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5,738 results for “Standards”
Plant-mediated root methane emissions and oxidation in a thermokarst bog complex in the Bonanza Creek LTER Experimental Forest III - Non-standard Dark Methane Fluxes and Associated Standard Oxic Fluxes 2015
Vascular plants are important in the wetland methane cycle but their effect on production, oxidation, and transport has high uncertainty, limiting our ability to predict emissions. Vegetation operated on top of baseline methane emissions, which varied with proximity to the thawing permafrost margin. Emissions from vegetated plots increased over the season, resulting in cumulative seasonal methane emissions that were 4.1-5.2 g m-2 season-1 greater than unvegetated plots. Mass balance calculations signify these greater emissions were due to increased methane production (3.0-3.5 g m-2 season-1) and decreased methane oxidation (1.1-1.6 g m-2 season-1). Minimal oxidation occurred along the plant-transport pathway and oxidation was suppressed outside the plant pathway. Our data indicate suppression of methane oxidation was stimulated by root exudates fueling competition among microbes for electron acceptors. Root exudates are known to fuel methane production and our work provides evidence they also decrease methane oxidation. This dataset contains the 2015 weekly anoxic control methane flux from treatment plots within a bog complex in the Bonanza Creek LTER. Anoxic control measurements used to assess the effect of the anoxic fluxes on results. Treatments include natural-vegetation, simulated-aerenchyma, and sphagnum-only. No updates are planned.
Standardized ring-width chronologies for white and black spruce at three sites in the southern Brooks Range, Alaska
Although black spruce is the dominant treeline species in the eastern boreal forest, its distribution stops several kilometers short of treeline in the Brooks Range in Alaska, and white spruce is the dominant treeline species. The explanation for this distribution is not known, but two hypotheses are plausible. First, black spruce may be less tolerant of climatic conditions near treeline than white spruce. Second, black spruce may be unable to regenerate successfully near treeline due to long intervals between fires. We are establishing permanently marked study plots along a transect from the Yukon River basin, where black spruce is the dominant species, to the foothills of the Brooks Range, where it reaches its distributional limit. We are reconstructing recruitment history of both black and white spruce at our study sites, and are reconstructing recent fire history from analysis of fire scars and stand age structures. These data are being used to parameterize matrix population models, with which we are describing patterns of population stability.
SGS-LTER Standard Met Data: 1986-09-12-2010 CR21x Level 2 Meteorological Data on the Central Plains Experimental Range, Nunn, Colorado, USA 1986 - present, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Additional information and referenced materials can be found:http://hdl.handle.net/10217/82146.
SGS-LTER Standard Met Data: Cr21x Station 12 - Daily Meteorological Data on the Central Plains Experimental Range in Nunn, Colorado, USA 1986-2010, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82449. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values.
SGS-LTER Standard Met Data: CR21x Station 12 - Hourly Meteorological Data on the Central Plains Experimental Range Headquarters in Nunn, Colorado, USA 1986-2010, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82449. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values.
SGS-LTER Standard Met Data: CR21x Station 12 - 15 minute preciptation values from LTER meteorological station on the Central Plains Experimental Range in Nunn, Colorado, USA 1986-1994, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82449. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values.
SGS-LTER Standard Met Data: CR21x Station 12 - 15 Minute Meteorological Data on the Central Plains Experimental Range in Nunn, Colorado, USA 1995-2010, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82449. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values.
SGS-LTER Standard Met Data: Monthly precipitation totals and temperatures from the Central Plains Experimental Range, Nunn, CO 1941-1973
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83446. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values.
SGS-LTER Standard Met Data: 1969- 2010 Manually Collected Standard Meteorological Data on the Central Plains Experimental Range, Nunn, Colorado, USA 1969 - 2011, ARS Study Number 6
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. additional information and referenced materials can be found:http://hdl.handle.net/10217/82146.
SGS-LTER Standard Met Data: 1969-2010 Manually Collected Aboveground Meteorological Data: English Units on the Central Plains Experimental Range, Nunn, Colorado, USA, ARS Study Number 4
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82446.
BIDS Data for "An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging"
<p>Base data package for the “An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging” article, formatted corresponding to the Brain Imaging Data Structure.</p>
Branching Trees from standard Epidemic Aftershock Sequences (ETAS) Model (no time, no space)
<p>All files licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)</p> <p>###############<br> 0. SUMMARY<br> ###############</p> <p>1. DESCRIPTION</p> <p>2. INPUT PARAMETERS</p> <p>3. TYPES OF FILES<br> 3.1. Raw data<br> 3.2. List of trees<br> 3.3. Tree-size frequencies</p> <p>4. LIST OF FILES<br> 4.1. Raw data<br> 4.2. List of trees<br> 4.3. Tree-size frequencies<br> 4.4 Known missing/broken files</p> <p>###############<br> 1. DESCRIPTION<br> ################<br> Simulation results of an standard ETAS model as a branching process. Using a two seed version of the RANDU linear congruential pseudorandom number generator. The offspring number is a Poisson number given the rate n(M) (see below). Details of simulation procedure can be found in reference [1]: 'Topological properties of epidemic aftershock processes', by J. Baró submitted to J. of Geophysical Research - Solid Earth (JGR-B)</p> <p>##############<br> 2. INPUT PARAMETERS<br> ##############<br> The input parameters (see reference for details) for each raw and processes data-file are indicated in the prefix of the file: "ETASbranch_b(b)r(r)N(nb)*"</p> <p>- M0(= 1) = magnitude of completeness (arbitrary for the study of topological properties of trees)<br> - (b) = b-value (arbitrary for the study of topological properties of trees)<br> - (nb) = average branching ratio<br> - (r) = ratio a/b</p> <p>The b-value defines the distribution of event-magnitudes: P(M) = 10^(b*(M-M0)) . The nb and a define the productivity law: n(M) = (nb*(b-a)/b)*10^(a*(M-M0))</p> <p><br> #################<br> 3. TYPES OF FILES<br> #################</p> <p>3.1. Raw data:<br> --------------</p> <p>42 x "*.Seq" files with input b=0.50 and different nb, r values. Raw data from simulation code. (all cases, simulated with 10^5 background events)<br> Each row represents an individual event in the point process, or element of the simulated branching forest.<br> Columns description (9 columns x data point):<br> c0:Time (arbitrary, used here as id.)<br> c1:Magnitude of the event<br> c2:Identification number of the cluster or tree<br> c3:Depth of the event in the tree structure (background events have Depth = 0)<br> c4:Time of the direct parent of the event (set to -1 for background event)<br> c5:Magnitude of the direct parent of the event (set to -10 if background event)<br> c6:Time of the event initiating the tree (set to own time if background)<br> c7:Magnitude of the event initiating the tree (set to own magnitude if background)<br> c8:N or offspring number of the event. (Events are leafs if N=0)</p> <p><br> 3.2. List of trees<br> ------------------</p> <p>53 x "*TopoTrees.dat" files obtained from simulations (after processing of *.Seq files. Files ending with "N0.99", "N0.50" obtained from 10^5 background events from files above. Files ending with "N0.500*" obtained from 10^7 simulations)<br> Each row represents an individual tree constituted by one or several causally connected events of the simulated branching forest. Files used to generate fig. 4 of ref. [1]</p> <p>Columns description (8 columns x data point):<br> c0:Maximum Depth of the tree<br> c1:Number of events in the tree<br> c2:Average depth of leaves<br> c3:total number of leaves<br> c4:Sum of the depth of all leaves (=c2*c3)<br> c5:(=0) not used<br> c6:Magnitude of root<br> c7:Maximum magnitude of an event inside the tree</p> <p><br> 4.3. Tree-size frequencies<br> --------------------------</p> <p>18 x "*TopoTrees.FK" files obtained from "*TopoTrees.dat". Contains the frequencies of tree-sizes. Each raw number correspond to a size. Each value corresponds to number of incidences of that size divided by total number of events (10^5 in all cases). Notice that last point is missing at size = max-length, freq.= 1.0 / total number of events. Files used to generate fig. 3 of ref. [1]</p> <p><br> ################<br> 4. LIST OF FILES<br> ################</p> <p>(copy of this text)<br> readme.txt<br> md5:2073cdbb4a0afd8ab96f8bfaef20579f 13 Kb</p> <p>4.1. Raw data (42 files)<br> ------------------------</p> <p>ETASbranch_b0.50r0.00N0.50.Seq<br> md5:bae21678db4fe575b06b69a5f32253d2 12.5 Mb<br> ETASbranch_b0.50r0.00N0.99.Seq<br> md5:76ea70ba94aeeed7ba3c358305f4b85b 1.3 Gb<br> ETASbranch_b0.50r0.05N0.50.Seq<br> md5:0ccf5af07efb9dd99d63a9675fd3069f 12.4 Mb<br> ETASbranch_b0.50r0.05N0.99.Seq<br> md5:77a7bfd490af54a5a3d15c4183e9e6f7 1.4 Gb<br> ETASbranch_b0.50r0.10N0.50.Seq<br> md5:f274717039f8f69ce57d5054577777af 12.4 Mb<br> ETASbranch_b0.50r0.10N0.99.Seq<br> md5:e18f4c26acfdff8d79a6223aa86867d2 1.3 Gb<br> ETASbranch_b0.50r0.15N0.50.Seq<br> md5:5f93c14a8d0f88fd021bc6d8a5fc7d6e 12.3 Mb<br> ETASbranch_b0.50r0.15N0.99.Seq<br> md5:906e09ecb6c6c18fdbcfdca3d9dbe225 1.3 Gb<br> ETASbranch_b0.50r0.20N0.50.Seq<br> md5:2467c2c7fafeddaa630074e991cb7767 12.4 Mb<br> ETASbranch_b0.50r0.20N0.99.Seq<br> md5:64c45a47b7f953d7088b10d4badd068c 1.3 Gb<br> ETASbranch_b0.50r0.25N0.50.Seq<br> md5:d65abaa4664b2412707b27b6e9143215 12.4 Mb<br> ETASbranch_b0.50r0.25N0.99.Seq<br> md5:8d7467b0bd80ce7e8cbcaf5dfe2725b7 1.2 Gb<br> ETASbranch_b0.50r0.30N0.50.Seq<br> md5:fd3af9802b3b66a623172bb2aafc0a0b 12.4 Mb<br> ETASbranch_b0.50r0.30N0.99.Seq<br> md5:2ad1b9b9c858638067af6068dfd25d59 1.3 Gb<br> ETASbranch_b0.50r0.35N0.50.Seq<br> md5:8cf36cc90a19f1283c838dee4849626b 12.5 Mb<br> ETASbranch_b0.50r0.35N0.99.Seq<br> md5:6b35085100561865ac9463edfb166d7f 1.4 Gb<br> ETASbranch_b0.50r0.40N0.50.Seq<br> md5:317b154c8ed74262b18c41762597d236 12.3 Mb<br> ETASbranch_b0.50r0.40N0.99.Seq<br> md5:c83dc299035928c9ae8a7f6d101ac9b8 1.2 Gb<br> ETASbranch_b0.50r0.45N0.50.Seq<br> md5:d3baccfbf347e555c30f3dfb1db1d9c0 12.4 Mb<br> ETASbranch_b0.50r0.45N0.99.Seq<br> md5:5bf42163acc2eae1eeae8549b850ccc9 1.1 Gb<br> ETASbranch_b0.50r0.50N0.50.Seq<br> md5:3ad7427725dfcf302c2dc2c519dad8c0 12.4 Mb<br> ETASbranch_b0.50r0.50N0.99.Seq<br> md5:7316fb3661b87215a1a7b1c11a54975b 1.3 Gb<br> ETASbranch_b0.50r0.55N0.50.Seq<br> md5:6dbc9846506675c61d110ae918165a6b 12.4 Mb<br> ETASbranch_b0.50r0.55N0.99.Seq<br> md5:5d4d007bf898512185dd2f87b715715e 1.4 Gb<br> ETASbranch_b0.50r0.60N0.50.Seq<br> md5:569c1c409ef618f518fe6fb6f41493a3 12.7 Mb<br> ETASbranch_b0.50r0.60N0.99.Seq<br> md5:e739613598e8d2530d108ff24e8c045a 1.1 Gb<br> ETASbranch_b0.50r0.65N0.50.Seq<br> md5:e95cead475c1eedb0618bebf1b548ec2 12.3 Mb<br> ETASbranch_b0.50r0.65N0.99.Seq<br> md5:8fe8b042085f261971d94bf12f96dc7e 1 Gb<br> ETASbranch_b0.50r0.70N0.50.Seq<br> md5:48824f1efdbc31c5bb4d4f799bc166d5 12.2 Mb<br> ETASbranch_b0.50r0.70N0.99.Seq<br> md5:2ead803394a4c3b04f1e22e9b8c5ba46 872.9 Mb<br> ETASbranch_b0.50r0.75N0.50.Seq<br> md5:8c5cf4753836076c677a74d831354f33 12.2 Mb<br> ETASbranch_b0.50r0.75N0.99.Seq<br> md5:91ed931aedfb365761799e0882fcfb86 1 Gb<br> ETASbranch_b0.50r0.80N0.50.Seq<br> md5:f93e1779bc5344ffd17021bb387bb373 11 Mb<br> ETASbranch_b0.50r0.80N0.99.Seq<br> md5:6b0fd7fcad20ac4467fecda9502e9954 75.6 Mb<br> ETASbranch_b0.50r0.85N0.50.Seq<br> md5:a5debe21de70204a490c75dd7c68657a 11 Mb<br> ETASbranch_b0.50r0.85N0.99.Seq<br> md5:f9ed3ad6db12ad4a612c3945ccf72f18 48.5 Mb<br> ETASbranch_b0.50r0.90N0.50.Seq<br> md5:4493eef2170b68052cf56636c63091d7 9.7 Mb<br> ETASbranch_b0.50r0.90N0.99.Seq<br> md5:70043ed6c84e3896268c46f77eabefd3 26.7 Mb<br> ETASbranch_b0.50r0.95N0.50.Seq<br> md5:874916faa95c63312aa27aecea45de8f 7.5 Mb<br> ETASbranch_b0.50r0.95N0.99.Seq<br> md5:bbc54464c2e3cbac64401978216ae993 11.7 Mb<br> ETASbranch_b0.50r1.00N0.50.Seq<br> md5:3ff467b894f46a4be0b0747c686edad5 5.8 Mb<br> ETASbranch_b0.50r1.00N0.99.Seq<br> md5:d4d5416d56ed3de6eb2702a8eeb0a0b5 5.8 Mb</p> <p><br> 3.2. List of trees (53 files)<br> -----------------------------</p> <p><br> ETASbranch_b1.00r0.00N0.500TopoTrees.dat<br> md5:a5834b8d047fd3446e6d83488422bb5b 1.8 Gb<br> ETASbranch_b1.00r0.00N0.99TopoTrees.dat<br> md5:34eb899782444475151073ed6fcfc6aa 18.6 Mb<br> ETASbranch_b1.00r0.05N0.50TopoTrees.dat<br> md5:cc5f0edfc31a5f15394444243560e0ad 18.6 Mb<br> ETASbranch_b1.00r0.05N0.99TopoTrees.dat<br> md5:58cc7e7e746051f9efbe510c164a2b09 18.6 Mb<br> ETASbranch_b1.00r0.10N0.500TopoTrees.dat<br> md5:52cb3a9a5aeb1e222743bcdad3ea3b3d 1.8 Gb<br> ETASbranch_b1.00r0.10N0.50TopoTrees.dat<br> md5:a68a69280c0129ee3554d5c97dd0fa47 18.6 Mb<br> ETASbranch_b1.00r0.10N0.99TopoTrees.dat<br> md5:307edf1353af5141f11d89cbab6c4d30 18.6 Mb<br> ETASbranch_b1.00r0.15N0.500TopoTrees.dat<br> md5:52807004c32bdc1a999a2aaf1ff91bb9 1.8 Gb<br> ETASbranch_b1.00r0.15N0.50TopoTrees.dat<br> md5:0bdb45b13ed0fd5c0fa1619310d71670 18.6 Mb<br> ETASbranch_b1.00r0.15N0.99TopoTrees.dat<br> md5:f26e351184921ed9a85c8993404a4718 18.6 Mb<br> ETASbranch_b1.00r0.20N0.500TopoTrees.dat<br> md5:5fd930a98e28d551c9c204d22c6f564a 1.8 Gb<br> ETASbranch_b1.00r0.20N0.50TopoTrees.dat<br> md5:4aaeb952e0ecb7d4fc4bd2fb7822c729 18.6 Mb<br> ETASbranch_b1.00r0.20N0.99TopoTrees.dat<br> md5:a30687662b269f447d7e4cc020bd3773 18.6 Mb<br> ETASbranch_b1.00r0.25N0.500TopoTrees.dat<br> md5:4168e6e70b247977213d2278b94d65f3 1.8 Gb<br> ETASbranch_b1.00r0.25N0.50TopoTrees.dat<br> md5:b360877be00088b52676ba4717377761 18.6 Mb<br> ETASbranch_b1.00r0.25N0.99TopoTrees.dat<br> md5:34d11b64c78c852d4f50de7b1265c9b7 18.6 Mb<br> ETASbranch_b1.00r0.30N0.500TopoTrees.dat<br> md5:8c1a8b07c5f2350646d415a687f84492 1.8 Gb<br> ETASbranch_b1.00r0.30N0.50TopoTrees.dat<br> md5:0c1bfccd8cd141090a0bfb0cc7ae1ccd 18.6 Mb<br> ETASbranch_b1.00r0.30N0.99TopoTrees.dat<br> md5:652f975754241fed317acba066cf339b 18.6 Mb<br> ETASbranch_b1.00r0.35N0.500TopoTrees.dat<br> md5:6bc2a3f7b4f7fd8a92595271d2ea425f 1.8 Gb<br> ETASbranch_b1.00r0.35N0.50TopoTrees.dat<br> md5:d1d4e8a467d8d445cd3f6ca27c3c8af3 18.6 Mb<br> ETASbranch_b1.00r0.35N0.99TopoTrees.dat<br> md5:e56eee0119b193898ea729b75c572617 18.6 Mb<br> ETASbranch_b1.00r0.40N0.500TopoTrees.dat<br> md5:370a2f6c670fd7c9b515e43891417b73 1.8 Gb<br> ETASbranch_b1.00r0.40N0.50TopoTrees.dat<br> md5:9f50c23e33cca983df58e2b17f29f8e5 18.6 Mb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.dat<br> md5:d9971930b46838de09a7ccdc7cd9b459 18.6 Mb<br> ETASbranch_b1.00r0.45N0.500TopoTrees.dat<br> md5:cfef99746043d0718ad19e48ffdfeee4 1.8 Gb<br> ETASbranch_b1.00r0.45N0.50TopoTrees.dat<br> md5:44667f56360024e40b11be4f52ccf9c3 18.6 Mb<br> ETASbranch_b1.00r0.45N0.99TopoTrees.dat<br> md5:09b84c6f3c9dd58331c6f9ab4eec8f58 18.6 Mb<br> ETASbranch_b1.00r0.50N0.500TopoTrees.dat<br> md5:e9e880f24ffc9ba40db7c7f78d0f8d89 1.8 Gb<br> ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> md5:ce4835d532513bffd54723f285207898 1.9 Mb<br> ETASbranch_b1.00r0.50N0.99TopoTrees.dat<br> md5:4c935d76aee113c88f4a3348197f75f3 18.6 Mb<br> ETASbranch_b1.00r0.55N0.500TopoTrees.dat<br> md5:46cd9e8d508c15009e90a194a141008f 1.8 Gb<br> ETASbranch_b1.00r0.55N0.50TopoTrees.dat<br> md5:8e1cb9c364c2a275d10373b54dab6239 18.6 Mb<br> ETASbranch_b1.00r0.55N0.99TopoTrees.dat<br> md5:304b947c52e826a9b4cb68c38f007443 18.6 Mb<br> ETASbranch_b1.00r0.60N0.500TopoTrees.dat<br> md5:df8b7889e76694188b29f7fc16f6dce2 1.8 Gb<br> ETASbranch_b1.00r0.60N0.50TopoTrees.dat<br> md5:fc6c2110e139290ae9401765e8aae782 18.6 Mb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.dat<br> md5:6af466cc527b7422f1f958bfc6a87d15 18.6 Mb<br> ETASbranch_b1.00r0.65N0.500TopoTrees.dat<br> md5:129abd6de7821da465265a485217156d 1.8 Gb<br> ETASbranch_b1.00r0.65N0.50TopoTrees.dat<br> md5:469fc2ae3768fe9ae0ab0b8fbfaf3051 18.6 Mb<br> ETASbranch_b1.00r0.65N0.99TopoTrees.dat<br> md5:b7148c414b243b1911de8543d25e3d38 18.6 Mb<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> md5:3a3eed181ae6308c29f274d5e14a97df 1.8 Gb<br> ETASbranch_b1.00r0.70N0.50TopoTrees.dat<br> md5:ca7472f17916e7f8634a97c08ca96c58 18.6 Mb<br> ETASbranch_b1.00r0.70N0.99TopoTrees.dat<br> md5:8c2a4ab3800e8a8178be5d6e856f5b50 18.6 Mb<br> ETASbranch_b1.00r0.75N0.50TopoTrees.dat<br> md5:374796f34a3e25f711601161f8a34ae7 18.6 Mb<br> ETASbranch_b1.00r0.75N0.99TopoTrees.dat<br> md5:591f6ac31545f3e7558b4a5e151c80a5 18.6 Mb<br> ETASbranch_b1.00r0.80N0.50TopoTrees.dat<br> md5:473c6e9ac33cc6e8eabe0ed3c91b40b7 18.6 Mb<br> ETASbranch_b1.00r0.80N0.99TopoTrees.dat<br> md5:81f41fbce89c931f87890133b4c0f9f9 18.6 Mb<br> ETASbranch_b1.00r0.85N0.50TopoTrees.dat<br> md5:2c38db4208898d95c1c5b2ef0a94c930 18.6 Mb<br> ETASbranch_b1.00r0.85N0.99TopoTrees.dat<br> md5:4866f179b52ff5a6191d47e6d8ead5ce 18.6 Mb<br> ETASbranch_b1.00r0.90N0.50TopoTrees.dat<br> md5:f14a7f700c0c73743eb5747399abdcce 18.6 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.dat<br> md5:da4bd00aeefcbd1e67f0d7fe8fb1d8be 18.6 Mb<br> ETASbranch_b1.00r0.95N0.50TopoTrees.dat<br> md5:57b7f0bd901bb47ba3c253f801301716 18.6 Mb<br> ETASbranch_b1.00r0.95N0.99TopoTrees.dat<br> md5:6381354768603c6d2129c99df2a7798a 18.6 Mb</p> <p>4.3. Tree-size frequencies (18 files)<br> -------------------------------------</p> <p>ETASbranch_b1.00r0.00N0.99TopoTrees.FK<br> md5:5b6ccaf1c1218f101ed24c332bfefec3 612 Kb<br> ETASbranch_b1.00r0.10N0.30TopoTrees.FK<br> md5:f9f07bbd7be5377e1589101e85640149 468 B<br> ETASbranch_b1.00r0.20N0.30TopoTrees.FK<br> md5:6c7e40f8b93c7a3e62f2c105f0e7a89b 558 B<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> md5:158884c9bc4afa10c8ebb2e7b516a421 3.3 Mb<br> ETASbranch_b1.00r0.30N0.30TopoTrees.FK<br> md5:14d837b9763b9a54e64311e832e29f04 846 B<br> ETASbranch_b1.00r0.30N0.99TopoTrees.FK<br> md5:90b8b5a38a83cb4b4c833b605cf6b38e 2.1 Mb<br> ETASbranch_b1.00r0.40N0.30TopoTrees.FK<br> md5:1f198a7c08078066e7fa57ce9ebda8eb 3 Kb<br> ETASbranch_b1.00r0.40N0.99TopoTrees.FK<br> md5:229b837e7950fe85ea1c51be8e3f457e 3.3 Mb<br> ETASbranch_b1.00r0.50N0.30TopoTrees.FK<br> md5:6ead8bff09c3c2687c526648bfec6ab3 16 Kb<br> ETASbranch_b1.00r0.60N0.30TopoTrees.FK<br> md5:38c5cb9f544367ff9bdab766c4bcf03f 112 Kb<br> ETASbranch_b1.00r0.60N0.99TopoTrees.FK<br> md5:2591d4e2dd1c2051c0b7d96f423c526a 106.5 Mb<br> ETASbranch_b1.00r0.70N0.30TopoTrees.FK<br> md5:4f7741314e1a0d0b1c1733532d909277 7.5 Mb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 602 Kb<br> ETASbranch_b1.00r0.80N0.30TopoTrees.FK<br> md5:3c4240e79ee19c03afac91d631f38cb8 602 Kb<br> ETASbranch_b1.00r0.90N0.30TopoTrees.FK<br> md5:914a782fa2e73c0bc5a6e70bbb2afec9 1.3 Mb<br> ETASbranch_b1.00r0.90N0.99TopoTrees.FK<br> md5:cc165045d4d9f25c0f9c30c0ec71759f 864 Kb<br> ETASbranch_b1.00r0.99N0.30TopoTrees.FK<br> md5:a08cfc27a0e42d54f207ea05b7210714 187 Kb<br> ETASbranch_b1.00r0.99N0.99TopoTrees.FK<br> md5:a15681be56f870a95fe62794b11524df 365 Kb</p> <p><br> 4.4 Known missing/broken files<br> ------------------------------</p> <p>ETASbranch_b1.00r0.50N0.50TopoTrees.dat<br> ETASbranch_b1.00r0.10N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.20N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.50N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.70N0.99TopoTrees.FK<br> ETASbranch_b1.00r0.05N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.70N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.75N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.80N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.85N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.90N0.500TopoTrees.dat<br> ETASbranch_b1.00r0.95N0.500TopoTrees.dat<br> </p>
Standardized on-road tests assessing fitness-to-drive in people with cognitive impairments: a systematic review
<p>Data repository for a systematic review concerning on-road tests. Are included the following data :</p> <ul> <li>items and scoring system</li> <li>risk of bias assessment</li> <li>summary of findings</li> <li>the complete search strategy</li> </ul>
Towards a standard procedure for the measurement of the multi-photon component in a CW telecom heralded single-photon source
<p>Single-photon sources are set to be a fundamental tool for metrological applications as well as for quantum information related technologies. Because of their upcoming widespread dissemination, the need for their characterization and standardization is becoming of the utmost importance. Here, we illustrate a strategy to provide a quantitative estimate of the multi-photon component of a single-photon source, showing the results achieved in a pilot study for the measurement of the second-order autocorrelation function <em>g</em><sup>(2)</sup>(0) of a low-noise CW heralded single photon source prototype (operating at telecom wavelength nm) realized in INRiM. The results of this pilot study, involving INRiM, NPL and PTB, will help to build a robust and unambiguous procedure for the characterization of the emission of a single-photon source.</p>
soilmap_simple: a simplified and standardized derivative of the digital soil map of the Flemish Region
<p>The data source <code>soilmap_simple</code> is a simplified and standardized derived form of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a>' (the shapefile of which we named <code>soilmap</code>, for analytical workflows in R) published by 'Databank Ondergrond Vlaanderen’ (DOV). It is a GeoPackage that contains a <strong>spatial polygon layer</strong> ‘<code>soilmap_simple</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>), plus a <strong>non-spatial table</strong> ‘<code>explanations</code>’ with the meaning of category codes that occur in the spatial layer. Further documentation about the digital soil map of the Flemish Region is available in Van Ranst & Sys (2000) and Dudal et al. (2005).</p> <p>This version of <code>soilmap_simple</code> was derived from version '<code>soilmap_2017-06-20</code>' (<a href="https://doi.org/10.5281/zenodo.3387008">Zenodo DOI</a>) as follows:</p> <ul> <li>all attribute variables received English names (purpose of standardization), starting with prefix <code>bsm_</code> (referring to the 'Belgian soil map');</li> <li>attribute variables were reordered;</li> <li>the values of the morphogenetic substrate, texture and drainage variables (<code>bsm_mo_substr</code>, <code>bsm_mo_tex</code> and <code>bsm_mo_drain</code> + their <code>_explan</code> counterparts) were filled for most features in the 'coastal plain' area. <ul> <li>To derive morphogenetic texture and drainage levels from the geomorphological soil types, a conversion table by Bruno De Vos & Carole Ampe was applied (for earlier work on this, see Ampe 2013).</li> <li>Substrate classes were copied over from <code>bsm_ge_substr</code> into <code>bsm_mo_substr</code> (<code>bsm_ge_substr</code> already followed the categories of <code>bsm_mo_substr</code>).</li> </ul> These steps coincide with the approach that had been taken to construct the <code>Unitype</code> variable in the <code>soilmap</code> data source;</li> <li>only a minimal number of variables were selected: those that are most useful for analytical work.</li> </ul> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/b3c6696/src/generate_soilmap_simple">'n2khab-preprocessing' at commit b3c6696</a> for the creation from the <code>soilmap</code> data source.</p> <p>A reading function to return <code>soilmap_simple</code> (this data source) or <code>soilmap</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p><strong>The attributes</strong> of the spatial polygon layer <code>soilmap_simple</code> can have <code>mo_</code> in their name to refer to the <em>Belgian Morphogenetic System</em>:</p> <ul> <li><code>bsm_poly_id</code>: unique polygon ID (numeric)</li> <li><code>bsm_region</code>: name of the region</li> <li><code>bsm_converted</code>: boolean. Were morphogenetic texture and drainage variables (<code>bsm_mo_tex</code> and <code>bsm_mo_drain</code>) derived from a conversion table (see above)? Value <code>TRUE</code> is largely confined to the 'coastal plain' areas.</li> <li><code>bsm_mo_soilunitype</code>: code of the soil type (applying morphogenetic codes within the coastal plain areas when possible, just as for the following three variables)</li> <li><code>bsm_mo_substr</code>: code of the soil substrate</li> <li><code>bsm_mo_tex</code>: code of the soil texture category</li> <li><code>bsm_mo_drain</code>: code of the soil drainage category</li> <li><code>bsm_mo_prof</code>: code of the soil profile category</li> <li><code>bsm_mo_parentmat</code>: code of a variant regarding the parent material</li> <li><code>bsm_mo_profvar</code>: code of a variant regarding the soil profile</li> </ul> <p>The <strong>non-spatial table</strong> <code>explanations</code> has following variables:</p> <ul> <li><code>subject</code>: attribute name of the spatial layer: either <code>bsm_mo_substr</code>, <code>bsm_mo_tex</code>, <code>bsm_mo_drain</code>, <code>bsm_mo_prof</code>, <code>bsm_mo_parentmat</code> or <code>bsm_mo_profvar</code></li> <li><code>code</code>: category code that occurs as value for the corresponding attribute in the spatial layer</li> <li><code>name</code>: explanation of the value of <code>code</code></li> </ul>
Fig. 3.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.2. Camera positions of a single rotation when taking pictures of an object.
Fig. 4.1 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.1. Scorpion pictured in UV fluorescence. Focus stacking image.
Fig. 2.14 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 2.14. Picture of a quickly composed image of a mite at 20× magnification.
Data from: NOVOWrap: an automated solution for plastid genome assembly and structure standardization
<p>Plastid genomes play an important role in genomics and evolutionary biology. Next-generation sequencing has revolutionized plastid genomic data acquisition to the point that genome assembly has become a bottlenecks for widespread utilization of plastid genome data. To solve this problem, we developed an open-source, cross-platform tool known as, NOVOWrap, which includes both command-line and graphical interfaces for automatically assembling plastid genomes on personal computers. With minimal inputs, settings, and user intervention, NOVOWrap can automatically assemble plastid genomes, validate results and standardize the structure using affordable computer resources. The performance of this software has been successfully benchmarked against the plastid genomes of 11 species belonging to lycopods, gymnosperms, and angiosperms. This program is expected to liberate researchers from laborious and cumbersome computer manipulations and create reliable and standardized genomic data.</p>
Silvicultural and economic dataset for even-aged and coppice-with-standards managements in France.
<p>Data set from DEFIFORBOIS project "Development and sustainability of the forest-wood sector in the Centre region", PSDR 4 project Centre-Val de Loire Region, France. This study has been carried out also with financial support from the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45) through the Project LUCAS.</p>
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.