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Replication package for the article "Who chooses fast-track programs in mathematics? The role of class origin, ethnicity, and gender among Norwegian lower-secondary students", submitted to European Societies.
<p>This file contains a replication package for the article "Who chooses fast-track programs in mathematics? The role of class origin, ethnicity, and gender among Norwegian lower-secondary students", submitted to European Societies</p> <p>****************************************************************************************************************************************<br> The analyses in this article are of secondary data from Norwegian state registers. We analyzed the data with concession from The Norwegian Data Protection Authority, and under the auspices of a confidentiality agreement with Statistics Norway. All direct identifiers were stripped from the file prior to analysis. Data were handled in such a way as to protect individual’s privacy and confidentiality. This included storing these data on a secure Research Computing server and requiring each researcher working with the data to sign a confidentiality affidavit. Other parties may not access the data because it would violate the concession given by The Norwegian Data Protection Authority, and the confidentiality agreement with Statistics Norway. This prevents us from uploading the data to a public repository. <br> Researchers may apply to get access to such data, and the procedure one needs to follow is described at Statistics Norway’s webpage: <br> https://www.ssb.no/en/data-til-forskning/utlan-av-data-til-forskere<br> </p>
Contamination in TESS light curves: The case of the Fast Yellow Pulsating Supergiants
<p>MESA 22.11.1 inlist files used to create Fig. 1 of the paper "Contamination in TESS light curves: The case of the Fast Yellow Pulsating Supergiants".</p>
FIG. 2 in Postnatal variation in ectoparasite (Spinturnix emarginata) load in neonates of Geoffroy's bat (Myotis emarginatus): how fast do young bats become infested with ectoparasites?
FIG. 2. Relationship between mite load and body mass in male (Ì) and female (u) neonates of M. emarginatus measured in nine sampling occasions in the Kerend cave, western Iran
FIG. 1 in Postnatal variation in ectoparasite (Spinturnix emarginata) load in neonates of Geoffroy's bat (Myotis emarginatus): how fast do young bats become infested with ectoparasites?
FIG. 1. Average and SE of ectoparasite load in nine sampling occasions on neonates (■) and lactating female M. emarginatus (●) during the postnatal period in the Kerend cave in western Iran. The number of mites and bats is shown in Table 1. The drawn lines show some tendencies although they are not statistically significant. Sampling occasions range from day 1 (June 3) till day 43 (July 15)
Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control
<p>If you use any of this data, please cite the following publication:</p> <p><span>@inproceedings{monforte2023fast,</span><br><span> title={Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control},</span><br><span> author={Monforte, Marco and Gava, Luna and Iacono, Massimiliano and Glover, Arren and Bartolozzi, Chiara},</span><br><span> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},</span><br><span> pages={4035--4043},</span><br><span> year={2023}</span><br><span>}</span></p> <p>Event-based datasets of synthetic and real trajectories of a bouncing ball.</p> <p>The synthetic trajectories were obtained converting frames taken using Unreal Engine to events. The ground truth is provided along with objects and camera settings.</p> <p>The real trajectories wer dumped from a real event camera located in front of the robot workspace.</p> <p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p> <p>Specifically use the functions to import .log files:</p> <p>data = importIitYarpBinaryDataLog(filePathOrName=input_path)<br> </p> <p> </p> <p> </p>
Fast Indoor Radio Propagation Prediction Using Deep-Learning Dataset
<p>We show a dataset composed by Radio Maps Estimation (RME) and Cells Maps Estimation (CME) for the 5GHz band WIFI in indoor scenarios: it has 60 indoor constructions plans and 1000 distributions initially for a training process and 20 indoor constructions plans and 50 distributions aditionals for a test process of access points to even construction. These distributions are random and several WLAN's structures: 1 to 5 access points.</p> <p>The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.</p> <p>Every coverage map have like maximum power delivered is <em>Pr = Pt = 26</em> dBm (according to data from <a href="https://www.cisco.com/c/en/us/products/wireless/catalyst-9100ax-access-points/index.html">current commercial equipment</a>) and like minimum power a value noise established in <em>Pr = KTB</em>, where K is the Boltzmann's constant, T the enviroment temperatura equal to 290°K and B the band width equal to 80MHz.</p> <p>Dataset DeepFIRP is the result of a lot of simulations by a <a href="https://doi.org/10.5281/zenodo.7983595">own software developed in MATLAB</a> that work with the<a href="https://mentor.ieee.org/802.11/dcn/14/11-14-0882-04-00ax-tgax-channel-model-document.docx"> IEEE 802.11ax channel model</a>.</p> <p>The pictures have a depth of 8 bits and size of <em>256pixels X 256pixels</em> equivalents to indoor constructions of <em>20 X 20</em> m<sup>2</sup>. These ones make reference to offices's spaces at general or classroom. </p> <p>A application to this dataset and the codes used for generate it is found <a href="https://github.com/johanflorez98/Fast-Indoor-Radio-Propagation-Prediction-Using-Deep-Learning">here</a>, where we implement a U-Net model for theRME and CME in indoor enviroments. This investigation contribute in novels methods for estimate by fast way coverage and cells maps using deep-learning in comparation with the conventional phisics methods like dominath-path model or ray-tracing. Whats allows save a lot of amount time in the WLANs's designs.</p>
Thermal video of experiment to determine how fast different brands of towel dry under realistic conditions
<p>These four video files contain the raw measurements of the experiment described in this publication:</p> <p><a href="https://zenodo.org/badge/latestdoi/668661321">Measuring how fast towels dry using an infra-red camera</a></p> <p>In these experiments, I measured the surface temperature of wet and dry towels using a thermal camera to determine which brand of towels dries fastest. This was done on the request of the TV program "Kassa". The analyses of the data and the results can be found on the link above. </p>
Deep and fast label-free Dynamic Organellar Mapping - Imaging data
<p>Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei.</p> <p>Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.</p>
fast-SMT movies of H2B-Halo neurons
<p>Fast single molecule tracking experiments performed on primary cortical neurons infected with a lentiviral vector expressing Halo-tagged H2B.</p>
fast-SMT movies of WT-MeCP2-Halo neurons
<p>Fast single molecule tracking experiments performed on primary cortical neurons with a wild type Mecp2 allele endogenously tagged with a HaloTag.</p>
fast-SMT movies of Halo-NLS neurons
<p>Fast single molecule tracking experiments performed on primary cortical neurons infected with a lentiviral vector expressing Halo-tagged NLS.</p>
fast-SMT movies of G118E-MeCP2-Halo neurons
<p>Fast single molecule tracking experiments performed on primary cortical neurons with a G118E Mecp2 allele endogenously tagged with a HaloTag.</p>
Fast Track Module Based Colonoscopy Training Programme for Surgical Trainees at North Jutland Surgical Departments
ClinicalTrials.gov study NCT02425657. IPD Sharing: NO. Countries: 1. Publications: 4.
FAST MRI Study in Breast Cancer Survivors
ClinicalTrials.gov study NCT02244593. IPD Sharing: YES. Countries: 1. Publications: 1.
Super-fast 3T Prostate MRI Using High Gradient Strength and Deep Learning
ClinicalTrials.gov study NCT06244680. IPD Sharing: NO. Countries: 1. Publications: 16.
Secondary Analysis of Gut Hormones and Inflammatory Cytokines in Fasting Subjects
ClinicalTrials.gov study NCT01257841. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Fast Track to Fertility
ClinicalTrials.gov study NCT07154888. IPD Sharing: YES. Countries: 1. Publications: 11.
Fast-Track in Cardiac Anesthesia
ClinicalTrials.gov study NCT05321173. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Ultrasound Assessment of Gastric Residual Volume in Children Scheduled for Elective Surgery After Clear Fluids Fasting for One Versus Two Hours: a Comparative Study
ClinicalTrials.gov study NCT04228497. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
O-SEMA-Fast: A Research Study to Understand How Oral Semaglutide Works in People With Type 2 Diabetes Who Plan to Fast During Ramadan in the United Arab Emirates, Saudi Arabia, and Kuwait
ClinicalTrials.gov study NCT05716724. IPD Sharing: YES. Countries: 3. Publications: 1.
ScienceDex guides
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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.