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1,310 results for “construction”

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zenodo44/100

Estimative constructions in crosslinguistic perspective

CLDF version of Guillaume Jacques' dataset 'Estimative constructions in crosslinguistic perspective'

opencc-by-4.0Aug 2024View details →

zenodo44/100

Miocene construction of the High Andes recorded by exhumation of the Frontal Cordillera, La Ramada Massif of western Argentina (32°S) (Supporting Information)

Supporting datasets for Howlett et al., "Miocene construction of the High Andes recorded by exhumation of the Frontal Cordillera, La Ramada Massif of western Argentina (32°S)" in TECTONICS.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Dataset for a typological study of adpossessive constructions

This material contains the dataset and the scripts from the <a href="https://version.helsinki.fi/gramadapt/udw2020-adpossessive-constructions">gitlab repository</a> of the following article. Please cite the article when using the data. Sinnemäki, Kaius & Viljami Haakana 2020. Variation in Universal Dependencies annotation: A token-based typological case study on adpossessive constructions. In Marie-Catherine de Marneffe, Miryam de Lhoneux, Joakim Nivre & Sebastian Schuster (eds.), Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020), 158–167. Barcelona (online): The Association for Computational Linguistics. Available at <a href="https://www.aclweb.org/anthology/2020.udw-1.0">https://www.aclweb.org/anthology/2020.udw-1.0</a>.

opencc-by-4.0Jun 2021View details →

zenodo44/100

Graph construction method impacts variation representation and analyses in a bovine super-pangenome

Pangenomes for minigraph, pggb, and cactus containing assemblies from <ul> <li>Hereford (cattle reference genome)</li> <li>Highland</li> <li>Brown Swiss</li> <li>Angus</li> <li>Simmental</li> <li>Original Braunvieh</li> <li>Piedmontese</li> <li>Nellore</li> <li>Brahman</li> <li>Yak</li> <li>Bison</li> <li>Gaur</li> </ul>   Also contains the genomic region classifications for the ARS-UCD1.2 reference genome for <ul> <li>Satellites</li> <li>Tandem repeats</li> <li>Low mappability</li> <li>Repetitive regions</li> <li>Normal (everything else)</li> </ul>

opencc-by-4.0Mar 2023View details →

zenodo44/100

WikiCausal Corpus for Evaluation of Causal Knowledge Graph Construction

Documentation on the data format and how it can be used can be found on: <a href="https://github.com/IBM/wikicausal">https://github.com/IBM/wikicausal</a> as well as our paper: <pre><code>@unpublished{, author = {Oktie Hassanzadeh and Mark Feblowitz}, title = {{WikiCausal}: Corpus and Evaluation Framework for Causal Knowledge Graph Construction}, year = {2023}, doi = {10.5281/zenodo.7897996} }</code></pre> <pre>Corpus derived from Wikipedia and Wikidata. Refer to Wikipedia and Wikidata <a href="https://en.wikipedia.org/wiki/Wikipedia:Copyrights">license and terms of use</a> for more details:</pre> <ul> <li>Permission is granted to copy, distribute and/or modify Wikipedia's text under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License and, unless otherwise noted, the GNU Free Documentation License, unversioned, with no invariant sections, front-cover texts, or back-cover texts.</li> <li>A copy of the Creative Commons Attribution-ShareAlike 3.0 Unported License is included in the section entitled "<a href="https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License">Wikipedia:Text of Creative Commons Attribution-ShareAlike 3.0 Unported License</a>"</li> <li>A copy of the GNU Free Documentation License is included in the section entitled "<a href="https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License">GNU Free Documentation License</a>".</li> <li>Content on Wikipedia is covered by <a href="https://en.wikipedia.org/wiki/Wikipedia:General_disclaimer">disclaimers</a>.</li> </ul> <pre>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</pre>

opencc-by-3.0May 2023View details →

edi44/100

Road construction history (1952 - 1990), Andrews Experimental Forest

Andrews road construction history is mapped. This data includes the year and 1/2 decade of construction for the road network around the HJ Andrews Experimental Forest. Note that the underlying road network has been revised and updated based on the 2007 LiDAR flight. This road network can be found at GI007: Transportation network locations.

openCustomJul 2005View details →

edi44/100

Flow dynamics and pump kinematics in polychaete burrows constructed in a transparent mud analog

We used Particle Tracking Velocimetry (PTV) to measure fluid flow within burrows constructed by the polychaete Alitta succinea in a transparent mud analog. We also measured the kinematics of the undulatory pumping by the polychaete that drives flow through the burrow. The flow velocity data is presented in the spreadsheet worm_burrow_particle_tracking_data.csv and consists of the x and y coordinates (in mm) of each tracked particle, the time at which it was tracked (in seconds) and the velocity of the particle at that time (in mm per second). The ClipID is the reference of the video clip the data is from, and is a unique identifier. The SequenceID is retained between the pump dynamics data and the particle tracking data, because worm kinematics and flow dynamics were recorded simultaneously. Each tracked particle in a given sequence has a unique TrackID. The worm kinematics data consists of the track of the peak of the undulatory wave created as an individual polychaete ventilates its burrow and is presented in the spreadsheet worm_burrow_pump_dynamics_data.csv. The variables included are the x and y coordinates of the wave peak (in mm), the time at which the point was taken (in seconds) and the instantaneous velocity of the wave peak at that time (in mm per second). The ClipID is the reference of the video clip the data is from, and is a unique identifier. The SequenceID is retained between the pump dynamics data and the particle tracking data, because worm kinematics and flow dynamics were recorded simultaneously. Each tracked wave in a given sequence has a unique TrackID. The metadata, in the spreadsheet worm_burrow_metadata.csv, gives the polychaete Individual ID (a unique identifier for each specimen used) for each Clip ID and Sequence ID from the data spreadsheets, the location in the burrow at which the video was taken (between the head of the worm and the burrow entrance is "ahead", between the tail of the worm and the burrow exit is "behind", and a video of

openCustomMay 2024View details →

zenodo40/100

Fig. 5 in Age and growth of the Amazonian migratory catfish Brachyplatystoma rousseauxii in the Madeira River basin before the construction of dams

Fig. 5. Mean monthly relative marginal increment (RMI ± S.D.) of 357 Brachyplatystoma rousseauxii's otoliths in relation to the hydrological cycle in the Madeira River basin. The values above bars indicate the number of otoliths analysed each month.

opencc-by-4.0Mar 2018View details →

zenodo40/100

Fig. 4a in Age and growth of the Amazonian migratory catfish Brachyplatystoma rousseauxii in the Madeira River basin before the construction of dams

Fig. 4a. Different types of growth rings; and b. their relative proportions, in transverse thin sections of Brachyplatystoma rousseauxii from the Madeira River basin. S-single, D-double, T-triple rings.

opencc-by-4.0Mar 2018View details →

zenodo40/100

Notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011

This dataset was extracted from NANDO, the website of the European Commission for notified bodies. It contains the list of notified bodies, horizontal specifications and harmonised standards for construction materials third party assessment according to the CPR - EU 305/2011

opencc-by-4.0Apr 2020View details →

zenodo40/100

Fig. 2. Phylogenetic tree constructed with 57 in A review of Bennelongia De Deckker & McKenzie, 1981 (Crustacea, Ostracoda) species from eastern Australia with the description of three new species

Fig. 2. Phylogenetic tree constructed with 57 novel COI sequences of Bennelongia, 26 published Bennelongia sequences and one Heterocypris spec. as outgroup (sequence names are given in brackets at the end of species names). This tree represents two trees of identical topology inferred by ML and BI. Bootstrap values (for 1000 bootstrap replicates) from ML analyses and Bayesian posterior probabilities (ranging from 0 to 1) are shown for each node (in the format: 'Bootstrap Support/Posterior Probability'). Branch lengths are proportional to the genetic distance scale at the bottom left. Clades with published sequences have been collapsed; the number of sequences in these clades is included in brackets after the species name. Nodes with less than 50% bootstrap support and a posterior probability of less than 0.5 have been collapsed. The tree shows six strongly supported clades that correspond to the species presented in this study.

opencc-by-3.0Oct 2012View details →

zenodo40/100

Figure 2 in Bornean caterpillar (Lepidoptera) constructs cocoon from Vatica rassak (Dipterocarpaceae) resin containing multiple deterrent compounds

Figure 2. Pieces of resin taken from the cocoon and imaged (A) using photomontage; and (B– D) environmental electron microscopy. Images (B–D) show the elaborate shearing patterns within the resin. The centre of image (D) shows what may be a score mark in the surface of the resin made by the caterpillar.

opencc-by-4.0Aug 2014View details →

zenodo40/100

Photochromic fluorophores enable imaging of endogenous fusion constructs in Candida albicans

10. Gcn5_Stat : Main dataset for figure 2 stationary phase of the manuscript. 5., 6. and 7.Gcn5_Stat are additional datasets for figure 2 stationary phase. 11. SC5314_Stat : main negative control dataset for figure 2 depicted in supplementary figure 1. 14. Gcn5_Exp : Main dataset for figure 2 exponential phase of the manuscript. 12., 13. Gcn5_Exp are additional datasets for figure 2 exponential phase. 15. SC5314_Exp : main negative control dataset for figure 2 depicted in supplementary figure 1. ___________________________________________________________________________________________________ 2. Erg11 and 7. Erg11 are main datasets for figure 3 of the manuscript. 15. SC5314 (Erg11 control) : negative control dataset for figure 3 depicted in supplementary figure 3.

opencc-by-4.0Nov 2020View details →

zenodo40/100

Design of perfused PTFE vessel-like constructs for in vitro applications

The design of a vessel-like construct for further use in perfused tissue models using PTFE membranes and collagen is presented. Endothelial cells were able to adhere to the tube’s wall, resisted perfusion, and formed an endothelial barrier delaying diffusion of fluorescently labeled molecules.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Experimental Data on Pragmatic, Constructive and Reconstructive Memory Influences on the Hindsight Bias

After knowing how events turned out, we are quick to say ‘we knew it all along.’ Decades of research on hindsight bias have shown that outcome information biases what we later present as our original judgments. This experiment combined established between- and within-participant designs in a longitudinal study.

opencc-by-4.0Jan 2021View details →

zenodo40/100

A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox

Data set used in "A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox"

opengpl-2.0Dec 2014View details →

zenodo40/100

Model, data, and analysis for Negative Niche Construction Favors the Evolution of Cooperation

This repository contains the model, data, and analysis corresponding to Negative Niche Construction Favors the Evolution of Cooperation as submitted for review by Brian D. Connelly, Katherine J. Dickinson, Sarah P. Hammarlund, and Benjamin Kerr. Contents are released to the public domain under the Creative Commons CC0 License.

opencc-zeroApr 2015View details →

zenodo40/100

Bedesten, Nicosia, Cyprus. Plan of the building and its phases of construction, 2010.

Bedesten, Nicosia, Cyprus. Plan of the building and its phases of construction. As available on the site, 2010 and published in 2020, see Related Identifiers.

opencc-by-4.0Mar 2017View details →

zenodo40/100

TCOM-CH4: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset [1991-2021] constructed using machine-learning

Methodology:   he TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>CH4 Profile Processing and Bias Correction</h3> Collocated CH4 profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). It is important to note that unlike previous versions that might have used both HALOE and ACE measurements, this version exclusively utilizes ACE-FTS data, which is why the dataset starts from 2000. Separate XGBoost regression models are then trained for these CH4 differences at each height level within a given latitude bin. These trained models are subsequently used to estimate CH4 bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved CH4 profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean CH4 profiles: <ul> <li> <code>zmch4_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmch4_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0Nov 2022View details →

zenodo40/100

TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>N2O Profile Processing and Bias Correction</h3> Collocated N2O profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these N2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate N2O bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved N2O profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean N2O profiles: <ul> <li> <code>zmn2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmn2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0Nov 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record