wind</h3> <p>Wind speeds in both locations show similar patterns of strong winds during winter and early spring, with some days registering high gusts:</p> <ul> <li><strong>Kardamyla</strong>: <ul> <li>January: Maximum wind speed 85.3 km/h</li> <li>February: Maximum wind speed 82.1 km/h</li> <li>March: Maximum wind speed 77.2 km/h</li> </ul> </li> <li><strong>Chios town</strong>: <ul> <li>January: Maximum wind speed 90.1 km/h</li> <li>February: Maximum wind speed 67.6 km/h</li> <li>March: Maximum wind speed 66.0 km/h</li> </ul> </li> </ul> <p>Chiostown recorded the highest wind speeds in January (90.1 km/h), while the wind intensity decreased slightly in February and March. In contrast, Kardamyla experienced consistently high wind speeds throughout the three months. Northerly and north-northeasterly winds dominate in both areas, with southern winds occurring during stormy conditions.</p> <h3>Conclusions</h3> <p>Several key conclusions can be drawn from this comparison:</p> <ol> <li><strong>Temperature</strong>: Temperatures rise gradually as spring approaches, with Chiostown showing slightly higher average temperatures than Kardamyla during February and March.</li> <li><strong>Rainfall</strong>: Both locations experienced significant rainfall, particularly in February. The high daily rainfall values on February 12 suggest a shared intense weather event affecting both locations.</li> <li><strong>Wind</strong>: Strong winds are a consistent feature in both areas, with Chiostown experiencing slightly higher maximum wind speeds in January but Kardamyla maintaining stronger winds throughout the three-month period.</li> </ol> <p>Overall, the weather conditions in Chios (town) and Kardamyla are quite similar, with local topographical and geographical factors influencing small variations in temperature, rainfall, and wind speeds.</p>
Acoustic recording: Pihaena (Moorea Island) January 2021
<p>Dataset</p> <p>"Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring"</p> <p>Status: Marine Protected Area, coast: North, site: Pihaena, island: Moorea Island, depth: 10 m, position: external slope of the barrier reef, latitude: -17.4765, longitude: -149.8291, year: 2021, month: January, format: wav. The files are named DDHHMMSS (DD = day, HH = hour, MM = minutes, SS = seconds). </p> <p>Sampling frequency: 44.1 kHz, type of recorder: SNAP, type of hydrophone: HTI-96, sensitivity: between -170.1 and -169.6 dB re 1 V µPa^-1, gain: between 2 and 2.05 dB, acquisition: continuously.</p> <p>This research is funded by two grants attributed to Xavier Raick. The first one is from the King Leopold III Fund for Nature Exploration and Conservation and the second one is from the University of Liège “financement mission scientifique”. This research is part of the project “Acoustics to Assess the Health Status of Reefs” hosted by the Ocean Decade Research Programme on the Maritime Acoustic Environment (OD-MAE) endorsed by the 2021-2030 United Nations Decade of Ocean Science for Sustainable Development (UNESCO).</p> <p>More information can be found in the main text and in the Supplementary Materials of the related article by Raick et al. ""Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring".</p>
Acoustic recording: Nuarei (Moorea Island) January 2021
<p>Dataset</p> <p>"Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring"</p> <p>Status: Marine Protected Area, coast: East, site: Nuarei, island: Moorea Island, depth: 10 m, position: external slope of the barrier reef, latitude: -17.5006, longitude: -149.7546, year: 2021, month: January, format: wav. The files are named DDHHMMSS (DD = day, HH = hour, MM = minutes, SS = seconds). </p> <p>Sampling frequency: 44.1 kHz, type of recorder: SNAP, type of hydrophone: HTI-96, sensitivity: between -170.1 and -169.6 dB re 1 V µPa^-1, gain: between 2 and 2.05 dB, acquisition: continuously.</p> <p>This research is funded by two grants attributed to Xavier Raick. The first one is from the King Leopold III Fund for Nature Exploration and Conservation and the second one is from the University of Liège “financement mission scientifique”. This research is part of the project “Acoustics to Assess the Health Status of Reefs” hosted by the Ocean Decade Research Programme on the Maritime Acoustic Environment (OD-MAE) endorsed by the 2021-2030 United Nations Decade of Ocean Science for Sustainable Development (UNESCO).</p> <p>More information can be found in the main text and in the Supplementary Materials of the related article by Raick et al. ""Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring".</p>
Acoustic recording: Temae (Moorea Island) January 2021
<p>Dataset</p> <p>"Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring"</p> <p>Status: non Marine Protected Area, coast: East, site: Temae, island: Moorea Island, depth: 10 m, position: external slope of the barrier reef, latitude: -17.5067, longitude: -149.7600, year: 2021, month: January, format: wav. The files are named DDHHMMSS (DD = day, HH = hour, MM = minutes, SS = seconds). </p> <p>Sampling frequency: 44.1 kHz, type of recorder: SNAP, type of hydrophone: HTI-96, sensitivity: between -170.1 and -169.6 dB re 1 V µPa^-1, gain: between 2 and 2.05 dB, acquisition: continuously.</p> <p><span>This research is funded by two grants attributed to Xavier Raick. The first one is from the King Leopold III Fund for Nature Exploration and Conservation and the second one is from the University of Liège “financement mission scientifique”. </span><span>This research is part of the project “Acoustics to Assess the Health Status of Reefs” hosted by the Ocean Decade Research Programme on the Maritime Acoustic Environment (OD-MAE) endorsed by the 2021-2030 United Nations Decade of Ocean Science for Sustainable Development (UNESCO).</span></p> <p><span>More information can be found in the main text and in the Supplementary Materials of the related article by Raick et al. ""Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring".</span></p>
Acoustic recording: E2B (Moorea Island) January 2021
<p>Dataset</p> <p>"Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring"</p> <p>Status: non Marine Protected Area, coast: North, site: Entre deux baies (E2B), island: Moorea Island, depth: 10 m, position: external slope of the barrier reef, latitude: -17.4751, longitude: -149.8372 , year: 2021, month: January, format: wav. The files are named DDHHMMSS (DD = day, HH = hour, MM = minutes, SS = seconds). </p> <p>Sampling frequency: 44.1 kHz, type of recorder: SNAP, type of hydrophone: HTI-96, sensitivity: between -170.1 and -169.6 dB re 1 V µPa^-1, gain: between 2 and 2.05 dB, acquisition: continuously.</p> <p>This research is funded by two grants attributed to Xavier Raick. The first one is from the King Leopold III Fund for Nature Exploration and Conservation and the second one is from the University of Liège “financement mission scientifique”. This research is part of the project “Acoustics to Assess the Health Status of Reefs” hosted by the Ocean Decade Research Programme on the Maritime Acoustic Environment (OD-MAE) endorsed by the 2021-2030 United Nations Decade of Ocean Science for Sustainable Development (UNESCO).</p> <p>More information can be found in the main text and in the Supplementary Materials of the related article by Raick et al. ""Highlighting the resilience potential of marine protected areas in the face of coral bleaching with passive acoustic monitoring".</p>
LBHL (~170 nm) UVI images of Polar satellite from December 1996 to January 1997
<p>The UVI aboard the Polar satellite can obtain the ultraviolet auroral intensity distribution in the northern hemisphere. In order to reduce the influence of dayglow on UVI image data, we only used LBHL (~170 nm) UVI images from December 1996 to January 1997, which have full aurora oval (the aurora oval region in the northern hemisphere during this period is in the polar night region, and the aurora image is weakly affected by dayglow and solar light). Each image has a pixel size of 200×228 and a spatial resolution of about 0.04° per pixel (UVI auroral images of Polar satellite taken at apogee have a pixel size of 40×40 km at 100 km above the ground), and the value of each pixel is converted to the value with auroral intensity, the unit is photons/cm<sup>2</sup>/s.</p> <p>These UVI data is save as the mat file of Matlab.</p>
GPRChinaTemp1km: 1 km monthly maximum air temperature for China from January 1951 to December 2020
<p>GPRChinaTemp1km is a new high-resolution (1-km) monthly gridded air temperature dataset for China from January 1951 to December 2020. The dataset includes monthly maximum air temperature covering the main land area of China during 1951-2020, which was interpolated by the Gaussian process regression (GPR) method based on the meteorological station data. The monthly gridded temperature dataset was evaluated by the observed values of the meteorological stations from the China Meteorological Data Service Centre. The dataset is in GeoTIFF format in the WGS84 (EPSG:4326) coordinate system. The unit of the data is degree Celsius (°C).</p>
GPRChinaTemp1km: 1 km monthly mean air temperature for China from January 1951 to December 2020
<p>GPRChinaTemp1km is a new high-resolution (1-km) monthly gridded air temperature dataset for China from January 1951 to December 2020. The dataset includes monthly mean air temperature covering the main land area of China during 1951-2020, which was interpolated by the Gaussian process regression (GPR) method based on the meteorological station data. The monthly gridded temperature dataset was evaluated by the observed values of the meteorological stations from the China Meteorological Data Service Centre. The dataset is in GeoTIFF format in the WGS84 (EPSG:4326) coordinate system. The unit of the data is degree Celsius (°C).</p>
GPRChinaTemp1km: 1 km monthly minimum air temperature for China from January 1951 to December 2020
<p>GPRChinaTemp1km is a new high-resolution (1-km) monthly gridded air temperature dataset for China from January 1951 to December 2020. The dataset includes monthly minimum air temperature covering the main land area of China during 1951-2020, which was interpolated by the Gaussian process regression (GPR) method based on the meteorological station data. The monthly gridded temperature dataset was evaluated by the observed values of the meteorological stations from the China Meteorological Data Service Centre. The dataset is in GeoTIFF format in the WGS84 (EPSG:4326) coordinate system. The unit of the data is degree Celsius (°C).</p> <p> </p>
ScienceDex guides
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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)
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.