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522 results for “consolidation”
Alpha-2 Adrenoreceptor Antagonist Yohimbine Potentiates Consolidation of Conditioned Fear (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Panitz, C., Skoluda, N., Nater, U. M., Pizzagalli, D. A., Hermann, C., & Mueller, E. M. (2022). Alpha-2 adrenoreceptor antagonist yohimbine potentiates consolidation of conditioned fear. <em>International Journal of Neuropsychopharmacology</em>, 25(9), 759–773.</strong></p> <p><em>Background:</em> Hyperconsolidation of aversive associations and poor extinction learning have been hypothesized to be crucial in the acquisition of pathological fear. Previous animal and human research points to the potential role of the catecholaminergic system, particularly noradrenaline and dopamine, in acquiring emotional memories. Here, we investigated in a between-participants design with 3 groups whether the noradrenergic alpha-2 adrenoreceptor antagonist yohimbine and the dopaminergic D2-receptor antagonist sulpiride modulate long-term fear conditioning and extinction in humans.<br><em>Methods:</em> Fifty-five healthy male students were recruited. The final sample consisted of n = 51 participants who were explicitly aware of the contingencies between conditioned stimuli (CS) and unconditioned stimuli after fear acquisition. The participants were then randomly assigned to 1 of the 3 groups and received either yohimbine (10 mg, n = 17), sulpiride (200 mg, n = 16), or placebo (n = 18) between fear acquisition and extinction. Recall of conditioned (non-extinguished CS+ vs CS−) and extinguished fear (extinguished CS+ vs CS−) was assessed 1 day later, and a 64-channel electroencephalogram was recorded.<br><em>Results:</em> The yohimbine group showed increased salivary alpha-amylase activity, confirming a successful manipulation of central noradrenergic release. Elevated fear-conditioned bradycardia and larger differential amplitudes of the N170 and late positive potential components in the event-related brain potential indicated that yohimbine treatment (compared with a placebo and sulpiride) enhanced fear recall during day 2.<br><em>Conclusions:</em> These results suggest that yohimbine potentiates cardiac and central electrophysiological signatures of fear memory consolidation. They thereby elucidate the key role of noradrenaline in strengthening the consolidation of conditioned fear associations, which may be a key mechanism in the etiology of fear-related disorders.</p>
GulfDrifters: A consolidated surface drifter dataset for the Gulf of Mexico
<p>This dataset consists of all publicly available surface drifter trajectories from the Gulf of Mexico, subjected to a uniform quality control and processing methodology and interpolated onto hourly resolution. Full details as to the datasets and processing may be found in</p> <p>Lilly, J. M. and P. Pérez-Brunius (2021). A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements. <em>Earth System Science Data</em>, 13: 645–669. https://doi.org/10.5194/essd-13-645-2021.</p> <p>A related dataset is the GulfFlow space/time gridded velocity product, comprised of these data together with three proprietary experiments. GulfFlow is available at https://zenodo.org/record/3978793 for noncommercial use. </p> <p>One of those proprietary experiments is the Deep Water Dispersion Experiment (DWDE). This is available for noncommercial use at https://zenodo.org/record/3979964, together with a version of the GulfDrifters dataset, GulfDriftersDWDE, that also incorporates the DWDE data.</p> <p> </p> <p> </p> <p> </p>
A Comprehensive Self-Consolidating Concrete Dataset for Advanced Construction Practices
<ul> <li><span>Size: over 2500 Self-consolidating concrete mixtures from 176 published papers.</span></li> <li><span>Material type: Self-consolidating concrete (SCC).</span></li> <li><span>Features:</span> <ul> <li><span>Identification features (5 features): References, number of the mixture, the authors, year of publication, & the mixture code.</span></li> <li><span>Powders type, content, & density (76 features): Cement, various supplementary cementitious materials, & other mineral additions.</span></li> <li><span>Paste properties (8 features): The total amount of powder used, the water content, the calculated volume of the paste, the water-to-cement ratio, the water-to-binder ratio, the water-to-powder ratio, the volume of water to the volume of powder ratio, & the volume of water to the volume of cement ratio.</span></li> <li><span>Aggregate properties (7 features): Content and density of fine and coarse aggregates, the total aggregate, the maximum size of the aggregate, & the fine-to-total-aggregate ratio.</span></li> <li><span>Admixture properties (3 features): Quantity of admixture used, its proportion relative to the cement & the total binder content.</span></li> </ul> </li> <li>Properties: <ul> <li>Fresh properties (13 features): Including filling ability properties, i.e., slump flow spread, V-funnel flow time, & the T50 time; Passing ability properties, i.e., J-Ring flow spread, L-box H1/H2 ratio, & U-box flow; Segregation resistance i.e., sieve segregation index, column segregation index, dynamic segregation index, segregation factor, & sieve GTM stability test. Additionally, the percentage of air content is also documented.</li> <li><span>Rheological properties (3 features): yield stress & plastic viscosity values alongside with the used rheometer. The instruments employed in these measurements include the ICAR Rheometer, R/S Plus Rheometer, ConTec5 Viscometer, ConTec4SCC, Concrete Shear Box, & TR-CRI Concrete Rheometer.</span></li> </ul> </li> <li><span>Application: Essential in choosing Self-Compacting Concrete (SCC) mixtures for different uses, considering the importance of both fresh & rheological properties. Intended to support the creation of sustainable & eco-friendly building materials.</span></li> </ul>
RMTable Consolidated Catalog of Faraday Rotation Measures of Astronomical Radio Sources
<p>This is a catalog of Faraday rotation measures (and other related properties) of astronomical radio sources, consolidated from many published catalogs in the astronomical literature from 1980 to the present day. These catalogs have been converted to the RMTable standard and stored in 3 formats: FITS binary table, tab-seperated-value ASCII, and VOTable XML.</p> <p>These catalog files can be read by any suitable reader, but we have created a Python module, RMTable (https://github.com/CIRADA-Tools/RMTable), which streamlines the process of interacting with and creating new RMTables.</p>
BeauAMP : processing and consolidation of open data on public procurement in France (2015-2023)
<p>This accurate and comprehensive dataset encapsulates the main information published on the BOAMP website (the official journal for public procurement notices in France) from 2015 to 2023, enriched with the individual characteristics of contracting authorities and holders of public contracts. After converting the notices into a processed table, we use a machine learning algorithm to estimate the SIRETs (i.e. national identifiers) of the contracting parties, so that we can merge the open data on public procurement with individual information on public and private agents (size, legal status, main activity, geolocation...). Finally, we estimate the geolocation of foreign firms. The dataset contains about 300,000 public contracts and describes more than 1,000,000 interactions between approximately 16,000 public entities and 130,000 companies. It covers over 100 variables on the contract features, the outcome of the award procedure, the characteristics of contracting authorities and the characteristics of awarded firms.</p> <p> </p> <p>See similar data from 2024 : https://zenodo.org/records/17187786</p>
Spectral induced polarization of non-consolidated heterogeneous clay mixtures
<p>We present a spectral induced polarization dataset on heterogeneous mixtures of illite and red montmorillonite, with two longitudinal, and one transversal arrangement. Additionally, there is a 50-50% in volume content homogeneous mixture of illite and red montmorillonite.</p> <p>Each file has its header, describing each column. The ReadMe file also explains the content and format of each dataset.</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
Data sets for simulation of urban construction consolidation centres
<p>Data from SUCCESS H2020 project used as input in the simulation activities of the Work Package 4</p> <p>This data sets is a public version of the data used in the simulation activities of the workpackage 4 in the SUCCESS project.</p> <p>It can be used to simulate the options of using one, several or no construction consolidation centers in an urban area.</p> <p>The dataset is composed of 7 distinct CSV files. All CSV files have headers.</p> <ol> <li>CCC_options_data.csv</li> <li>construction sites_data.csv</li> <li>material_demand.csv</li> <li>material_demand_periods.csv</li> <li>origin_destination.csv.csv</li> <li>suppliers_data.csv</li> <li>trucks_data.csv</li> </ol> <p><strong>construction sites_data </strong>file</p> <p>This file contains descriptions of construction sites that would be candidate to use the services of a Construction Consolidation Center (CCC).</p> <p>This file contains 99 observations of 11 fields :</p> <ol> <li> <p>site_id (<em>String</em>)<br> a unique identifier of the construction site, composed of:</p> <ul> <li> <p>one letter, </p> </li> <li> <p>an underscore, and </p> </li> <li> <p>3 digits.</p> <p>The letter represents the success pilot that provided the data. The digits sequence is the numeric identifier for the pilot.</p> </li> </ul> </li> <li> <p>private_public (<em>String</em>)<br> The mention whether the site builds a public building, a private building or a mixed building (both public and private)</p> </li> <li> <p>site_profile (<em>String</em>)<br> The profile of the building under construction:</p> <ul> <li> <p><strong>Profile I</strong> is an <strong>apartments building</strong>,</p> </li> <li> <p><strong>Profile II </strong>is an <strong>offices building</strong>,</p> </li> <li> <p><strong>Profile III </strong>is a <strong>leisure </strong>construction,</p> </li> <li> <p><strong>Profile IV</strong> is a <strong>specific building </strong>like an hospital</p> </li> </ul> </li> <li> <p>Y1 (<em>Integer</em>)<br> the turnover of the construction site on the first year of operations in EUR</p> </li> <li> <p>Y2 (<em>Integer</em>)<br> the turnover of the construction site on the second year of operations in EUR</p> </li> <li> <p>Y3 (<em>Integer</em>)<br> the turnover of the construction site on the third year of operations in EUR</p> </li> <li> <p>start (<em>Date</em>)<br> The start date of the construction project</p> </li> <li> <p>end (<em>Date</em>)<br> The end date of the construction project</p> </li> <li> <p>duration (<em>Integer</em>)<br> The duration of the construction project in months</p> </li> <li> <p>total_value_eur (<em>Integer)</em><br> The total value of the construction project in EUR</p> </li> <li> <p>size_sqm (<em>Integer</em>)<br> The size of the construction project in square meters</p> </li> </ol> <p><strong>CCC_options_data </strong>file</p> <p>This file contains descriptions of Construction Consolidation Centers that could service construction sites.</p> <p>This file contains 25 observations of 40 fields :</p> <ol> <li>ccc_id (<em>String</em>)<br> a unique identifier of the CCC, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>capacity_sqm (<em>Integer</em>)<br> The storage area capacity of the CCC in square meters</li> <li>capacity_cubic_meters (<em>Integer</em>)<br> The storage volume capacity of the CCC in cubic meters</li> <li>Activation_Cost (<em>Integer</em>)<br> The CCC activation costs in EUR</li> <li>Accessories (<em>Integer</em>)<br> The storage capacity for Accessories</li> <li>Bitumen (<em>Integer</em>)<br> The storage capacity for Bitumen</li> <li>Bricks (<em>Integer)</em><br> The storage capacity for Bricks</li> <li>Cement (Integer)<br> The storage capacity for Cement</li> <li>Coating (<em>Integer)</em><br> The storage capacity for Coating</li> <li>Electrical (Integer)<br> The storage capacity for Electrical</li> <li>Epoxi (<em>Integer)</em><br> The storage capacity for Epoxi</li> <li>External_Doors (<em>Integer</em>)<br> The storage capacity for External_Doors</li> <li>Fences (<em>Integer</em>)<br> The storage capacity for Fences</li> <li>Fire_Doors (<em>Integer</em>)<br> The storage capacity for Fire_Doors</li> <li>Gabions (<em>Integer</em>)<br> The storage capacity for Gabions</li> <li>Garden_Equipment (<em>Integer</em>)<br> The storage capacity for Garden_Equipment</li> <li>Geotexil (<em>Integer</em>)<br> The storage capacity for Geotexil</li> <li>Glass_wool (<em>Integer</em>)<br> The storage capacity for Glass_wool</li> <li>Hydraulic (<em>Integer</em>)<br> The storage capacity for Hydraulic</li> <li>Internal_Doors (<em>Integer</em>)<br> The storage capacity for Internal_Doors</li> <li>Lift (<em>Integer</em>)<br> The storage capacity for Lift</li> <li>Metal (<em>Integer</em>)<br> The storage capacity for Metal</li> <li>Metal_1 (<em>Integer</em>)<br> The storage capacity for Metal_1</li> <li>Paint (<em>Integer</em>)<br> The storage capacity for Paint</li> <li>Parquet (<em>Integer</em>)<br> The storage capacity for Parquet</li> <li>Pipes (<em>Integer</em>)<br> The storage capacity for Pipes</li> <li>Plants (<em>Integer</em>)<br> The storage capacity for Plants</li> <li>Plaster (<em>Integer</em>)<br> The storage capacity for Plaster</li> <li>Polystyrene (<em>Integer</em>)<br> The storage capacity for Polystyrene</li> <li>Precasted_Concrete (<em>Integer</em>)<br> The storage capacity for Precasted_Concrete</li> <li>Roof (<em>Integer</em>)<br> The storage capacity for Roof</li> <li>Scaffolding (<em>Integer</em>)<br> The storage capacity for Scaffolding</li> <li>Signals (<em>Integer</em>)<br> The storage capacity for Signals</li> <li>Steel (<em>Integer</em>)<br> The storage capacity for Steel</li> <li>Stone (<em>Integer</em>)<br> The storage capacity for Stone</li> <li>Store_Equipment (<em>Integer</em>)<br> The storage capacity for Store_Equipment</li> <li>Tar (<em>Integer</em>)<br> The storage capacity for Tar</li> <li>Tiles (<em>Integer</em>)<br> The storage capacity for Tiles</li> <li>Windows (<em>Integer</em>)<br> The storage capacity for Windows</li> <li>Wood (<em>Integer</em>)<br> The storage capacity for Wood</li> </ol> <p> </p> <p><strong>suppliers_data</strong> file</p> <p> </p> <p>This file contains description of suppliers that provide materials to the above construction sites.</p> <p>This file contains 407 observations of 3 fields :</p> <ol> <li>supplier_id (<em>String</em>)<br> an identifier of the supplier, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>Material_delivered (<em>String</em>)<br> the material delivered by the supplier</li> <li>Truck (<em>Integer</em>)<br> the truck identifeir of the usual truck used by the supplier to deliver the material</li> </ol> <p><strong>trucks_data</strong> file</p> <p>This file contains description of truck used by suppliers to deliver construction sites.</p> <p>This file contains 5 observations of 6 fields:</p> <ol> <li>Truck_id (<em>Integer</em>)<br> a unique identifier for the truck</li> <li>Vehicle (<em>String</em>)<br> description of the vehicle (including the number of axles)</li> <li>Capacity_(kg) (<em>Integer</em>)<br> the material transport capacity of the truck in kilograms</li> <li>Capacity_(m3) (<em>Integer</em>)<br> the material transport capacity of the truck in cubic meters</li> <li>FlagFirstEchelon (<em>String)</em><br> a flag indicating if the truck is used in 1st echelon</li> <li>FlagSecondEchelon (<em>String</em>)<br> a flag indicating if the truck is used in 2nd echelon</li> </ol> <p><strong>origin_destination </strong>file</p> <p>This file contains the quantitative data of distance and time to travel from construction sites, suppliers, and CCCs to construction sites, suppliers and CCCs using a delivery truck.</p> <p>This file contains 38640 observations of 4 fields:</p> <ol> <li>origin (<em>String</em>)<br> A composite identifier of the origin location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>destination (<em>String</em>)<br> A composite identifier of the destination location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>meters (<em>Integer</em>)<br> The drive distance from origin to destination in meters</li> <li>seconds (<em>Integer</em>)<br> The driving time from origin to destination in seconds</li> </ol> <p><strong>material demand </strong>file</p> <p>This file contains the qualitative data representing the material demand of construction sites per construction site profile .</p> <p>This file contains 1277 observations of 7 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> a unique identifier for the material demand</li> <li>profile (<em>String)</em><br> the profile of the construction site for such demand</li> <li>start_date (<em>Date</em>)<br> the start date of the activity</li> <li>end_date (<em>Date</em>)<br> the end date of the activity</li> <li>number_of_days (<em>Integer</em>)<br> the duration of the activity in days</li> <li>material (<em>String</em>)<br> the type of material requested</li> <li>supplier_id (<em>String</em>)<br> the identifier of the supplier providing the material</li> </ol> <p><strong>material_demand_periods </strong>file</p> <p>This file contains the quantitative demand data per demand and per period. Units of periods are weeks.</p> <p>This file contains 93663 observations of 5 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> the identifier for the material demand</li> <li>profile (<em>String</em>)<br> the profile type of construction for the demand</li> <li>period (<em>Integer</em>)<br> the period of the construction project during which the material has to be delivered (in number of weeks from the beginning of the construction project)</li> <li>demand_m3 (<em>Integer)</em><br> the volume of material to be delivered during the period</li> <li>demand_kg (<em>Integer)</em><br> the weight of material to be deliverd during the period</li> </ol>
"45' Reception" Our Mythical Childhood ERC Consolidator Grant Project, Episode 4: "Cyclades" and "Olympos"
<p>"45' Reception" Our Mythical Childhood ERC Consolidator Grant Project: Episode 4: "Cyclades" and "Olympos"</p> <p>Storyteller: Urlich Schädler, Swiss Museum of Games; ERC Advanced Grant: "Locus Ludi: The Cultural Fabric of Play and Games in Classical Antiquity", www. locusludi.ch </p> <p>Edit: Anna Mik, University of Warsaw, with the collaboration of Edwan: <a href="https://youtube.com/edwanmusic">https://youtube.com/edwanmusic</a>.</p> <p>Music: Chris Haugen "Way Out West" - YouTube license</p> <p>With special thank you to Mirosław Kaźmierczak for preparing the graphic designs.</p>
"45' Reception" Our Mythical Childhood ERC Consolidator Grant Project: Episode 5: "Blu e le streghe" ["Blu and the Witches"]
<p>Storyteller: Alessia Borriello, University of Bologna/Erasmus+ at the University of Warsaw, a finalist of the Italian Literary Contest Zeno 2019 (<a href="https://www.youtube.com/redirect?q=https%3A%2F%2Fprogettozeno.it%2Fnews%2F1503%2Fpremio-zeno%3A-seconda-selezione-racconti-lunghi&redir_token=QUFFLUhqbEcxSzZnTFRDSHdrOFVIZTJNcnRaN3B1TFdPZ3xBQ3Jtc0tsTElCTnJESXlhSlprNXhEZElVLWFFQU0xcDNzZ2JDV2xaV2pBYzlFdlNreERGMFQwY0tVdW1zQ3dsZGpJUGJVX2QyWU1KRFBTNVh6NUNzMWFHN21UR2ZFeE9YQktzczJDYXlVSDZVNkFOaDdwdkVTMA%3D%3D&event=video_description&v=XRaHTWERd4o">https://progettozeno.it/news/1503/pre...</a>)</p> <p>Illustration for the novel by Ludovica Lusvardi, student at Politecnico di Milano, Fashion Design</p> <p>Edit: Anna Mik, University of Warsaw</p> <p>Music: Chris Haugen "Way Out West" - You Tube license</p> <p>With special thank you to Mirosław Kaźmierczak for preparing the graphic designs.</p>
Temporal cluster-based organisation of sleep spindles underlies motor memory consolidation
<p><span>Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remai<span>n unclear. Here, sim</span>ultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organised patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only</span><span>. Altogether</span><span>,</span><span> our findings suggest t</span><span>hat</span><span> spindles' clustering and </span><span>rhythmic occurrence </span><span>during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronised oscillatory activity within a subcortical-cortical network involved during learning</span>.</p>
Data of Self-Weight Consolidation Process of Water-Saturated Deltas on Mars and Earth
<p><strong>Data of the paper "Self-Weight Consolidation Process of Water-Saturated Deltas on Mars and Earth".</strong> This dataset includes five tables. <strong>Table S1</strong> is the original data of the measurements of the moisture content <em>w</em><sub>0</sub>, <strong>Table S2</strong> is the original data of the pycnometer test, which was conducted to obtain the specific gravity <em>G</em><sub>s</sub> of our samples, <strong>Table S3</strong> is the original data of the consolidation experiments, <strong>Table S4</strong> is the original data of the permeability experiments, and <strong>Table S</strong><strong>5</strong> is the martian global delta relief obtained by us based on MOLA data, which is used as the maximum thickness of a delta.</p> <p><strong>Table S1.</strong> The original data of the measurements of the moisture content <em>w</em><sub>0</sub>. The initial void ratio is calculated by equation (1). <em>A'</em> is the inner area of the consolidation container.</p> <p><strong>Table S2.</strong> The original data of the pycnometer test, which was conducted to obtain the specific gravity <em>G</em><sub>s</sub> of our samples. The specific gravity <em>G</em><sub>s </sub>can be derived from <em>m</em><sub>d</sub><em>G</em><sub>wT</sub> /(<em>m</em><sub>bw+</sub><em>m</em><sub>d+</sub><em>m</em><sub>bws</sub>), where <em>m</em><sub>d </sub>is the samples’ dry mass, <em>m</em><sub>bw </sub>is the total mass of the pycnometer and water,<em> m</em><sub>bws </sub>is the total mass of the pycnometer, water and samples, and <em>G</em><sub>wT</sub> is the specific gravity of pure water at<em> T </em>℃.</p> <p><strong>Table S3.</strong> The original data of consolidation experiments of our samples. The void ratio is calculated by equation (2).</p> <p><strong>Table S4. </strong>The original data of permeability experiments of our samples. The hydraulic conductivity <em>K </em>was calculated by equations (3) and (4). The inner area of the consolidation container is 30 cm<sup>2</sup>, the cross-sectional area<em> a' </em>of the water pipe is 0.89286 cm<sup>2</sup>, and the seepage path length <em>L</em> equals the sample initial height <em>h</em><sub>0</sub> minus the accumulated height <em>Σ</em>Δ<em>h</em><sub>i</sub>. <em>t</em>1 and <em>t</em>2<sub> </sub>are the first and the second test results of time-taken for water dropping from <em>H</em><sub>1</sub> to <em>H</em><sub>2</sub>, respectively. <em>t</em> is the average of <em>t</em>1 and <em>t</em>2. <em>T</em><em>’</em> is the temperature during the experiments. Note: we only test the <em>T</em><em>’</em> of the third group and here we used the average of <em>T’ </em>(=12.5℃) to represent the temperature of all three parallel groups during the experiments. It’s acceptable because <em>T’</em> varies slightly throughout the experiments, whose fluctuations hardly affect the order of magnitude of the hydraulic conductivity <em>K</em>. The seepage velocity <em>v</em>=<em>Q</em>/<em>A’t</em>, in which <em>Q</em> is the volume of water that seeps out of the samples.</p> <p><strong>Table S5.</strong> The delta relief of a delta. The locations of martian deltas are based on the database of Wilson et al. (2021)</p> <p> </p> <p> </p>
Spectral induced polarization of non-consolidated clays
<p>We present a spectral induced polarization dataset on four different types of clay (red and green montmorillonite samples, kaolinite sample, and illite sample) at five different salinities (from de-ionised water to 1 mol/L NaCl), and additionally two other clay samples (beige montmorillonite sample and Boom clay sample) at three differente salinities (from de-ionised water to 1 mol/L NaCl).</p> <p>Each file has its header, describing each column.</p> <p>The logic of the filenames is: "Clay type_salinity_SIP_raw".txt</p> <p><br> These results are published in Mendieta et al. (2021):<br> https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JB021125 (pre-print: doi:10.1002/essoar.10505885.2)</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>The parameter estimates along with their 90% confidence intervals obtained for the 20 multinomial logistic regressions are shown here in two presentations. In 'Covariate trends' we show the annual trends for the 20 years of data by type of covariate. In 'Covariates magnitude for each year', we show for each year the magnitude that each covariate has in contrast with the other 23 covariates (the intercept is not included due to scaling issues).</p> <p>All parameter estimates and their confidence intervals can be found in a table format in 'allparameters.csv'.</p>
Рис. 5. Дополнительные структуры, служаЩие укреплению Замочного краЯ и раковины у Laternula elliptica: А – дополнительнаЯ поддерживаюЩаЯ пластинка прикрывает макушечную Щель; Б – утолЩение ранее поврежденного краЯ раковины; В – пример воЗникновениЯ двух поддерживаюЩих пластинок. ОбоЗначениЯ: мщ – макушечнаЯ Щель; хр – хондрофор; ппЛ – поддерживаюЩаЯ пластинка; дпЛ – дополнительные пластинки; уКр – утолЩениЯ Задне-дорсального краЯ и краЯ сифонального ЗиЯниЯ. Fig. 5. Additional structures serving for consolidation of hinge margin and for restoration of shell edge in Laternula elliptica: А – the umbonal crack covered by additional buttress; Б – thickening of damaged edge; В – appearance of two supporting plates. Notes: мщ – umbonal crack; хр – chondrophore; ппЛ – buttress; дпЛ – additional supporting plate; уКр – thickening of posterior-dorsal margin. in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 5. Дополнительные структуры, служаЩие укреплению Замочного краЯ и раковины у Laternula elliptica: А – дополнительнаЯ поддерживаюЩаЯ пластинка прикрывает макушечную Щель; Б – утолЩение ранее поврежденного краЯ раковины; В – пример воЗникновениЯ двух поддерживаюЩих пластинок. ОбоЗначениЯ: мщ – макушечнаЯ Щель; хр – хондрофор; ппЛ – поддерживаюЩаЯ пластинка; дпЛ – дополнительные пластинки; уКр – утолЩениЯ Задне-дорсального краЯ и краЯ сифонального ЗиЯниЯ. Fig. 5. Additional structures serving for consolidation of hinge margin and for restoration of shell edge in Laternula elliptica: А – the umbonal crack covered by additional buttress; Б – thickening of damaged edge; В – appearance of two supporting plates. Notes: мщ – umbonal crack; хр – chondrophore; ппЛ – buttress; дпЛ – additional supporting plate; уКр – thickening of posterior-dorsal margin.
Dataset: Consolidated Water Co. Ltd. (CWCO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Coca-Cola Consolidated, Inc. (COKE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Consolidated Communications Holdings, Inc. (CNSL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Supplemental Material: Consolidating the concept of low-energy magnetic dipole decay radiation
<p>This record consists of all shell model calculation results used in Midtbø <em>et al</em>., <em>Consolidating the picture of low-energy magnetic dipole decay radiation, </em>Phys. Rev. C (2018, accepted), arXiv:1807.04036 [nucl-th].</p> <p>All calculations are performed using KSHELL (arXiv:1310.5431 [nucl-th]). For details on our calculations we refer to our article.</p>
Figure 4 in A consolidated account of the polymorphic Caribbean milliped, Anadenobolus monilicornis (Porat, 1876) (Spirobolida: Rhinocricidae), with illustrations of the holotype
Figure 4. Distribution of A. monilicornis plotting both indigenous and adventive localities; the bold arrow denotes Bermuda. Some dots represent more than one closely proximate site. The question marks denote the questionable occurrence in Apopka, Orange Co., Florida, and the unknown type locality in Brazil; the stars denote adventive specimens in North Carolina, USA, and Manitoba, Canada.
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.