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40 results for “2015-2016”
Hysteresis of the Sarracenia Purpurea Microecosystem in Northern Vermont 2015-2016
The restoration of plant and animal communities damaged by chronic detrital or nutrient enrichment is a long-standing environmental problem. In a replicated greenhouse experiment with the aquatic microbial community that inhabits the cup-shaped leaves of the carnivorous pitcher plant Sarracenia purpurea, we monitored O2 levels and the concentration of bovine serum album (BSA), an effective molecular substitute for detritus. Low BSA enrichment rates triggered a classic hysteresis response, a substantial lag in the return of O2 levels. At intermediate BSA enrichment rates, O2 levels closely tracked BSA concentrations during both the enrichment and recovery phases. High BSA enrichment rates induced an unusual "anti-clockwise" hysteresis in which O2 levels were higher during the recovery phase than during the enrichment phase. These experiments revealed complex dynamic behavior in response to enrichment rates of a single environmental driver. Our results suggest that the past trajectory of enrichment is critical for understanding how systems will respond to restoration initiatives. Specifically, systems that have been exposed to chronic low levels of enrichment may be the ones that are most resistant to restoration efforts that reduce nutrient or detrital inputs.
Ovenbird song recordings from Alberta (Canada) with individual labels and spatial locations, 2015-2016
This dataset includes spatially localized and individually identified Ovenbird songs. We used automated species detection and acoustic localization to localize Ovenbird singing events from microphone arrays in Alberta, Canada (2015-2016). We then hand-annotated songs to individuals based on acoustic characteristics. This dataset includes the manual annotations and annotations from automated individual identification approaches. This data publication pertains to the manuscript [in prep] by Lapp et al on Ovenbird individual identification and provides further details on the study and the individual identification approach.
Inter-Chemical Correlation results for the study: NHANES20152016 (NHANES Survey 2015-2016)
Title: NHANES Survey 2015-2016 <br>Species: Homo sapiens <br>Number of samples: 9170 <br>Number of named analytes: 341 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
West Texas Lightning Mapping Array - KTaL 2015-2016
<p>West Texas Lightning Mapping Array data collected near Lubbock, Texas as part of the Kinematic Texture and Lightning (KTaL) field campaign in 2015-2016. Includes VHF source locations and clustered flashes and their physical properties. Data are a subset of the full record, corresponding to the storms studied in a forthcoming publication.</p>
Groundwater and surface water chemistry from the Hole-in-the-Donut and nearby Taylor Slough in Everglades National Park, Florida, USA: 2015-2016
This research investigated the effects of the vegetation and soil removal on the hydrologic conditions of the Hole-in-the-donut (HID) of Everglades National Park. Groundwater and surface water samples were collected from 2 wells within the HID (DO1,DO3) and two wells outside the HID in adjacent Taylor Slough (NP-67, TSB). Surface water was collected at each site if present.
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
Toolik Lake 2011 common garden leaf length phenology 2015-2016 Alaska
Data on Eriophorum vaginatum leaf length collected from a common garden established at Toolik Lake in 2011 with tussocks from No Name Creek, Coldfoot, Eagle Creek, Toolik Lake, Sagwon, and Prudhoe Bay. Data collected during the growing seasons of 2015 and 2016. Results published in Parker, T. C., J. Tang, M. B. Clark, M. M. Moody, and N. Fetcher. 2017. Ecotypic differences in the phenology of the tundra species Eriophorum vaginatum reflect sites of origin. Ecology and Evolution 7: 9775-9786. doi: 10.1002/ece3.3445
Alaskan Peatland Experiment (APEX): Static chamber methane fluxes from fen sites, 2005-2011 and 2015-2016
Methane fluxes were collected at APEX rich fen during the summers of 2005 - 2011 and 2015-2016. APEX Fen is located in the Bonanza Creek LTER outside of Fairbanks, AK. APEX Fen uses a full-factorial water table (flooded or raised, lowered or drained, and control treatments, as well as a moisture gradient). A warming manipulation (warmed, unwarmed treatment) was in place in 2005-2011, but ceased by 2014. In 2015, vegetation manipulation subplots were installed within the water table treatments.
Hubbard Brook Experimental Forest: Soil Acid-Base Properties and Microbial Activity, Watershed 1 and West of Watershed 6 (2015-2016)
In summer 2015 and spring 2016, researchers collected soils from the CaSiO3-enriched watershed at Hubbard Brook (W1) and from a nearby site west of the reference watershed at Hubbard Brook (W6). These soils were sampled throughout the hardwood zone of each watershed, and the sampling scheme explicitly examined pit-and-mound microtopographic gradients. These soils were analyzed for acid-base properties, net and gross N cycling rates, microbial biomass, and C cycling rates. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Innovations in scholarly communication - data of the global 2015-2016 survey
<p>Innovations in scholarly communication - data of the global 2015-2016 survey.</p> <p>This data set contains:</p> <ul> <li>Full raw (anonymized) and cleaned data files of the 2015-2016 global Survey on Innovations in Scholarly Communication. Data are in xls format (raw and cleaned) and csv format (only for cleaned data as the raw data contain non-Roman script).</li> <li>Survey questionnaires for 7 languages (zipped PDFs)</li> <li>Variable list (xls)</li> <li>Readme file (txt)</li> </ul> <p>The data files contain >3,000,000 cells, and thus cannot be opened in their entirety in Google Drive.</p> <p>Many new websites and online tools have come into existence to support<br /> scholarly communication in all phases of the research workflow. To what extent<br /> researchers are using these and more traditional tools has been largely<br /> unknown. This 2015-2016 survey aimed to fill that gap. Its results may help<br /> decision making by stakeholders supporting researchers and may also help<br /> researchers wishing to reflect on their own online workflows. In addition,<br /> information on tools usage can inform studies of changing research workflows.<br /> The online survey employed an open, non-probability sample. A largely<br /> self-selected group of 20663 researchers, librarians, editors, publishers and<br /> other groups involved in research took the survey, which was available in seven<br /> languages. The survey was open from May 10, 2015 to February 10, 2016. It<br /> captured information on tool usage for 17 research activities, stance towards<br /> open access and open science, and expectations of the most important<br /> development in scholarly communication. Respondents’ demographics<br /> included research roles, country of affiliation, research discipline and year of<br /> first publication.</p> <p>A full description of data collection, survey response and methodology is in a data publication in F1000 Research:</p> <p>Kramer, Bianca & Jeroen Bosman (2016) Innovations in scholarly communication - global survey on research tool usage. F1000 Research. DOI:10.12688/f1000research.8414.1</p> <p>Contact:</p> <p>Jeroen Bosman: http://orcid.org/0000-0001-5796-2727 / j.bosman@uu.nl</p> <p>Bianca Kramer: http://orcid.org/0000-0002-5965-6560 / b.m.r.kramer@uu.nl</p>
Effects of the 2015-2016 El Niño on Currents and Temperatures on the Northern Peruvian Coast
<p>Temperatures and currents were measured using equipment moored on an oil platform (4°26.90' S, 81° 20.13' W) off Talara, Peru. The depth of the platform was 45 m at the center and approximately 50 m around the edge. A thermistor chain (RBRconcerto) with 3 m intervals was moored at the center of the platform to determine variations in the vertical thermal structure. An acoustic Doppler current profiler (ADCP, RDI WH300) was moored on the bottom about 80 m to the west of the platform. The sampling intervals were 10 minutes for the thermistor chain and 30 minutes for the ADCP. The equipment was deployed on 6 December 2014, and maintained until 27 May 2016, for one year and five months. As the mooring line of the thermistor chain was lost, however, the thermistor chain data were obtained until 30 December 2015. To obtain the daily data, a median filter was applied to the raw temperature and a two-day moving average to the raw current data. The data in MATLAB binary format and the explanation file were uploaded.</p>
Central Valley InSAR Data - 2015 -2016 -2017-2018
<p>InSAR range change data, cumulative since 2/2015.</p> <p>The files are in ascii and are of the form</p> <p>Decimal degrees longitude, Decimal degrees latitude, Range change (cm)</p> <p>.</p> <p>.</p> <p>.</p>
Curren density from MMS FGM data of 2015-2016 using curlometer method
<p>Valid date range:</p> <p>2015/09/06-2015/09/15<br> 2015/09/24-2015/09/29<br> 2015/10/09-2015/10/13<br> 2015/10/22-2015/11/17<br> 2015/11/26-2015/12/15<br> 2015/12/17-2015/12/18<br> 2015/12/20-2016/01/12<br> 2016/01/20-2016/02/17<br> 2016/02/26-2016/03/22<br> 2016/04/02-2016/04/19<br> 2016/04/29-2016/05/03<br> 2016/05/14-2016/05/17<br> 2016/05/25-2016/06/14<br> 2016/06/22-2016/07/26<br> 2016/08/03-2016/09/30<br> 2016/10/01-2016/10/05<br> 2016/10/08-2016/10/19<br> 2016/10/23-2016/11/15<br> 2016/11/19-2016/12/13<br> 2016/12/17-2016/12/27</p> <p>Contains the following data (time resolution 1 min):</p> <p>Epoch</p> <p>Position</p> <p>Current density (from MMS FGM data using curlometer method)</p> <p>Magnetic field</p> <p>Interplanetary magnetic field</p> <p>Solar wind pressure</p> <p>AE index</p> <p>SYM_H index</p> <p>Footpoint on the equatorial plane</p> <p>Tetrahedral configure factors</p> <p>DivB/curB</p>
Time series of environmental parameters and organic matter analyses for dissolved and particulate organic matter in the Neuse River Estuary, North Carolina, USA 2015-2016
Environmental parameters and organic matter analyses (concentration, absorbance, fluorescence) for dissolved and particulate organic matter for the Neuse River Estuary, North Carolina, USA from 20 July 2015-28 July 2016. Samples were collected bi-weekly from July 2015-October 2015 and March 2016-July 2016 and monthly from November 2015-February 2016. The dataset consists of environmental parameters measured (water temperature, salinity, percent dissolved oxygen, turbidity, chlorophyll-a) and calculated (flushing time) as well as organic matter analyses for dissolved and particulate organic matter (concentration, absorbance, fluorescence) from surface (0.2 m below surface) and bottom (0.5 m above bottom) at 11 stations from the furthest extent of saltwater intrusion (Station 0) to the mouth of the estuary (Station 180). Data were collected as part of the Neuse River Estuary Modeling and Monitoring Program (ModMon; http://paerllab.web.unc.edu/projects/modmon/) at the University of North Carolina - Chapel Hill, Institute of Marine Science.
Chemical characteristics of supraglacial stream water on Canada Glacier, McMurdo Dry Valleys, Antarctica during the 2015-2016 austral summer
This data package contains chemical characteristics of water samples collected along four supraglacial streams on Canada Glacier, located in Taylor Valley, McMurdo Dry Valleys, Antarctica. Samples were collected during the 2015-16 and 2016-17 austral summers in order to characterize the spatial and temporal evolution of glacial meltwater as it travels over and off Canada Glacier. Samples from the 2015-16 austral summer have been fully analyzed and those data are included here. Samples from the 2016-17 austral summer have been stored frozen since collection and have not been analyzed. This package may be amended in the future if analysis of the 2016-17 samples occurs.
Phenological time lapse images from landscape camera MC118 in Paljakka Spruce stand (2015-2016)
<p>This dataset contains phenological time lapse images from x camera Paljakka Spruce stand. Camera was mounted at landscape view level at location 64.677381;28.114014(N;E, WGS84). Images were taken between 29.05.2015--06.07.2016.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Phenological time lapse images from landscape camera MC117 in Paljakka Spruce stand (2015-2016)
<p>This dataset contains phenological time lapse images from camera Paljakka Spruce stand. Camera was mounted at landscape view level at location 64.677381;28.114014(N;E, WGS84). Images were taken between 27.05.2015--06.07.2016.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Data from: Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia
Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015-2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.
Termite abundance and ecosystem processes in Maliau Basin, 2015-2016 [HMTF]
<p><strong>Description: </strong></p> <p>This dataset consists of invertebrate abundance data and associated ecosystem measurements (Including leaf litter depth and mass, seedlings, soil moisture and nutrients, and rainfall) measured within an area of lowland, old growth dipterocarp rainforest in the Maliau Basin Conservation Area, Sabah, Malaysia between 2015 and 2016. Data were collected during a collaborative project which was included in the NERC Human-modified tropical forest (HMTF) programme.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/54"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Experimental manipulations of biodiversity at SAFE</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NERC grant NE/L000016/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence na)</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265746">here</a></p> <p><strong>Files: </strong>This consists of 1 file: Termite_monitoring_maliau.xlsx</p> <p><strong>Termite_monitoring_maliau.xlsx</strong></p> <p>This file contains dataset metadata and 11 data tables:</p> <ol> <li> <p><strong>Leaf_litter_depth</strong> (described in worksheet Leaf_litter_depth)</p> <p>Description: summary of leaf litter measurements collected on experimental plots. An in situ assay of ecosystem-level decomposition was carried out by measuring leaf litter depth during the drought (March 2016) and non-drought (October 2016) periods. Forty leaf litter depth measurements were taken in total per plot in March 2016, with 10 measurements spaced every 3 m across four 30 m transect lines, with each transect being separated by 10 m. In October 2016, a total of sixty measurements were taken per plot, similarly spaced out across a total of six 30 m transect lines</p> <p>Number of fields: 5</p> <p>Number of data rows: 800</p> <p>Fields:</p> <ul> <li><strong>Date</strong>: The month and year in which leaf litter depth was recorded (Field type: Date)</li> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>LINE</strong>: The sampling line within each plot a leaf litter measurement was taken (Field type: ID)</li> <li><strong>Depth_cm</strong>: The depth of leaf litter measured at each point (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Leaf_litter_invertebrates</strong> (described in worksheet Leaf_litter_invertebrates)</p> <p>Description: In 2016 (two years after initial poisoning), fifteen 1 m2 leaf litter samples were collected from each plot. These were collected every 7m along a 100m transect. Sieved litter samples were suspended in Winkler bags for three days to extract invertebrates. All leaf litter invertebrates were identified to order and counted.</p> <p>Number of fields: 31</p> <p>Number of data rows: 120</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>Distance</strong>: Distance along the sampling transect in metres (Field type: ID)</li> <li><strong>Coleoptera_Adults</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Coleoptera_Larvae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Diptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Hemiptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Araneae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Opiliones</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Isopoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Oligochaeta</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Hymenoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Formicidae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Mollusca</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Lepidoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Chilipoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Diplopoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Thysanoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Psocoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Dermaptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Orthoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Blattodea</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Leeches</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Plecoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Neuoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Trichoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Mecoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Odonata</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Siphonaptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Termites</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Pseudoscorpions</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> </ul> </li> <li> <p><strong>Leaf_litter_mass</strong> (described in worksheet Leaf_litter_mass)</p> <p>Description: Decomposition rate was assessed using leaf litter decomposition bags. We collected freshly abscised Shorea johorensis leaf litter from trees close to our experimental plots for use in the leaf litter decomposition bags. The leaf litter was dried at 60 degrees Celsius until it reached a constant weight. We used 300-micron nylon mesh to produce macroinvertebrate exclusion bags, the closed-bag treatment, and created an open-bag treatment by cutting 10, 1 cm holes in each side of the 300-micron mesh bags to allow access to the material by termites and other macroinvertebrates. This approach avoided any unintentional bias due to the use of different mesh size. Each leaf litter bag contained on average 10.5 g ± 0.6 g of dried Shorea johorensis. We left litter bags on the forest floor for 112 days before collection. Bags were placed on plots at the beginning of the 2015 drought (August 2015) and again during the non-drought period (July 2016).</p> <p>Number of fields: 6</p> <p>Number of data rows: 87</p> <p>Fields:</p> <ul> <li><strong>Condition</strong>: The rainfall season in which leaf litter bags were deployed (Field type: Categorical)</li> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Bag_treatment</strong>: The treatment applied to each leaf litter bag - open = accessible to invertebrates, closed = inaccessible to invertebrates (Field type: Categorical)</li> <li><strong>Plot_treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>Mass_loss</strong>: total leaf litter mass loss from each bag in grams (Field type: Numeric)</li> <li><strong>Proportion</strong>: the proportion of leaf litter mass loss from each bag (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Non_target_inverts</strong> (described in worksheet Non_target_inverts)</p> <p>Description: non-termites were collected in 2014 (pre-drought and pre-suppression), 2015 (during the drought and the suppression) and 2016 (post-drought). We collected 1m2, leaf litter samples, sieved the leaf litter and extracted invertebrates with Winkler bags for three days.</p> <p>Number of fields: 5</p> <p>Number of data rows: 5040</p> <p>Fields:</p> <ul> <li><strong>Year</strong>: the year in which sampling occured (Field type: ID)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>variable</strong>: the order of non-target invertebrates samples (Field type: Taxa)</li> <li><strong>value</strong>: the number of individuals belonging to each order (Field type: Numeric)</li> <li><strong>log</strong>: the log of the number of individuals belonging to each order (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Seedling_survival_non_drought</strong> (described in worksheet Seedling_survival_non_drought)</p> <p>Description: Seedling mortality was assessed using a seedling transplant experiment. In July 2015, 200 individuals of a leguminous liana, Agelaea borneensis, were collected from the forest matrix surrounding our plots. Seedlings were selected from seedling mats resulting from a masting event in 2014. We selected individuals that had only their cotyledons and had not yet developed their first true leaves, and were roughly the same height. We are therefore confident that individuals were all of the same age and developmental stage and that we minimised confounding influences of genetic variability by using individuals from the same conspecific seedling mat. Seedlings were planted in the ground in July 2015 in the same grid of 25 used to assess soil moisture (n = 25 per plot), which was located within the central 50 m sampling area of experimental plots. Each seedling was separated by at least 5 m from the next closest seedling. To minimise the effect of stochastic disturbance-induced mortality as a result of transplantation shock, we used the number of individuals alive one month after the initial transplant as the baseline abundance. Survival of seedlings during the drought was assessed 11 months after transplantation, in June 2016. Following this assessment, the number of live individuals in June 2016 was used as a new baseline abundance. Survival during non-drought conditions was assessed 12 months later in June 2017</p> <p>Number of fields: 4</p> <p>Number of data rows: 64</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>alive_2016</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>alive.2017</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Seedling_survival_drought</strong> (described in worksheet Seedling_survival_drought)</p> <p>Description: Tree seedlings survival during the drought</p> <p>Number of fields: 3</p> <p>Number of data rows: 274</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>alive_2016</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Soil_moisture</strong> (described in worksheet Soil_moisture)</p> <p>Description: Soil moisture was measured using a Delta-T Devices HH2 moisture metre in March and October 2016. Soil moisture was recorded at 25 points, spread evenly across each plot in a grid, with each sampling point separated by 5 m from the next point.</p> <p>Number of fields: 5</p> <p>Number of data rows: 400</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>soil_moisture</strong>: the % soil moisture measured at each point (Field type: Numeric)</li> <li><strong>condition</strong>: The rainfall season in which soil moisture measurements were taken (Field type: Categorical)</li> <li><strong>date</strong>: the month and year in which the soil moisture recording was taken (Field type: Date)</li> </ul> </li> <li> <p><strong>Soil_nutrients</strong> (described in worksheet Soil_nutrients)</p> <p>Description: We used Plant Root Simulator (PRS®) resin probes to assess mineralization rates of plant available soil nutrients (NO3-, NH4+, P, K, Ca, Mg, Mn, Al, Fe, Zn) over a two-week period, during drought and non-drought conditions. In March 2016, we buried two anion and cation probe pairs at a random subsample of 12 points within the 25 sampling grid used to measure soil moisture. In October 2016, four probe pairs were placed at each point of the complete 25 sampling grid. We buried the probe membranes to a depth of 10 cm and left them in situ for two weeks, after which they were removed from the soil, cleaned with de-ionized water and subsequently analysed by Western Ag Innovations, Saskatoon, Canada.</p> <p>Number of fields: 15</p> <p>Number of data rows: 292</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>Condition</strong>: the season in which the soil nutrient sampling occurred – drought (2015) or non-drought (2016) (Field type: Categorical)</li> <li><strong>NO3_N_micro_grams/10cm2/burial length</strong>: Soil NO3 at 10cm2 profile (Field type: Numeric)</li> <li><strong>NH4_N_micro_grams/10cm2/burial length</strong>: Soil NH4 at 10cm2 profile (Field type: Numeric)</li> <li><strong>Ca_micro_grams/10cm2/burial length</strong>: Soil Ca at 10cm2 profile (Field type: Numeric)</li> <li><strong>Mg_micro_grams/10cm2/burial length</strong>: Soil Mg at 10cm2 profile (Field type: Numeric)</li> <li><strong>K_micro_grams/10cm2/burial length</strong>: Soil K at 10cm2 profile (Field type: Numeric)</li> <li><strong>P_micro_grams/10cm2/burial length</strong>: Soil P at 10cm2 profile (Field type: Numeric)</li> <li><strong>Fe_micro_grams/10cm2/burial length</strong>: Soil Fe at 10cm2 profile (Field type: Numeric)</li> <li><strong>Mn_micro_grams/10cm2/burial length</strong>: Soil Mn at 10cm2 profile (Field type: Numeric)</li> <li><strong>Cu_micro_grams/10cm2/burial length</strong>: Soil Cu at 10cm2 profile (Field type: Numeric)</li> <li><strong>Zn_micro_grams/10cm2/burial length</strong>: Soil Zn at 10cm2 profile (Field type: Numeric)</li> <li><strong>B_micro_grams/10cm2/burial length</strong>: Soil B at 10cm2 profile (Field type: Numeric)</li> <li><strong>Al_micro_grams/10cm2/burial length</strong>: Soil Al at 10cm2 profile (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Termite_cumulative_attacks</strong> (described in worksheet Termite_cummulative_attack)</p> <p>Description: We monitored termite feeding activity on the plots using untreated TPRs. Sixteen untreated TPRs were placed on each plot and were scored for termite attack on a 0 to 5 scale, where 0 is untouched and 5 is completely eaten. After one month, TPR were scored and replaced. Before they were replaced, we recorded the cumulative amount of TPR consumed on each plot and calculated the plot-level cumulative mean attack scores.</p> <p>Number of fields: 4</p> <p>Number of data rows: 120</p> <p>Fields:</p> <ul> <li><strong>month</strong>: The month in which termite attack scores were recorded (Field type: ID)</li> <li><strong>cumulative_consumption</strong>: The cumulative consumption rate for each plot (Field type: Numeric)</li> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Termite_C_plot_SPI</strong> (described in worksheet Termite_C_plots_SPI)</p> <p>Description: To assess the relationship between rainfall and termite abundance, we carried out termite transects on control plots every 2 months from March 2016 to December 2016 and also at the beginning and the end of the experimental period in June 2015 and June 2017. Daily total rainfall was collected from Danum Valley forest reserve (4°57′53″ to 55″ N and 117°48′14″ to 30″E) from November 2010 to March 2017. Daily values were used to calculate total monthly rainfall in the region, and this was used to calculate 3-monthly Standardised Precipitation Index (SPI)[2] in the 'SPI' package in R. The SPI is a climatic proxy used to quantify and monitor drought; negative values indicate drier than average conditions, while positive values represent wetter than average conditions.</p> <p>Number of fields: 5</p> <p>Number of data rows: 32</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: the control plot on which samples were collected (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>total</strong>: total number of termite hits recorded (Field type: Numeric)</li> <li><strong>date</strong>: the month in which sampling occurred (Field type: Date)</li> <li><strong>SPI</strong>: the standardized precipitation index number calculated for each time period from rainfall data collected at Danum Valley Field Station (Field type: Numeric)</li> <li><strong>Wet.dry</strong>: the rainfall conditions at the time of sampling (wet = 2017, dry = 2015) (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Termite_hits_on_T_and_C_plot</strong> (described in worksheet Termite_hits_on_T_and_C_plots)</p> <p>Description: termite abundance data. In order to quantify the effect of the suppression treatment on termite community composition, we sampled termites on suppression and control plots in June 2015 and October 2016 using the Jones and Eggleton transect method</p> <p>Number of fields: 7</p> <p>Number of data rows: 192</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>date</strong>: The month and year in which the sampling occurred (Field type: Date)</li> <li><strong>genus</strong>: the genus to which each termite encounter belongs (Field type: Taxa)</li> <li><strong>hits</strong>: number of termite of hits on each plot (Field type: Numeric)</li> <li><strong>SPI</strong>: the standardized precipitation index at the time of each sampling occasion (Field type: Numeric)</li> <li><strong>Wet.dry</strong>: the season in which sampling occurred – wet = 2016, dry = 2015 (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2014-10-01 to 2017-07-30</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Annelida<br>  -  - Clitellata<br>  -  -  - [Oligochaeta]<br>  - Arthropoda<br>  -  - Arachnida<br>  -  -  - Araneae<br>  -  -  - Opiliones<br>  -  -  - Pseudoscorpiones<br>  -  - Chilopoda<br>  -  - Diplopoda<br>  -  - Insecta<br>  -  -  - Blattodea<br>  -  -  -  - [Termites]<br>  -  -  - Coleoptera<br>  -  -  - Dermaptera<br>  -  -  - Diptera<br>  -  -  - Hemiptera<br>  -  -  - Hymenoptera<br>  -  -  -  - Formicidae<br>  -  -  - Isoptera<br>  -  -  -  -  - <em>Procapritermes</em><br>  -  -  -  -  - <em>Prohamitermes</em><br>  -  -  -  - Rhinotermitidae<br>  -  -  -  -  - <em>Heterotermes</em><br>  -  -  -  -  - <em>Parrhinotermes</em><br>  -  -  -  -  - <em>Schedorhinotermes</em><br>  -  -  -  - Termitidae<br>  -  -  -  -  - <em>Bulbitermes</em><br>  -  -  -  -  - <em>Dicuspiditermes</em><br>  -  -  -  -  - <em>Globitermes</em><br>  -  -  -  -  - <em>Macrotermes</em><br>  -  -  -  -  - <em>Malaysiotermes</em><br>  -  -  -  -  - <em>Microcerotermes</em><br>  -  -  -  -  - <em>Odontotermes</em><br>  -  -  - Lepidoptera<br>  -  -  - Mecoptera<br>  -  -  - Neuroptera<br>  -  -  - Odonata<br>  -  -  - Orthoptera<br>  -  -  - Plecoptera<br>  -  -  - Psocodea<br>  -  -  - Siphonaptera<br>  -  -  - Thysanoptera<br>  -  -  - Trichoptera<br>  -  - Malacostraca<br>  -  -  - Isopoda<br>  - Mollusca</p> <p> </p>
TTU-Ka Mobile Doppler Radar - KTaL 2015-2016
<p>Mobile Doppler weather radar data collected near Lubbock, Texas as part of the Kinematic Texture and Lightning (KTaL) field campaign in 2015-2016. Radial velocity data have been unfolded and other quality control measures applied. Files are in NetCDF format with the CF-Radial metadata convention. Data are a subset of the full record, corresponding to the storms studied in a forthcoming publication.</p>
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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.