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574 results for “anchoring”
Anchor sampling of inorganic carbon (DIC) and dissolved oxygen (DO) at the flux tower tidal creek in the Duplin River
An anchor sampling of DIC and DO level at the mouth of a first order creek of the Duplin was conducted seasonally in order to estimate the metabolism and CO2/O2 dynamics of water when it floods the marsh where the GCE LTER project operates an eddy covariance tower.
MINIATURA 6 Housing decisions, behavioral aspects of choices, price expectations and anchoring effect - Polsh case study
<p>The data was created as a result of a survey conducted in accordance with the guidelines: - the survey questionnaire consisted of approximately 30 questions and a form, - the surveyed population was defined as 1,000 households living in a large Polish city (over 450,000 inhabitants), quota selection based on the number of city inhabitants, - CAWI method (online), - completion date: 1 week. The survey was parameterized. Part of the sample is a control trial, part is an experimental trial.</p><p>Dane powstały w wyniku przeprowadzonej ankiety zgodnie z wytycznymi: - kwestionariusz badania składał się z ok. 30 pytań oraz metryczki, - badana zbiorowość określono na 1000 gospodarstw domowych zamieszkałych w dużym mieście Polski (powyżej 450 tys. ludności), dobór kwotowy na podstawie liczby mieszkańców miast, - badanie metodą CAWI (on-line), - termin realizacji 1 tydzień. Ankieta byłą sparametryzowana. Część próby stanowi próba kontrolna, część próba eksperymentalna. </p>
Webis-MS-MARCO-Anchor-Texts-22
<p>The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of <a href="https://microsoft.github.io/msmarco/">MS MARCO</a> with anchor text extracted from six <a href="https://commoncrawl.org/">Common Crawl</a> snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that all anchor text is included for 94% of the documents in Version 1 and 97% of documents for Version 2). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with anchor text.</p> <p>Cleaned versions of the MS MARCO Anchor Text 2022 dataset are available in <a href="https://github.com/allenai/ir_datasets/issues/154">ir_datasets</a>, <a href="https://zenodo.org/record/5883456">Zenodo</a> and <a href="https://huggingface.co/datasets/webis/ms-marco-anchor-text">Hugging Face</a>. The raw dataset with additional information and all metadata for the extracted anchor texts (roughly 100GB) is available on <a href="https://huggingface.co/datasets/webis/ms-marco-anchor-text/tree/main/ms-marco-v1/anchor-text">Hugging Face</a> and <a href="https://files.webis.de/data-in-progress/ecir22-anchor-text/anchor-text-samples/">files.webis.de</a>.</p> <p>The details of the construction of the Webis MS MARCO Anchor Text 2022 dataset are described in the <a href="https://webis.de/publications.html#froebe_2022a">associated paper</a>. If you use this dataset, please cite<br> <code>@InProceedings{froebe:2022a,<br> address = {Berlin Heidelberg New York},<br> author = {Maik Fr{\"o}be and Sebastian G{\"u}nther and Maximilian Probst and Martin Potthast and Matthias Hagen},<br> booktitle = {Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022)},<br> editor = {Matthias Hagen and Suzan Verberne and Craig Macdonald and Christin Seifert and Krisztian Balog and Kjetil N{\o}rv\r{a}g and Vinay Setty},<br> month = apr,<br> publisher = {Springer},<br> series = {Lecture Notes in Computer Science},<br> site = {Stavanger, Norway},<br> title = {{The Power of Anchor Text in the Neural Retrieval Era}},<br> year = 2022<br> }</code></p>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</p>
VRTogether Pilot 2 Howard Anchor video
<p>Video of the news anchor in stereo format.</p> <p>Codec: H264 - MPEG-4 AVC</p> <p>Dimensions: 1536x768</p> <p>Framerate: 30 fps</p> <p>Bitrate: 3579kbps</p> <p>Audio: Stereo 48000 kHz</p>
ARLCL: Anchor-free Ranging-Likelihood-based Cooperative Localization
<p>This *dataset (68440 files of approx. 30GB unzipped) includes all Bluetooth Low Energy (BLE) Received Signal Strength (RSS) samples used for the evaluation of the Anchor-free Ranging-Likelihood-based Cooperative Localization (ARLCL) method. Each DB file corresponds to an evaluated scenario of a unique combination of nodes/samples (i.e. reflecting different swarm deployments and measurement qualities). The maximal setting is 21 Raspberry Pis and 20 RSS measurement samples. Each DB file contains the true positions of the participating nodes, 100 resampling cases, and one exceptional case (#RSS_0#) where all samples have been used (this case has not been considered in the paper).<br> <br> The specific structure of the DB files is required by our open-sourced Cooperative Localization Optimizer (<a href="https://github.com/CDS-Bern/ARLCL-Optimizer"><em><strong>ARLCL-Optimizer</strong></em></a>), and also the <strong>Mass Spring</strong> and <strong>Maximum Likelihood - Particle Swarm Optimization</strong> implementations that we used in the paper. These are also provided openly in our repo (<a href="https://github.com/CDS-Bern/ARLCL-Optimizer">https://github.com/CDS-Bern/ARLCL-Optimizer</a>).<br> <br> We encourage future cooperative localization solutions to use our provided dataset/software for comparisons or results reproduction.</p> <p>*The files have been compressed using <a href="https://www.7-zip.org/">7-zip</a>.</p>
Anchoring pan-scalar map Agile Dataset
<p>This dataset contains data used for the design of anchored pan-scalar maps in Madison and Chambery. Anchoring pan-scalar maps means improving the saliency and memorability of some cartographic elements, i.e. the pan-scalar anchors, to enhance the navigational cues that can be used for self-localization during or after a zoom in a map.</p> <ul> <li>Madison :</li> </ul> <ol> <li>VECTORSQUELETON : This folder contains the digitized data used to create map skeletons at different zoom levels in Madison.(12,13,14,15,16)</li> <li>ANCHOR TRAINLINE : This folder contains the map anchor generalized train line zoom 12,13,14,15,16<br>(12,13,14,15,16) pou madison</li> <li>ANCHOR PENINSULA :This folder contains the peninsula map anchor generalized to zoom 12,13,14,15,16 for Madison.</li> <li>...</li> </ol> <ul> <li>QGIS Project : Qgis project to make the squeleton map</li> <li>Illustration : </li> <li>Web : Anchoring map from madison</li> <li>Scalemaster : Scaleline of madison for anchoring</li> <li>Globalscaline : Scaleline on the global aspect of pan-scalar map</li> </ul>
Dataset of the publication: Spin-crossover nanoparticles anchored on MoS2 layers for heterostructures with tunable strain driven by thermal or light-induced spin switching
<p>Dataset of the publication: </p> <div>Spin-crossover nanoparticles anchored on MoS2 layers for heterostructures with tunable strain driven by thermal or light-induced spin switching</div> <div> <div>https://doi.org/10.1038/s41557-021-00795-y</div> <div>R. Torres-Cavanillas, M. Morant-Giner, G. Escorcia-Ariza, J. Dugay, J. Canet-Ferrer, S. Tatay, S. Cardona-Serra, M. Giménez-Marqués, M. Galbiati, A. Forment-Aliaga, E. Coronado, <em>Nat Chem</em> <strong>2021</strong>, <em>13</em>, 1101.</div> </div>
Data for "Atomic-scale perspective on individual thiol-terminated molecules anchored to single S vacancies in MoS2"
<p>This repository provides the original datasets for the manuscript "Atomic-scale perspective on individual thiol-terminated molecules anchored to single S vacancies in MoS2". It includes the original experimental data as well as the iPython Notebooks used to treat it in "<a href="../api/records/10160204/draft/files/Data_and_Analysis.zip/content" target="_blank" rel="noopener noreferrer">Data_and_Analysis.zip</a>" (see readme in individual folders for precise information), the datasets for the structure search and molecular dynamics calculations in "<a href="../api/records/10160204/draft/files/structure_search_and_MD.zip/content" target="_blank" rel="noopener noreferrer">structure_search_and_MD.zip</a>", as well as the data for the DFT calculations for the projected electronic density of states (PDoS) and the orbital densities of Fig.5 and 8 in "<a href="../api/records/10160204/draft/files/fig5.zip/content" target="_blank" rel="noopener noreferrer">fig5.zip</a>" and "<a href="../api/records/10160204/draft/files/fig8.zip/content" target="_blank" rel="noopener noreferrer">fig8.zip</a>", respectively.</p> <p> </p>
Anchored phylogenomics unravels the evolution of spider flies (Diptera, Acroceridae) and reveals discordance between nucleotides and amino acids
<p>Supplementary Material accompanying the manuscript titled "Anchored phylogenomics unravels the evolution of spider flies (Acroceridae) and reveals discordance between nucleotides and amino acids", including Supplementary Figures, Tables and Datasets.</p>
Advice Taking under Uncertainty: The Impact of Genuine Advice versus Arbitrary Anchors on Judgment
<p>A major module of rational advice taking consists in the metacognitive ability to distinguish between credible advice and arbitrary anchors. Accordingly, we investigated the extent to which framing the very same information as either advice or anchor exerts a differential influence on quantitative judgments. Four experiments showed that although arbitrary anchors were given lower weight than advice, they nevertheless exerted a systematic impact on final judgments. Degree of integration was related to subjective confidence only in the advice condition, but not in the anchoring condition, suggesting that arbitrary anchors were not considered informative. Framing the source of advice as a human being versus as a computer did not affect our results. Only the aboutness of advice, that is, whether it targeted the focal judgment item, determined its influence on final judgments and on confidence. On the one hand, these findings speak to the (partial) sensitivity of human judges to the source and validity of advice under uncertainty. On the other hand, the persevering effect of arbitrary anchors demonstrates the dependence of judgments on unauthorized influences. Both findings together highlight the need to study advice taking from a metacognitive perspective.</p>
Linked collectors and determiners for: A whiter shade of pale: anchoring the name Paecilaema C. L. Koch, 1839 onto a neotype (Opiliones, Cosmetidae).
Natural history specimen data linked to collectors and determiners held within, "A whiter shade of pale: anchoring the name Paecilaema C. L. Koch, 1839 onto a neotype (Opiliones, Cosmetidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d2d146be-d297-4c55-a744-c51d71221e78">https://bionomia.net/dataset/d2d146be-d297-4c55-a744-c51d71221e78</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d2d146be-d297-4c55-a744-c51d71221e78">https://gbif.org/dataset/d2d146be-d297-4c55-a744-c51d71221e78</a>. Formatted as a Frictionless Data package.
Known and Predicted GPI-anchored proteins in Trypanosoma brucei
<p>This table is a list of proteins that are either known or predicted to be a GPI-anchored proteins in <em>Trypanosoma brucei</em>. </p>
Carbon-isotope, geochemical, and biostratigraphic data from the Anchor 3 well, Green Canyon protraction area, Gulf of Mexico
<p>Dataset that accompanies "Carbon-isotope chemostratigraphy, geochemistry, and biostratigraphy of the Paleocene-Eocene Thermal Maximum, deep-water Wilcox Group, Gulf of Mexico (U.S.A.)"</p> <p>To be submitted to <em>Climate of the Past</em></p>
Automated improvement of stickleback reference genome assemblies with Lep-Anchor software
Open the record for dataset details and reuse information.
Hippocampal firing fields anchored to a moving object predict homing direction during path-integration-based behavior
Open the record for dataset details and reuse information.
An investigation on the effect of high energy storage anchor on surrounding rock conditions
High pre-tension bolt is an effective strata control technique and is the key to ensure the stability of anchorage and roadway. Based on the performances of high energy storage tension rock bolts in different rock properties, this study proposed a constitutive model to describe the energy balance of anchor under uniaxial compression. UDEC was used to simulate the behaviour of anchor in coal under uniaxial compression and the results were analysed to study the rock mechanical properties, degree of damage and energy evolution. Simulation results showed that tension rock bolts can improve the mechanical properties and energy storage capacities of anchor. The energy evolution was divided into three stages: i) the external work was stored in the form of elastic strain energy (Ue) in the anchor prior to the yielding strength; ii) the elastic strain energy reached its maximum near the peak strength; iii) Energy was dissipated from fracture friction (Wf ), plastic deformation (Wp) and acoustic emission (Ur) during post-peak stage. The installation of tension rock bolts was more suitable for medium hard rock (e.g. sandy mudstone), whereas it was not effective for hard rock (e.g. sandstone).
"La Brière" Anchor
Quick photogrammetry scan (70 pictures) of an anchor coming from the shipwreck of "La Brière", a cargo-ship which sanked off the coast of [Brignogan](https://goo.gl/maps/eiQHTSLuj2H2) in 1930. Amongst the cargo of the boat were bicycles, clothes, sugar, wood, copper... as well as 8 cows who are said to have survive by swimming to the coast ! Source: Objaverse 1.0 / Sketchfab
Rusty old anchor
* Software: Reality Capture * Images: 415 * Render setting: Normal quality * Camera: iPhone 7 Plus * Date: July 11th, 2017 * Location: Öckerö, Sweden Source: Objaverse 1.0 / Sketchfab
Anchor @ San Carlos de Borromeo Castle
Ancla no-identificada ubicada dentro del Castillo de San Carlos de Borromeo, Isla de Margarita, Venezuela Unidentified anchor located inside of the "San Carlos de Borromeo" castle in Margarita Island, Venezuela *Generated from 450+ images extracted from 2x HD videos captured with phone camera (Moto G5), using Meshroom. Resampled and filtered with Meshlab* Source: Objaverse 1.0 / Sketchfab
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.