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12,237 results for “2019”
Spatial distribution of snow water equivalent for the Niwot Ridge, 1996 - 2019
This dataset provides a daily estimation of snow water equivalent for the Niwot Ridge during snow melting period from 1997 to 2019 at 30-meter spatial resolution. The dataset includes two series of SWE data: 1) 1996-2007 daily SWE dataset is generated by Jepsen et al., (2012); 2) 2008-2019 daily SWE dataset is generated by Dr. Kehan Yang following the same method used by Jepsen et al., (2012). In brief, a physically based reconstruction model is used to calculate daily SWE backward from snow disappearance date to peak snow accumulation. The infilled hourly climate data set for C1, Saddle and D1 (data available at https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-nwt.168.2) is interpolated and used as the meteorological forcing in the snow energy balance calculation of SWE reconstruction. The shortwave radiation is estimated by downscaling hourly product of the Geostationary Operational Environmental Satellite (GOES) using TOPORAD tool. The USGS Landsat Level-3 fractional snow-covered area product is used to proportion potential energy flux for snowmelt at the pixel scale. Please see detailed methods included with this data package for more details and references.
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Dissolved inorganic carbon and alkalinity of discrete water column samples, collected aboard Palmer LTER annual cruises of the Western Antarctic Peninsula, 1993 - 2019.
Dissolved inorganic carbon (DIC or total CO2 – TCO2) and total alkalinity (TALK) are two of the four parameters defining the carbonate system in seawater. DIC is composed of dissolved CO2 gas, which dissociates into carbonate, 〖CO〗_3^(2-), and bicarbonate, 〖HCO〗_3^- in seawater. About 90% of the DIC is in the form of bicarbonate, ~10% is carbonate, and ~1% is CO2. The dissociation of CO2 dissolved in seawater into carbonate and bicarbonate gives seawater its great capacity to absorb CO2 from the atmosphere. Alkalinity (also known as “buffer capacity”) is a measure of the capacity of water to neutralize acids. Alkalinity is a complex product of the concentrations of (in decreasing order of importance) the DIC components, borate, hydroxide, phosphate, silicate and dissolved ammonium. Ocean biology regulates the alkalinity through the uptake and release of the DIC and the macronutrients N, P and Si. We measure surface DIC and ALK to understand the exchange of CO2 across the air-sea interface in our study area. With DIC and dissolved CO2, we can also derive estimates of ocean pH and thus monitor the extent and evolution of ocean acidification. Analytical methods and QC are presented under the Methods and Protocols tab.
PIE LTER time series of nutrient grab samples from Ipswich River and Parker River watershed catchments, Masachusetts, with frequency ranging from weekly to monthly between 2001 and 2019.
Time series of nutrient grab samples collected by hand (i.e. not with the Sigma autosampler) with frequency ranging from weekly to monthly between 2001 and 2016. Sites include three headwater catchments of contrasting land use (forest, urban, wetland), and the mouth of the two main watersheds draining to the Plum Island Estuary (Ipswich and Parker R.). An additional time series was collected for a site in the Upper Ipswich at North Reading and at Fish Br. in Boxford – this sampling was ended in 2002. All Samples were analysed for NO3, NO2, NH4, PO4, TDN and DOC. Si was analyzed until 2002. Particulates, anions and TSS have been analyzed since 2006.
Aboveground biomass from control sites in a Spartina alterniflora-dominated salt marsh at Stackyard Rd, Nelson Island, Parker River NWR, Plum Island Ecosystem LTER, Rowley, MA (2019-2025).
Aboveground biomass is determined non-destructively at permanent plots in a Spartina alterniflora-dominated salt marsh on Nelson Island near Stackyard Rd in the Parker River NWR within the Plum Island Ecosystems (PIE) LTER site, MA.
Aboveground biomass from a Spartina patens-dominated salt marsh at Stackyard Rd, Nelson Island, Parker River NWR, Plum Island Ecosystem LTER, Rowley, MA (2019-2025).
Aboveground biomass is determined destructively in a Spartina patens-dominated salt marsh on Nelson Island near Stackyard Rd in the Parker River NWR within the Plum Island Ecosystem (PIE) LTER site, MA.
Year 2019, 15 minute interval, water quality measurements of water column temperature, salinity, oxygen, and depth in the upper Parker River Estuary near Middle Road Bridge, Newbury, MA.
Year 2019, water quality sonde data.15 minute readings of water column temperature, salinity, oxygen and depth in the upper Parker River Estuary near Middle Rd Bridge, Newbury, MA
Year 2019-2021, 15 minute measurements of stage, water temperature in a small headwater stream draining draining a mainly forested catchment (55% forest + 19% wetland), Cart Cr., Newbury, MA.
Year 2019, 2020, and 2021 continuous measurements, every 15 minutes, were made of stage, water temperature in Cart Creek, Newbury, MA, a small headwater stream draining a mainly forested catchment (55% forest + 19% wetland) in the Parker River watershed. Discharge is determined from stage using discharge vs stage regressions.
FAIRsFAIR Policy and Practice Survey 2019 data for D3.1_D3.2_D6.1
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022. Landscaping activities have been a core activity during the initial stages of the FAIRsFAIR project and there has been close cooperation with colleagues in Work Package 3 carrying out the FAIR data policy and practice analyses; Work Package 2 on assessing FAIR requirements for interoperability and persistence; Work Package 6 on providing an overview of research communities’ needs for competence centres; and Work Package 7 on mapping RDM policies and support as well as FAIR education offerings in European HEIs. In particular, efforts were made to avoid duplication of effort across the three open consultation and survey instruments developed to assess the current landscape and to define a consistent approach to presenting our findings.</p> <p>As part of the landscape analysis, FAIRsFAIR ran an open consultation on Policy and Practice which was made available via EU Survey from August 2 - September 27, 2019. The consultation was also made accessible from the FAIRsFAIR website. A total of 106 responses were submitted by research support staff from across Europe and beyond. The open consultation included both open-ended and closed questions which sought to identify the different levels of maturity with regards to FAIR practices among disciplines, the range of policies that influence the way that researchers work, and the sources of support currently available to researchers. In developing the questions, FAIRsFAIR worked collaboratively with several related initiatives including the EOSC 5B projects, the Group of European Data Experts in RDA (GEDE), and the EOSC FAIR Working Group and Landscape Working Group to avoid duplication of effort in our information collection. FAIRsFAIR will continue to cooperate with these and other initiatives over the life of the project. The open consultation targeted members of the research support community to gain insights on their views and experiences in relation to implementing the FAIR principles.</p> <p>The open consultation questions were grouped under five broad themes:</p> <p>1) Practice</p> <p>2) Policy</p> <p>3) Repositories</p> <p>4) Skills</p> <p>5) Competence centres</p> <p>The data resulting from the open consultation has been used to inform three key FAIRsFAIR deliverables:</p> <ul> <li>D3.1 FAIR Data Policy Landscape Analysis</li> <li>D3.2 FAIR Data Practice Analysis</li> <li>D6.1 Overview of Needs for Competence Centre</li> </ul> <p> </p> <p> </p>
Christmas traditions 2019 - Nobile Teatro di San Giacomo di Corfu - Kynopiastes Philharmonic - Corfu - Hellas
<blockquote> <p>The Kynopiastes Philharmonic, one of the 18 philharmonics on the island of Corfu, founded in 1966. </p> <p>Τhe conductor Nikas Spyridon, led the orchestra's concert, on 28 December 2019, at the Nobile Teatro di San Giacomo di Corfu.</p> </blockquote> <p>Η Φιλαρμονική των Κυνοπιαστών, μία εκ των 18 φιλαρμονικών της νήσου Κέρκυρας, ιδρύθηκε το 1966.</p> <p>Ο αρχιμουσικός Νίκας Σπυρίδων, διηύθυνε την ορχήστρα, στις 28 Δεκεμβρίου 2019, στο Θέατρο του Σαν Τζιάκομο της Κέρκυρας.</p>
Living Lab Station data Feb-Sept 2019, Stoke Park, Guildford
<p>Data collected before and after hedges in Stoke Park, Guildford using Living lab stations.</p>
Data physicalization papers analysis (2019) V1
<p>Dataset containing a "report" of the reading of articles listed on <a href="http://dataphys.org/wiki/Bibliography">http://dataphys.org/wiki/Bibliography</a> with classification by human sense used in prototype/proposal.</p>
Modern Fortran Survey 2019 Raw Data
<p>We've organized an online survey about usage of Modern Fortran features (2003/2008 standard) on https://modernfortran.limequery.org</p> <p>The survey was online from June 2019 to March 2020 and includes 140 complete responses and 85 incomplete.</p> <p>This package contains the raw data and the automatically generated statistics from it:</p> <ul> <li>html-forms.zip .. the survey exported as a static HTML page (the original survey hid subquestions based on previous answers)</li> <li>limesurvey-archive.lsa .. the complete survey (including the results) in LimeSurvey archive format (ZIP file with proprietary files)</li> <li>printed-forms.pdf .. the survey as PDF</li> <li>results-{all,complete-only,incomplete-only}.pdf .. the automatically generated statistics of the survey results including plots. "all": include all surveys, "complete-only": only surveys which were completed, "incomplete-only": only surveys which were not completed</li> <li>results-{complete,incomplete}_only.csv .. the results in CSV format for the respective subsets</li> </ul>
BATAL POPS flight (July 2019)
<p>The Printed Optical Particle Spectrometer (POPS) was operated during the BATAL (Balloon Measurements of the Asian Tropopause Aerosol Layer) campaign in 2019 in Hyderabad (17N,78E), India. The primary goal of this campaign is the investigation of the ATAL (Asian Tropopause Aerosol Layer). Here, we use POPS observations from one balloon flight on July 17th 2019 (UT time). The instrument weighs around 800g and uses a 405 mm diode laser. POPS delivers aerosol number concentration and size distribution measurements. Here the file corresponds to concentrations in each size class between 0.15 and ~3 µm.<br> </p>
Coronavirus COVID-19 (2019-nCoV) Data Repository for Africa
<p>The purpose of this repository is to collate data on the ongoing coronavirus pandemic in Africa. Our goal is to record detailed information on each reported case in every African country. We want to build a line list – a table summarizing information about people who are infected, dead, or recovered. The table for each African country would include demographic, location, and symptom (where available) information for each reported case. The data will be obtained from official sources (e.g., WHO, departments of health, CDC etc.) and unofficial sources (e.g., news). Such a dataset has many uses, including studying the spread of COVID-19 across Africa and assessing similarities and differences to what’s being observed in other regions of the world.</p> <p>See the repo here <a href="https://github.com/dsfsi/covid19africa">https://github.com/dsfsi/covid19africa</a></p>
Host Network Traffic 2019
<p><strong><em>Dataset Summary</em></strong></p> <ul> <li><strong>Timespan</strong>: 2019-01-01 : 2019-12-31</li> <li><strong>Granularity: </strong>1-hour disjoint time windows</li> <li><strong># of characteristics observed: </strong>9</li> <li><strong>Hosts observed: </strong>65536</li> <li><strong>Labels: </strong>included</li> <li><strong>Unzipped volume: </strong>approx. 10 GB</li> </ul> <p><strong><em>Dataset Origins</em></strong></p> <p>Dataset was collected over the <strong>whole year</strong> <strong> 2019</strong>. The observation points for the collection of IP flows were located at the borders of the university campus network. The campus university network has /16 CIDR IPv4 network range at disposal and contains various network segments from segments connecting dormitories, over server segments, to a segment containing working stations of university administrative workers. <strong>A host in our dataset is identified by its source IPv4 address. </strong></p> <p><em><strong>Variables</strong></em></p> <p>The dataset contains the following variables:</p> <ul> <li><strong>Aggregations</strong> - created sums of the individual variables over a one-hour interval: <ul> </ul> <ul> <li><strong># of flows </strong>- number of flows for a given source IP </li> <li><strong># of packets </strong> - number of packets for a given source IP</li> <li><strong># of bytes </strong> - number of packets for a given source IP</li> <li><strong>flow duration </strong> - average flow duration in seconds</li> </ul> </li> <li><strong>Distinct Counts </strong>- count of distinct values for each variable over a one-hour window <ul> <li><strong># of peers </strong> - number of distinct communication peers for a given source IP</li> <li><strong># of ports </strong> - number of distinct destination ports for a given source IP</li> <li><strong># of protocols</strong> - number of distinct communication protocols for a given source IP</li> <li><strong># of AS numbers</strong> - number of distinct destination AS numbers for a given source IP</li> <li><strong># of countries </strong> - number of distinct destination countries for a given source </li> </ul> </li> </ul> <p><em><strong>Dataset Structure</strong></em></p> <ul> <li><strong>Dataset Files</strong> - each variable is contained in one <strong>Comma-Separated File (.csv) </strong>file <ul> <li><strong>Row index - </strong> timestamp of the observation window (8760 rows)</li> <li><strong>Columns index - </strong> anonymized IP addresses (65536 columns)</li> </ul> </li> <li><strong>Label File - </strong>contains labels of the individual IP addresses from the Dataset Files <ul> <li><strong>Row index </strong>- anonymized IP addresses (65536 rows)</li> <li><strong>Columns index </strong>- labels for the IP addresses <ul> <li><strong>Subnet </strong>- ID of a subnet - hosts belonging to the same subnet have the same Id.</li> <li><strong>Subnet_range </strong>- CIDR range of a subnet</li> <li><strong>Unit - </strong>an ID of administrative unit owning the network range</li> <li><strong>Sub-unit </strong> - an ID of administrative sub-unit owning the network range</li> <li><strong>Subnet_label - </strong>subnet label <ul> <li><strong>Servers - </strong>selected subnets containing mostly servers (133.250.178.0/24, 133.250.163.0/24)</li> <li><strong>Workstations - </strong>selected subnets containing mostly workstations (133.250.146.0/24, 133.250.157.128/25)</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong><em>Further notes</em></strong></p> <ul> <li><strong>N/A values </strong> <ul> <li><strong>Variables </strong>- means that in a given observation window, the host did not communicate</li> <li><strong>Labels - </strong>no additional information on this IP is available</li> </ul> </li> <li><strong>Dataset load </strong> <ul> <li> <pre><code class="language-python">df = pd.read_csv(<filename>,header=[0], index_col=[0])</code></pre> </li> </ul> </li> </ul>
ANPERC-source/SEG_Annual: The database of words and affiliations of the SEG Annual Conferences (1982 - 2019)
<p>The database of words and affiliations of the SEG Annual Conferences (1982 - 2019) v1.0 This repository includes data for the words and phrases frequency of occurrence analysis "SEGgrams.sqlite" and the database "SEG_affiliations_data.sqlite" consisting of the industry companies and academia of different countries that presented their research during the Society of Explorational Geophysicists Annual Conferences (1982 - 2019) with the corresponding number of affiliations for the whole period of study.</p>
DS_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_SEM images
<p>Scanning electron microscopy images showing P3HT pillar arrays with and without living cells on top</p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe
<p>Near real time epoch 2019 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Inter- and transdisciplinary projects in FP7 and H2020 (May 2019)
<p>The metadata of interdisciplinary (IDR) and transdisciplinary (TDR) projects conducted under the European Union framework programs (FP7 & Horizon 2020) were collected from the Cordis database (<a href="https://cordis.europa.eu/">https://cordis.europa.eu/</a>). SHAPE-ID research team used periodic data dumps, stored in EU open data portal (<a href="https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects">https://data.europa.eu/euodp/en/data/dataset/cordisfp7projects</a> and <a href="https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects">https://data.europa.eu/euodp/en/data/dataset/cordisH2020projects</a>). </p> <p>The data dump from <strong>May 2019</strong> was used, so the FP7 database is complete while H2020 projects were still being added periodically.</p> <p>CORDIS files were subsequently queried for interdisciplinar* or transdicsiplinar*, matched against title or abstract (“objective”). This procedure allowed for creating two subsets:</p> <p>FP7_projects_May2019_IDR_TDR.csv 1750 FP7 projects. Out of 1699 IDR projects, interdisciplinar* featured in 40 titles and 1679 abstracts. Out of 56 TDR projects transdisciplinar* featured in 2 project titles and 54 abstracts.</p> <p>1912 H2020 projects (as of May 2019). Out of 1837 IDR projects, interdisciplinar* featured in 57 titles and 1820 abstracts. Out of 85 TDR projects transdisciplinar* featured in 2 project titles and 85 abstracts.</p> <p><strong>Description of the files </strong></p> <p>CSV files contain the same fields as CORDIS database data dumps: id, acronym, status, programme, topics, framework Programme, title, startDate, endDate, projectUrl, objective, totalCost, ecMaxContribution, call, fundingScheme, coordinator, coordinatorCountry, participants, participantCountries, subjects.</p> <p>Additional fields:</p> <p>IDR - project features interdiciplinary research (1 = yes, 0 = no)</p> <p>TDR - project features transdiciplinary research (1 = yes, 0 = no)</p> <p>Title_Interdisciplinar* - frequency of “interdisciplinar*” in the project title</p> <p>Objective_interdisciplinar*- frequency of “interdisciplinar*” in the project objective</p> <p>Title_transdisciplinar* - frequency of “interdisciplinar*” in the project title</p> <p>Obj_transdisiplinar* - frequency of “interdisciplinar*” in the project objective</p> <p>Reference data (countries, funding schemes/types of action, subjects (SIC codes)) can be found in this dataset: <a href="https://data.europa.eu/euodp/en/data/dataset/cordisref-data">https://data.europa.eu/euodp/en/data/dataset/cordisref-data</a></p> <p> </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
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International Brain Laboratory public data
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OpenNeuro
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