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4,480 results for “initiative”
Cover and rank percentile of initially dominant species in global Nutrient Network plots from 2007-2023
This dataset uses data from the NutNet dataset to examine the factors controlling how initially dominant species decay in dominance through time. We use data from all sites that had NPK and/or fencing treatments and pre-treatment when data was downloaded in 2021; 90 sites in all were used. Perturbations were NPK treatments (nitrogen, phosphorus, potassium and micronutrients) and fencing (vertebrate herbivore exclusion). Dominance was quantified as the rank percentile of a species in each year. Rank percentiles range from 1 (most abundant) to 0 (absent), with decimal values indicating relative rank - e.g. a species with rank 0.6 is more abundant than 60% of co-occurring species. Thus, only species with a rank of 1 in year o (pre-treatment) are included in this dataset. Covariates for examining rates of dominance decay include plot level initial and yearly cover values, both absolute and relativized; species provenance, lifespan, and functional group; site level climate variables and site richness.
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
Initial Tree Diameter data taken in various studies, 1968-1972
Initial Tree Diameter data taken in various studies, 1968-1972. Diameters were taken at the start of the growing season for the setup year. Tree growth data files show the first years growth and the resulting dbh following the specified growing season year.
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
PROTECT project RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_5_2_20191113_180029.decod: collected in Rome (Italy) with the DUNE foot-mounted sensor unit n. 5 on November 13, 2019, in the frame of the project WP3 activities (system scale-up design); aprox. Duration, 30 mins.</li> <li>RawData_6_1_20191112_170005.decod; collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 6 on November 12, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins.</li> <li>RawData_008_01_20191130_162759.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on November 30, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_02_20191201_125122.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_03_20191201_153053.decod: collected in Beijing (China) with the DUNE foot-mounted sensor unit n. 8 on December 1<sup>st</sup>, 2019, in the frame of the WP4 activities (demonstration), aprox. Duration, 30 mins.</li> <li>RawData_008_06_20191120_182949.decod: collected in Roma (Italy)with the DUNE foot-mounted sensor unit n. 8 on November 20, 2019, in the frame of the WP3 activities (system scale-up design), aprox. Duration, 30 mins. The experiment has a mix of walk and fast run.</li> </ul> <p> </p> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p> </p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all dataset have been recorded with a 200 Hz sampling frequency.</p>
Initial conditions to reproduce figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system"
<p>The directory includes the initial conditions of the simulations needed to reproduce the figures of the article "On the impact of tides on the transit-timing fits to the TRAPPIST-1 system". </p> <p>The simulations were done with Posidonius version v2019.07.30. The information to download and use Posidonius can be found here:<br><a href="https://www.google.com/url?q=https://www.blancocuaresma.com/s/posidonius&sa=D&source=hangouts&ust=1580820901945000&usg=AFQjCNEmxwuCiIgh9d-aTfx-bqB0_u_9BA">https://www.blancocuaresma.com/s/posidonius</a>. <br>That version was only slightly modified to use a maximum timestep size for the IAS15 algorithm. </p> <p>The directory includes:</p> <ul> <li>a README giving all the information defining the test directories</li> <li>test directories with a file called trappist1.json, which can be used to launch Posidonius:<br>posidonius start --silent trappist1.json data_dir/trappist1.bin data_dir/trappist1_history.bin</li> </ul>
Fig. 2 in Changes in digestive enzymes activities during the initial ontogeny of wolf cichlid, Parachromis dovii (Perciformes: Cichlidae)
Fig. 2. Digestive proteolytic enzyme activity during ontogeny of Parachromis dovii larvae (means ± SD, n= 3 replicates). (a) specific acid proteolytic activity, (b) specific alkaline proteolytic activity, (c) specific trypsin activity, (d) specific chymo- trypsin activity, (e) specific leucine-aminopeptidase activity, (f) specific carboxypeptidase A activity.
CICE gx1 Grid and Initial Condition Data - 2020.03.20
<p>This file contains the gx1 grid and initial condition data for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
CICE gx3 Grid and Initial Condition Data - 2020.03.20
<p>This file contains the gx3 grid and initial condition files for CICE. </p> <p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p> <p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p>
Model Collection of the Business Process Management Academic Initiative
<p>This model collection contains 29,810 models. The collection originates from the BPM Academic Initiative (BPMAI). In 2012, the initial members of this initiative were the following institutions represented by the corresponding professors: Mathias Weske (HPI, University of Potsdam), Marlon Dumas (University of Tartu), Marcello La Rosa (University of Melbourne), Jan Mendling (WU Vienna), Hajo A. Reijers (Utrecht University), Michael Rosemann (Queensland University of Technology, Australia), Jan Recker (University of Cologne, Germany), Wil van der Aalst (RWTH Aachen, Germany), Michael zur Mühlen (Stevens Institute of Technology, Hoboken, NJ) and Frank Leymann (University of Stuttgart), respectively, complemented by Dr. Gero Decker (Signavio GmbH, Berlin). The vendor Signavio provides a <a href="http://www.signavio.com/en/academic.html">free workspace</a> to the members of the BPM Academic Initiative and the models being created are made available for reseach purposes on the Creative Commons licence.</p> <p>The BPM Academic Initiative collection comprises tens of thousands of models, of various process modeling languages, and size. Models of the collection are available in several revisions, which opens a new perspective in researching the way people model.<br> The BPMAI collection is shared in a JSON format. Tools have been developed at HPI, University of Potsdam for efficiently processing these files. See the <a href="https://github.com/tobiashoppe/promnicat">PromniCat project</a>. Note that this project is no longer further developed, so you have to work with the information available there. Various analysis techniques can be applied for these models using the <a href="https://code.google.com/archive/p/jbpt/">jBPT library</a> or <a href="https://code.google.com/archive/p/apromore/">Apromore</a>.</p>
Transcription initiation peaks based on FANTOM5 CAGE data on rn6, canFam3, and galGal5
<p><strong>Overview</strong></p> <p>Decomposition-based peak identification (DPI, https://github.com/hkawaji/dpi1) is applied to the FANTOM5 data of rat (rn3), dog (canFam3), and chicken (galGal5):</p> <ul> <li>https://fantom.gsc.riken.jp/5/datafiles/phase2.6/basic/</li> </ul> <p>The same parameters to the ones used in the previous paper (Forrest ARR, Kawaji H, Rehli M, et al. Nature 507: 462–470, 2014) was used.</p> <p><strong>Data files</strong></p> <p>Four data files per assembly are prepared as below.</p> <ol> <li>tag cluster in the original definition (*.tc.bed.gz)</li> <li>full set of DPI peaks (*.tc.decompose_smoothing_merged.bed.gz)</li> <li>permissive set of DPI peaks (*.tc.decompose_smoothing_merged.ctssMaxCounts3.bed.gz)</li> <li>robust set of DPI peaks (*.tc.decompose_smoothing_merged.ctssMaxCounts11_ctssMaxTpm1.bed.gz)</li> </ol> <p><strong>Acknowledgement</strong></p> <p>This data set is supported by Research Grant from MEXT to RIKEN Preventive Medicine and Diagnosis Innovation Program, RIKEN Center for Integrative Medical Sciences, and JSPS KAKENHI Grant-in-Aid for Scientific Research No. 16H02902.</p>
Subduction zone initiation (SZI) Database
<p>The <strong>Subduction zone initiation (SZI) Database</strong> is a <em>cross-disciplinary</em> and <em>community-driven</em> approach to gain an improved understanding of subduction zone initiation (SZI) and overcome the key long-standing questions of the Earth Sciences of how, when and where it happens on the Earth.</p> <p>The interdisciplinary database features more than a dozen documented SZI events that occurred during the last hundred million years. The SZI Database and its related online platform, <a href="http://www.szidatabase.org"><strong>www.szidatabase.</strong></a><strong><a href="http://www.szidatabase.org">org</a>,</strong> is an easily-accessible, fully transparent, expandable platform that contains relevant SZI data and analyses, and establishes a common language to sharpen discussion across the Earth Science community.</p> <p>Further details and the first novel scientific insights gained based on the database are presented in <a href="https://rdcu.be/b5ROu">Crameri et al. (2020, Nature Communications)</a>.</p>
Initial Investors of the BB1 token
<p>In 2019 the Berlin based crypto company <a href="https://www.bitbondsto.com/">bitbond</a> issued the security token BB1 (ITIN: 8WR5-AKBG-X) in Germany’s first, completely regulated STO (security token offering). The BB1 token is structured like a bond of the issuer Bitbond Finance GmbH, with a fixed coupon of 4.00% p.a. (frequency: quarterly), and a floating coupon of 60% of the pre-tax profit of the issuer (frequency: annually).</p> <p>All BB1 related transactions can be observed at the <a href="https://stellar.expert/explorer/public/asset/BB1-GD5J6HLF5666X4AZLTFTXLY46J5SW7EXRKBLEYPJP33S33MXZGV6CWFN">Stellar</a> blockchain. We used this transpareny to extract a list of all investors who initially bought the token. We found 1021 initial investments in total, with a total investment volume of around 2,5 mio BB1 token. The list contains all investments, which have been claimed between July 2019 and June 2020.</p> <p>For each investment we extracted the following data:</p> <ul> <li><strong>Date. </strong>This is the date, at which the investor claimed the purchased tokens.</li> <li><strong>Recipient.</strong> Is the ID of the investros account in the Stellar network. All details about this account can be retrieved by: https://stellar.expert/explorer/public/account/[ID]</li> <li><strong>Symbol. </strong>The symbol of the BB1 token.</li> <li><strong>Volume.</strong> The number of BB1 tokens the investor has intially purchased.</li> </ul> <p>If you have any questions regarding this dataset please contact Lutz Maicher (lutz.maicher@uni-jena.de).</p> <p>There is already a piece at Medium where this data is used: <a href="https://medium.com/@Lutz.Maicher/how-versatile-is-the-crowd-of-sto-investors-the-case-of-the-bb1-token-53a3095925c8">"How versatile is the crowd of STO investors — the case of the BB1 token"</a></p>
Initial Coin Offering database
<p>The dataset contains the raw data for the scientific paper "ICO Investors", forthcoming in Financial Markets and Portfolio Management. In an initial coin offering (ICO, sometimes also called token generation event or TGE), an entrepreneur raises capital by selling a newly-minted cryptocurrency or “utility-token” to the public. The dataset contains detailed data for about 300 successfully completed ICOs that took place before March 2018 and raised at least 1,000,000 CHF. The database has detailed information on the ICO itself (e.g., money raised, existence of soft cap and/or hard cap, size of presale and crowdsale, magnitude of discounts for early investors, time until funding goals reached, legal form of the entity conducting the ICO), on regulatory procedures (e.g., know your customer and anti-money laundering rules respected, restrictions on sales to certain countries, token structured as simple agreement for future tokens (SAFT)) as well as on the entrepreneurs and products offered (e.g., background, country of incorporation, prototype of product / code available). </p>
Gait Initiation with additional load
<p>Dataset containing raw data from gait initiation with additional load protocol using two force platforms.</p> <p>Each file has force, moments and center of pressure data.</p> <p>Additional load was additioned to waist and shoulder, uniformly and laterally on the stance foot side and swing foot side.</p> <p>The control condition was no load.</p> <p>Each participant performed each condition three times.</p>
Spontaneous initiation of debris flow surges from sedimentary deposits
<p>Data used in figures of Research paper manuscript 'Spontaneous initiation of debris flow surges from sedimentary deposits', which has been contribution to <a href="http://www.letpub.com.cn/index.php?page=journalapp&view=detail&journalid=10219">JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE</a> journals .</p>
Fig. 3 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids
Fig. 3. Mean of flight initiation distances for the three labrid species studied in MUAs (Multiple-use areas) and NTAs (No-take areas) in Abrolhos. The upper limits of lines indicate standard deviation; *symbols indicate significant differences according to ANCOVA; **(p <0.001).
Fig. 4 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids
Fig. 4. Relationship between flight initiation distances (FID), group size and body size (continuous covariates) inside NTAs and MUAs (categorical factor). Black dots represent samples from no-take areas (NTAs); white dots represent samples from multiple-use areas (MUAs). The continuous line represents the best fit for MUAs data and the dotted line that for NTAs data.
Fig. 1 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids
Fig. 1. Map of study area showing sampled sites (MUAs: Multiple-use areas; NTAs: No-take areas) in the Abrolhos bank.
Fig. 5 in Are Abrolhos no-take area sites of naïve fish? An evaluation using flight initiation distance of labrids
Fig. 5. Average group size (a) and body size (b) estimated for the three labrids studied in both no-takes (NTAs) and multiple-use areas (MUAs) sampled in the Abrolhos Bank. The upper limits of lines indicate standard error.
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.