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Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
They saw it, onu, 它, coming: An information theoretic study of cross-linguistic variation in personal pronouns (Appendix B)
<p>The pronominal systems: data from miniciep+ on pre/post verbal position and alternative vs. standard version (where available)</p>
Literature information on benthic mesophotic ecosystems of the Mediterranean Sea
<p>Literature records on benthic mesophotic ecosystems in the Mediterranean Sea reporting the reference, the subject of the study, location. Depth of occurrence of mesophotic ecosystems obtained from EmodNET bathymetry (https://www.emodnet-bathymetry.eu) is also included.</p>
DRIAMS: Database of Resistance Information on Antimicrobials and MALDI-TOF Mass Spectra
<p>Early administration of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based techniques can take up to 72 hours to generate results. We have developed a novel machine learning approach to predict antimicrobial resistance directly from MALDI-TOF mass spectra profiles of clinical samples. We trained calibrated classifiers on a newly-created publicly available database of mass spectra profiles from clinically most relevant isolates with linked antimicrobial susceptibility phenotypes. The dataset combines more than 300,000 mass spectra with more than 750,000 antimicrobial resistance phenotypes from four medical institutions. Validation against a panel of clinically important pathogens, including Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, resulting in AUROC values of 0.80, 0.74, and 0.74 respectively, demonstrated the potential of using machine learning to substantially accelerate antimicrobial resistance determination and change of clinical management. Furthermore, a retrospective clinical case study found that implementation of this approach would have resulted in a beneficial change in the clinical treatment in 88% (8/9) of cases. MALDI-TOF mass spectra based machine learning may thus be an important new tool for treatment optimization and antibiotic stewardship.</p>
Biologically informed machine learning for identifying human placental cellular heterogeneity and preeclampsia discovery
<p>Single-cell transcripts of 20,518 placental cells and identified 12 major placental cell clusters were collected from the European Bioinformatics Institute (EBI; accession no. EGAS00001002449) (29). Based on the same parameters, we clustered and visualized highly similar cells using t-distribution random neighbourhood embedding (T-SNE) to identify 17 cell subpopulations. we selected nine placental cell clusters that have received more attention from biologists for our study according to the literature survey (Table 1). Considering the sample balance, 7178 single-cell transcriptome data were used to identify human placental cell subpopulations. The samples were randomly divided into a 4809-sample training set and a 2369-sample testing set. The same strategy was applied to split single-cell transcriptomic datasets from healthy and preeclampsia patients (EBI; accession no. EGAS00001002449), with 9852 samples (healthy 4705, preeclampsia 5147) in the training set and 5305 samples (healthy 2473, preeclampsia 2832) in the independent test set</p>
Electronic supplementary information: Independent and adaptive evolution of phenotypic novelties is driven by coral symbiosis in barnacle larvae
<p class="Standard">The invasion of novel habitats is recognized as a major promotor of adaptive trait evolution in animals. We tested whether similar ecological niches entail independent and adaptive evolution of key phenotypic structures related to larval host invasion in distantly related taxa. We use disparately related clades of coral barnacles as our model system (Acrothoracica: <i>Berndtia</i> and Thoracica: Pyrgomatidae). We analyze the larval antennular phenotypes and functional morphologies facilitating host invasion. Extensive video recordings show that coral host invasion is carried out exclusively by cypris larvae with spear-shaped antennules. These first exercise a series of complex probing behaviors followed by repeated antennular penetration of the soft host tissues, which subsequently facilitates permanent invasion. Phylogenetic mapping of larval form and function related to niche invasion in 99 species of barnacles (Thecostraca) compellingly shows that the spear-phenotype is uniquely associated with corals and penetrative behaviors. These features evolved independently in the two coral barnacle clades and from ancestors with fundamentally different antennular phenotypes. The larval host invasion system in coral barnacles likely evolved adaptively across millions of years for overcoming challenges associated with invading and entering demanding coral hosts.</p> <p class="Standard"> </p> <p class="Standard"><i>Key words: </i>adaptive host invasion, larval phenotypes, coral barnacle, barnacle phylogeny</p>
A Repeated Measures Dataset on Public Responses to the COVID-19 Pandemic: Social Norms, Attitudes, Behaviors, Conspiracy Thinking, and (Mis)Information
<p>We present a quantitative repeated measures dataset from intensive longitudinal research (diary survey) conducted with participants drawn from a nationally representative sample of the German population. This diary survey addresses a wide range of variables relevant to the COVID-19 pandemic, including information seeking, attitudes towards science, public health, and related behaviors. Respondents were asked about their thoughts, challenges, concerns, and experiences related to the pandemic. Survey responses were requested every two weeks from November 2020 and September 2021 for a total of 18 stages of data collection. The diary survey responses were all matched to the same individual. Using the unique identifier for each individual, the diary survey dataset can be analyzed in concert with a larger representative survey sample that was conducted three times within the same research project over this time period. The diary dataset we present here includes socio-demographic information and other potentially relevant variables, ready for secondary analysis.<br> <br> Initial sample frame: <em>N </em>= 1,480</p> <p>Initial diary survey sub-sample: <em>N </em>= 133</p>
Data of "Using current research information systems to investigate data acquisition and data sharing practices of computer scientists"
<p>This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.</p> <p>Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.</p> <p><strong>Data was collected from the following sources:</strong></p> <p>All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.</p> <p>The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.</p> <p>The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. </p> <p> </p> <p>The following data points were used in the analysis:</p> <p><strong>Data points</strong></p> <ul> <li><strong>Main study types</strong></li> <li>Literature-based study (e.g. literature reviews, archive studies, studies of social media)</li> <li>yes/no</li> <li>Novel computational methods (e.g. algorithms, simulations, software)</li> <li>yes/no</li> <li>Interaction studies (e.g, interviews, surveys, tasks, ethnography)</li> <li>yes/no</li> <li>Intervention studies (e.g., EEG, MRI, clinical trials)</li> <li>yes/no</li> <li>Measurement studies (e.g. astronomy, weather, acoustics, chemistry)</li> <li>yes/no</li> <li>Life sciences (e.g. “omics”, ecology)</li> <li>yes/no</li> <li><strong>Data acquisition</strong></li> <li>Article presents a data availability statement</li> <li>yes/no</li> <li>Article does not utilise data</li> <li>yes/no</li> <li>Original data was collected</li> <li>yes/no</li> <li>Open data from prior studies were used</li> <li>yes/no</li> <li>Open data from public authorities, companies, universities and associations</li> <li>yes/no</li> <li><strong>Data sharing</strong></li> <li>Article does not use original data</li> <li>yes/no</li> <li>Data of the article is not available for reuse</li> <li>yes/no</li> <li>Article used openly available data</li> <li>yes/no</li> <li>Authors agree to share their data to interested readers</li> <li>yes/no</li> <li>Article shared data (or part of) as supplementary material</li> <li>yes/no</li> <li>Article shared data (or part of) via open deposition</li> <li>yes/no</li> <li>Article deposited code or used open code</li> <li>yes/no</li> </ul>
X-ray diffraction data, crystallographic information file, infrared spectra, and LA-ICP-MS depthprofiles of davemaoite
<p>Calcium silicate perovskite, CaSiO3, is arguably the most geochemically important phase in the lower mantle, because it concentrates elements that are incompatible in the upper mantle, including the heat-generating elements thorium and uranium, which have half-lives longer than the geologic history of Earth. We report CaSiO3-perovskite as an approved mineral (IMA2020-12a) with the name davemaoite. The natural specimen of davemaoite proves the existence of compositional heterogeneity within the lower mantle. Our observations indicate that davemaoite also hosts potassium in addition to uranium and thorium in its structure. Hence, the regional and global abundances of davemaoite influence the heat budget of the deep mantle, where the mineral is thermodynamically stable.</p>
Experimental evidence that social information affects habitat selection in Marbled Murrelets
<p>Habitat selection decisions can impact individual fitness and ultimately scale up to mediate population dynamics. Understanding how birds select habitat is thus critical for discerning the biological processes structuring populations and developing conservation strategies, particularly for species in decline. Marbled Murrelet (<i>Brachyramphus marmoratus</i>; hereafter murrelet) populations have declined in recent decades due to loss of late-successional forest nesting habitat and changing ocean conditions that impact foraging success. Most other seabirds in the family Alcidae nest colonially and evidence suggests nesting murrelets may aggregate in stands, yet no studies have examined murrelet use of social information in nest site selection. In 2016 we experimentally simulated presence of murrelets at 14 randomly chosen potential breeding sites by broadcasting murrelet calls throughout the breeding period. Between broadcasting bouts, we recorded calls of wild murrelets and compared call rates with those recorded at 14 control sites (no broadcast). One year after playbacks ceased (2017) we conducted breeding season surveys to document behaviors indicative of murrelet breeding activity. Broadcasting murrelet calls in 2016 increased daily odds of wild murrelets vocalizing during the treatment period by up to 15.4× (95% CI = 2.3, 125.4) relative to control sites. During the 2017 breeding season, the odds of occupancy were 10.0× (CI = 1.2, 81.4) greater at treatment sites than control sites. These results indicate that social information influences murrelet breeding site selection because simulated conspecific presence in potential nesting habitat appeared to attract prospectors in 2016 that continued occupying treatment sites the following year. This conspecific attraction implies murrelet nesting sites are likely to remain occupied over time and that large tracts of nesting habitat may be important for supporting murrelet populations. Murrelets may also be susceptible to information-mediated Allee effects whereby a lack of conspecific information about nesting habitat could exacerbate long-term population declines.</p>
Information Science & Library Science Landmarks
<p>This dataset was used in a study for the Information Science & Library Science Landmarks. </p>
Shearwaters sometimes take long homing detours when denied natural outward journey information
<p>The cognitive processes (learning and processing of information) underpinning long-distance navigation of birds are poorly understood. Here, we utilised the homing motivation of the Manx shearwater to investigate navigational decision making in a wild bird by displacing them 294km to the far side of a large island (the Island of Ireland). Since shearwaters are reluctant to fly over land, the island blocked the direct route home, forcing a navigational decision. Further still, on the far side of the obstacle, we chose a release site where use of local knowledge could facilitate a 20% improvement in route efficiency if shearwaters were able to anticipate and avoid a large inlet giving the appearance of open water in the home direction. We found that no shearwater took the most efficient initial route home, but instead oriented in the home direction (even once the obstacle became visible). Upon reaching the obstacle, four shearwaters subsequently circumnavigated the land mass via the long route, travelling a further 900km as a result. Hence, despite readily orienting homewards immediately after displacement, shearwaters seem unaware of the scale of the obstacle formed by a large land mass despite this being a prominent feature of their regular foraging environment.</p>
Supporting information for v2 of ``A Transparent Window into Early-Type Stellar Variability''
<p>These folders contain grids of main-sequence stellar models computed four different metallicities and masses varying from 0.1-60 MSun. Models were computed using MESA r15140 following commit `964e07d` in this GitHub repo: https://github.com/adamjermyn/conv_trends.</p> <p>Changes from the previous version include:</p> <p>- Fixed a bug in calculating certain averages over the convection zone.</p> <p>- Incorporated the effects of radiation pressure on the Rayleigh number.</p> <p>- Increased time resolution of models.</p> <p> </p> <p>The metallicities are as follows:</p> <p>main_Z_time_2022_01_18_13_56_00_sha_ef8c - Z = 0.002 (SMC)</p> <p>main_Z_time_2022_01_18_13_56_06_sha_adbd - Z = 0.006 (LMC)</p> <p>main_Z_time_2022_01_18_13_56_13_sha_e814 - Z = 0.01<br> main_Z_time_2022_01_18_13_56_19_sha_f7ec - Z = 0.014 (MW)</p>
Past wet-dry climate cycles inform Central Asia's future
<p>Here we present the original dat of high-resolution pollen, sediment grain size and stable isotopes for cyclic moisture changes in Lop Nur, northwestern China, Central Asia since the Last Glacial Maximum. The pollen data includes the pollen abundance, concentration and flux of dominant types. The grain size data includes sand, silt, and clay. The stable isotope data comprises C and N. </p>
Armenian: Word order and information structure
<ul> <li> <p><strong>Basic word order: arguments in favour of OV and VO</strong></p> </li> <li> <p><strong>What is information structure: topic, focus</strong></p> </li> <li> <p><strong>Usual position of sentential stress and (EA) auxiliary</strong></p> </li> <li> <p><strong>‘Topical’ constituents (agent/experiencer subject, specific object): positions and properties</strong></p> </li> <li> <p><strong>Preverbal and postverbal focus</strong></p> </li> <li> <p><strong>Head-final vs. head-initial constituents</strong></p> </li> <li> <p><strong>Factors favouring VO order (cognitive, areal, typological)</strong></p> </li> </ul> <p> </p> <p>This lecture is part of the lecture series:</p> <p><em>Glottothèque: Languages of the Anatolia, Caucasus, Iran, Mesopotamia; grammatical snippets online </em>(electronic resource). Bamberg, Cambridge, Göttingen, Moskow, Nicosia, Paris: LACIM network, at https://spw.uni-goettingen.de/projects/lacim/, edited by Christiane Bulut, Anaïd Donabédian-Demopoulos, Geoffrey Haig, Geoffrey Khan, Pollet Samvelian, Stavros Skopeteas, Nina Sumbatova.</p>
Expression of CDCA2 in hepatocellular carcinoma by immunohistochemistry and patients' information
<p>Cohort of hepatocellular carcinoma patients enrolled in the study entitled "Cell division cycle associated 2 (CDCA2) upregulation promotes the progression of hepatocellular carcinoma in a p53-dependant mannar". CDCA2 expression in paraffin-embedded liver cancer tissue sections was evaluated by immunohistochemistry. Patients' clinicopathological information was collected.</p>
Investor Sentiment and Information Efficiency: Evidence from the Bitcoin Market.
<p>This dataset contains information about the volume of Google searches for the term "bitcoin," the values of the Twitter happiness index and the Fear Index, the number of page views for the term "Bitcoin" on Wikipedia, and the online participation of the Bitcointalk.org online forum. Additionally, bitcoin prices and historical returns for the period July 2015 to June 2021 are included.</p>
Information-optimal local features automatically attract covert and overt attention
<p><strong>COVERT ATTENTION FOLDER </strong></p> <p>The <strong>"COVERT ATTENTION" folder</strong> contains <strong>one hundred .mat files</strong> (eight files for sixteen participants). </p> <p>The file name for each subject (from S01 to S16) describes:</p> <ul> <li>Condition: Experimental or Control</li> <li>Cue Validity: 50% or 80%</li> <li>Type of trials: Neutral or Non-Neutral</li> </ul> <p>Each matrix of Neutral trials and Non-Neutral Control trials contains 8 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the gabor: 1=SX; 2=DX </li> <li>gabor orientation: 1 = -20°; 2 = +20°</li> <li>gabor contrast</li> <li>subject's response: 1 = -20°; 2 = +20°</li> <li>correct response: 1= right; 0=wrong</li> <li>trial type: 1= Valid trial; 2 = Invalid trial</li> <li>Reaction time</li> </ul> <p>Each matrix of Non-Neutral Experimental trials contains 10 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the gabor: 1=SX; 2=DX </li> <li>gabor orientation: 1 = -20°; 2 = +20°</li> <li>gabor contrast</li> <li>subject's response: 1 = -20°; 2 = +20°</li> <li>correct response: 1= right; 0=wrong</li> <li>trial type: 1= Valid trial; 2 = Invalid trial</li> <li>Identification code of the specific <em>optimal feature</em> presented</li> <li>Identification code of the specific <em>non-optimal</em> feature presented</li> <li>Reaction time</li> </ul> <p> </p> <p><strong>OVERT ATTENTION FOLDER</strong></p> <p>The file name for each subject (from S01 to S16) describes:</p> <ul> <li>Condition: Experimental or Control</li> <li>Cue Validity: 50% or 80%</li> </ul> <p>The <strong>"OVERT ATTENTION" folder</strong> contains <strong>sixty-four .EDF files</strong> (four files for sixteen participants). The EyeLink EDF* files contain many types of data, including eye movement events (BLINKS, FIXATIONS, SACCADES), messages (including the “default” ones by Eyelink, and the custom made ones, defining the single trial’s specific properties: this type of messages starts with “Trialinfo”), button presses, and eye-data samples. Here we have used the (calibrated) x and y position data of the right eye. Custom-made matlab scripts (available upon request to the authors) have been used to parse the data into trials, filter eye position and estimate eye velocity, select exploitable trials, detect and analyze saccades, etc. The EDF file format is a highly compressed binary format, intended for use with SR Research EyeLink viewers and applications. EDF files are not readable, they need to be transformed into a readable format (typically ASCII). A tool to transform edf files into ascii is freely available on the SR Research Support online forum site: https://download.sr-support.com/dispdoc/page25.html.</p> <p>The folder also contains <strong>sixty-four .mat files</strong> (four files for sixteen participants), containing the relevant information of each trial.</p> <p>Each matrix of the Control condition contains 5 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the place holder: 1=SX; 2=DX </li> <li>Type of trial: 0 = Neutral, 1 = Valid, 2 = Invalid</li> <li>Saccade to target: 1 correct saccade 0 no saccade</li> <li>Saccade latency</li> </ul> <p>Each matrix of Experimental condition contains 8 columns:</p> <ul> <li>trial number</li> <li>side of presentation of the place holder: 1=SX; 2=DX </li> <li>Identification code of the specific <em>optimal feature</em> presented</li> <li>Identification code of the specific <em>non-optimal</em> feature presented</li> <li>Type of trial: 3 = neutral, 4 = valid, 5 = invalid</li> <li>Saccade to target: 1 correct saccade 0 no saccade</li> <li>Saccade latency</li> </ul>
Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"
<p><strong>Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"</strong></p> <p>The dataset contains the following folders:</p> <p>- number_of_contacts: files with the number of contacts between the rim domains of LukF and Hlg2 and the membrane, for different bilayer compositions (Fig. 2).</p> <p>- binding_events: files with the duration of the time interavals in which LukF and Hlg2 are bound to the membrane, and the scripts used to compute for each system the number of binding/unbinding events and the average membrane residence time (Fig. 3).</p> <p>- electrostatic_potential: files of the surface electrostatic potential produced with the adaptive Poisson-Boltzmann solver and used for visualization with Chimera (Fig. 4).</p> <p>- angles: files with the angle values computed between the protein axis and the z-axis of the simulation box (Fig. 5).</p> <p>- contacts_per_residue: files with the number of frames in which each protein residue is in contact with the membrane, with respect to the total number of frames in which the rim domain interacts with the bilayer (Fig. 5).</p> <p>- distance_protein_membrane: files with the minimum distance between the protein and the membrane (Fig. 6).</p> <p>- binding_sites: file produced by PyLipid with relevant information on the main DOPC binding sites identified in LukF.</p> <p>- min_distance_per_residue: files with the minimum distance between each protein residue and the membrane, computed at the binding steps (Fig. S5).</p>
Supplementary Information – HyDRA challenge
<p>Related Output files for the geometry optimizations (wB97xD/def2-TZVP) of train and blind set of Hydra Challenge.</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.