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361 results for “January”
Cichlasoma urophthalmus cytochrome b sequences collected from the Florida Everglades (FCE) and Central America from January 2012 to May 2014
Cytochrome b sequences obtained from samples of Cichlasoma urophthalmus collected within the Florida Everglades and Central America. Samples were collected from fin clippings of fish caught by angling and/or cast netting. DNA was extracted, amplified and cytochrome b was sequenced. Data were used to determine the source for Mayan Cichlids in Florida.
Monthly water balance data for southern Taylor Slough Watershed (FCE LTER) from January 2001 to December 2011
The following abstract is from Sandoval (2013). The purpose of this research was to investigate the water balance, flushing time, and water chemistry of Taylor Slough; one of the main natural waterways of the coastal Everglades, during its early stages of restoration. Watershed flushing times were estimated as the surface water volume divided by the total water outputs. Both the water balance and water residence times were calculated on monthly from 2001 – 2011. Flushing times varied between 3 and 78 days, with the highest values occurring in December and the lowest in May. Flushing times were negatively correlated with evapotranspiration (ET), but were longer when surface water volume exceeded ET and shorter when ET exceeded water volume.
FCE LTER Taylor Slough/Panhandle-7 Site Scrub Red Mangrove (Rhizophora mangle) Leaf Gas Exchange Data, Florida, USA from January-December 2019
Rates of leaf gas exchange were measured monthly during the 2019 calendar year in a scrub Red mangrove (Rhizophora mangle (L.) L.) forest site (TS/Ph-7) near the mouth of Taylor River in southeastern Florida Everglades. Sampling of green mature leaves was designed to target scrub mangrove tree branches growing on slightly higher elevation mangrove island centers versus permanently inundated island edge habitats. Concurrent measurements of water depth and surface and porewater salinity were collected at each of the mangrove island habitats, with the research objective of assessing the effect of physicochemical variables on rates of leaf gas exchange (i.e., assimilation and stomatal conductance). Leaf gas exchange data were collected using the Li-6800 portable photosynthesis system (Li-COR, Lincoln, NE). Additional data on leaf functional traits and nutrient concentrations and environmental data from the site are included. Data are presented in five datasets (.csv).
Mangrove Leaf Litter Carbon and Nutrients from the Shark River Slough, Everglades National Park (FCE), South Florida, USA, January 2019 - ongoing
Mangrove litterfall dynamics have been monitored in all Shark River sites (SRS-4, SRS-5, SRS-6) since January 2001 using the same collection method stated in Castañeda-Moya et al. 2013 (metadata: knb-lter-fce.1195) and Danielson et al. 2017. Briefly, litterfall was collected monthly at all sites (10 baskets per site) using permanent 0.25 m2 wooden baskets supported approximately 1.3 m above the soil surface and lined with 1 mm mesh screening. Litterfall from each basket was sorted, dried, and weighed by leaf species, reproductive parts by species, and woody material. Leaf litter data from different years (2019, 2020, 2021, 2023) were selected for each site to identify species-specific foliar carbon and nutrient (N and P) content. Monthly leaf litter samples were analyzed separately by species for all years after grinding with a Wiley Mill to pass through a 40-µm mesh screen. Total leaf litter C and N contents were determined with a Carlo-Erba NA-1500 elemental analyzer (Fisons Instruments Inc., Danvers, MA, USA). Total leaf litter P was extracted using an acid-digest (HCl) extraction, and concentrations of SRP were determined by spectrophotometric analysis (Methods 365.4 and 365.2, USA EPA 1983). Litterfall data collection is ongoing every year since 2001, while C and nutrients analyses are performed every other year after 2021. See also Shark River mangrove litterfall data (knb-lter-fce.1195) on the FCE LTER website's data catalog or in the EDI repository (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1195). References: Castañeda-Moya, E., Twilley, R. R., & Rivera-Monroy, V. H. (2013). Allocation of biomass and net primary productivity of mangrove forests along environmental gradients in the Florida Coastal Everglades, USA. Forest Ecology and Management, 307, 226-241. Danielson, T.M., V.H. Rivera-Monroy, E. Castaneda-Moya, H. Briceno, R. Travieso, B.D. Marx, E. Gaiser, and L.M. Farfan. 2017. Assessment of Everglades mangrove forest re
Benthic chlorophyll concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from January, April, June and July 2001
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll and gross oxygenic photosynthesis were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll were measured in the bulk surface centimeter depth of sediment. Gross oxygenic photosynthesis rates were integrated over 100 um resolution measurements below the sediment water interface using oxygen microelectrodes. Several relatively pristine sites on Sapelo Island (Moses Hammock, Dean Creek and Marine Institute) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration and photosynthesis rate across bank and marsh zones with higher photosynthetic biomass and activity in the spring season. Nutrient inputs to these study sites are also suggested as a control on gross oxygenic photosynthesis rates as evidenced by a relationship between photosynthesis rate and land-use.
Benthic chlorophyll, density, porosity, and organic content concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from January, April, June and July 2001
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll, density, porosity, and organic content were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll, density, porosity, and organic content were measured in the bulk surface centimeter depth of sediment. Several relatively pristine sites on Sapelo Island (Moses Hammock and Dean Creek) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration across bank and marsh zones with higher photosynthetic biomass in the spring season.
GCE-LTER Hammock Well Vegetation and Invertebrate Monitoring - January 2012
In July 2008 the GCE LTER program selected two hammocks for intensive study: HN_i_1 and PC_i_29. HN_i_1 is of Holocene origin and is located adjacent to Blackbeard Island to the north of Sapelo Island, Georgia. PC_i_29 is also of Holocene origin and is located adjacent to the south end of Sapelo Island. These hammocks are of similar size, with similar vegetation zones in the high marsh environment. Groundwater well transects were established that run from the upland, through the marsh, and up and over the upland area of each hammock. Two permanent plots were established at each well in order to characterize the plant and invertebrate diversity and abundance by surveying plots once a year. This data set includes the results of January 2012 monitoring studies.
Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing
This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
Maximum temperature at El Verde Field Station, Rio Grande, Puerto Rico from January 1975 to August 1992
Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). Average record show that maximum values for maximum temperature recorded from May to October with a range from 29 to 30 and peaks of 29.7 Centigrade in October. The months of October through December show the most dramatic increase, specially December. Highest average maximum temperatures during these years were recorded in 1998 and 1999. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Lake Fryxell under-ice conductivity, temperature, and depth (CTD) profiles, McMurdo Dry Valleys, Antarctica, January 2025
To characterize the upper ~2 m of the water column beneath the ~3 m ice cover in Lake Fryxell, located in the McMurdo Dry Valleys of Antarctica, 27 CTD (conductivity, temperature, depth) profiles were collected at ~5 m horizontal spacing along a transect spanning the dive hole to the shoreline. Measurements were made using a YSI Castaway CTD mounted on a 1 m mast atop a remotely operated vehicle (ROV), profiling upward from ~2 m below the ice-water interface. Using an ROV rather than drilling a hole through the ice was intended to minimize artifacts associated with ice openings. Sampling late in the austral summer targeted conditions during maximum seasonal ice melt.
Test results and analysis of whitelisted URLs in Jammu and Kashmir, January 2020
<p><strong>Version 3: </strong>In this version, the tab entitled "New entries Jan 31 order" was added to version 2 of the spreadsheet. This tab contains new entries from the whitelist accompanying the order dated 31 January 2020 [Order number: Home-08 (TSTS) of 2020]. A separate tab was required because the "field" category present in previous versions of the whitelist was removed in the 31 January order. </p> <p><strong>Version 2:</strong> This dataset contains an analysis of a whitelist comprising 301 entries issued by the Home Department, Government of Jammu and Kashmir on 24 January 2020 [<a href="http://jkhome.nic.in/Home-05(tsts)%20of%202020_0001.pdf">Order number: Home-05 (TSTS) of 2020</a>]. The department issued an order with the first version of this whitelist in response to a Supreme Court judgement dated 10 January 2020 (<em><a href="https://indiankanoon.org/doc/82461587/">Anuradha Bhasin vs. Union of Indian and Ors</a>.</em>) that directed the Government of India to review the blanket suspension of Internet services in Jammu and Kashmir since 5 August 2019.</p> <p><strong>Version 1:</strong> The first version of the whitelist (dated <a href="https://www.scribd.com/document/443380803/Temporary-Suspension-of-Telecom-Services#download&amp;from_embed">18 January 2020</a>), and this dataset by extension, comprised 153 entries. The Home Department states in its orders that this whitelist will be continually updated; the next update may be issued on 31 January or earlier.</p> <p>This preliminary analysis was conducted by Rohini Lakshané and Prateek Waghre from 22 and 26 January 2020 IST, to empirically determine whether the whitelisted websites and services would be practically usable for an ordinary resident of Jammu and Kashmir at the time of writing.</p> <p>A Chrome browser extension was used to simulate access to only those URLs that are mentioned in the government order.</p> <p>A detailed description of the method, its limitations, and the full analysis of the findings was published on Medianama at <a href="https://www.medianama.com/2020/01/223-analysis-of-whitelisted-urls-in-jammu-and-kashmir-how-usable-are-they/">Even the 301 whitelisted sites in Jammu and Kashmir are not entirely accessible: An analysis </a>on 28 January 2020.</p> <p>For information on how to read this dataset, refer to the tab entitled "About this sheet". A numerical summary of the findings of this analysis is present in the tab entitled "Summary of findings".</p> <p>Data provided AS-IS, without warranty as to accuracy or completeness.</p> <p>This dataset has been released under the <a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons-Attribution-Share Alike (CC-BY-SA) 4.0 International License</a>. All uses of the accompanying data and modifications and derivatives thereof must contain the following attribution: "By Rohini Lakshané and Prateek Waghre (2020)".</p> <p>All versions have been uploaded in 3 file formats: PDF, XLSX and ODS.</p>
Data licences and organization type of contributors to the Global Biodiversity Information Facility as of 19 January 2016
<p>Data from the Global Biodiversity Information Facility were extracted using R (version 3.2.0) on 9 July 2015 using the rgbif package (version 0.9.0) (Chamberlain, S., Ram, K., Barve, V. & Mcglinn, D. (2015) Package ‘rgbif’: Interface to the Global 'Biodiversity' Information Facility 'API' http://cran.r-project.org/web/packages/rgbif/rgbif.pdf). The ‘rights’ statements was extracted for all occurrence datasets with one or more observations. A total of 12,458 datasets were extracted, but only about 11% of the datasets have an explicit data-useage-rights statement at the dataset level. However, some datasets use the occurrence level ‘rights’ and ‘accessRights’ fields. To extract these data the rights information was obtained from the first record of each dataset where a rights statement was missing at the dataset level.</p> <p>The datasets were categorized into 13 different types depending on the origin of the observations.</p> <ol> <li>Biodiversity Information Facility or data centre</li> <li>Botanical Garden or Herbarium</li> <li>Citizen science</li> <li>Commercial</li> <li>Data publisher</li> <li>Educational</li> <li>Government</li> <li>Museum</li> <li>Network</li> <li>Parks Authority or Nature Reserve</li> <li>Research institution</li> <li>Society</li> <li>Foundations</li> </ol>
Landslides from Space - Hpakan Jade Mine Landslides, Myanmar (January 2016)
<p>Between November 2015 and January 2016 multiple landslide occurred in the Hpakan Jade Mine. In total more than 200 people died in these human made accidents.</p> <p>The pre-event acquisition is from 23rd November 2015 (Sentinel-2) and the post-event acquisition is from 22 March 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2015-2016)</em></p>
Landslides from Space - Volcan Landslide (10th January 2017)
<p>On 10th January 2017 a landslide hit the village Volcan. Two people died in this event and the famous Dakar Rally was disrupted.<br> <br> The pre-event acquisition is from 17th December 2016 (Sentinel-2) and the post-event acquisition is from 15th February 2017 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016-2017)</em></p>
Landslides from Space - Landslide between Aickens and Jacksons, New Zealand (18th January 2017)
<p>Heavy rainfall triggered on 18th January 2017 a landslide on the West Coast of New Zealand. The landslide blocked a street and disconnected the villages Aickens and Jacksons.<br> <br> The pre-event acquisition is from 4th January 2017 (Sentinel-2) and the post-event acquisition is from 24th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Standard Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Instance-Inverse Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Instance-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.0.0 - January 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>January 25, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/January-25%2C-2021">here</a>.</li></ul>
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.