cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18405511
title: SuperDARN data in netCDF format (2006-Oct)

description: 2006-Oct SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6631468
title: SuperDARN data in netCDF format (2006-Oct)

description: 2006-Oct SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15483/3014001
title: Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

description: Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.
DOI: 10.15483/3014002
title: 220EV Box Truck Telemetry Dataset

description: This dataset contains telemetry data for the 520EV refuse trucks accessed through a Veracity data platform as provided by Peterbilt. The dataset contains driving data on both electric trucks used by SWS. Data were recorded at an hourly resolution and contain energy use data while driving and idling, distance driven (miles), and driving speed (miles per hour). This dataset also contains charging data on the fast charger in kilowatt-hours for each hour.
DOI: 10.15483/3014003
title: ChargePoint Level 2 Charging Data

description: This dataset contains charging data from the SWS Level 2 charger. This charger is exclusively used to charge the 220EV box truck. Data fields include charging duration (HH:MM:SS) and total energy charged during a session.
DOI: 10.15483/3014004
title: SWS Diesel Truck Fueling Data 

description: This dataset contains fueling data for two diesel trucks operated by SWS, provided by SWS. Data fields include date and time of fueling, total amount of fuel, odometer reading at the time of fueling, and total cost of fueling.
DOI: 10.15483/3014717
title: SWS Building Electric Demand Profile

description: This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
DOI: 10.5061/dryad.v6wwpzh3w
title: Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery

description: Many applications in machine vision and medical imaging require the
 capture of images from a scene with very low radiance, which may
 result in very noisy images and videos. An important example of
 such an application is the imaging of fluorescently-labeled
 tissue in fluorescence-guided surgery. Medical imaging systems,
 especially when intended to be used in surgery, are designed to
 operate in well-lit environments and use optical filters,
 time division, or other strategies that allow the simultaneous
 capture of low radiance fluorescence video and a well-lit visible
 light video of the scene. This work demonstrates video
 denoising can be dramatically improved by utilizing deep learning
 together with motion and textural cues from the noise-free video.
description: The following passage from the main article explains the dataset
 capture: "We capture data with a transient lighting-enabled, clinical
 wide-field FGS imaging system (OnLume Surgical, Madison, WI). OnLume’s
 system uses two camera sensors for both the reference and fluorescent
 cameras. In a surgical training model, we inject the near-infrared
 fluorescent agent, ICG, via syringe into the femoral artery of four
 chicken thighs to simulate vascular surgery. We prepared varying doses of
 ICG up to the clinical guidelines of 2.5 mg/mL to generate fluorescent
 videos with visual contrast and low noise that can be treated as ground
 truth and simulate much lower fluorescence. In future work, we would like
 to detect markers that have much fewer photons than our captured
 fluorescent videos. We capture about 100 minutes of simulated surgical
 footage with a variety of motion such as cutting, pulling, squeezing,
 injecting, and working with surgical tools. The 100 minutes of footage is
 broken up into 590 100-frame long videos. The videos are captured at 15
 frames per second at a resolution of 768 × 1024."
description: # Data for OFDVDnet: A Sensor Fusion Approach for Video Denoising in
 Fluorescence-Guided Surgery *Equal contribution from Trevor Seets and Wei
 Lin
 [https://doi.org/10.5061/dryad.v6wwpzh3w](https://doi.org/10.5061/dryad.v6wwpzh3w) ## Description of the data and file structure This dataset contains 50 videos (about 100 total minutes) of mock surgery on a chicken thigh using a the OnLume Avata fluorescence guided surgery (FGS) imaging system. Each video contains a fluorescence (top right), reference (bottom left), and overlay (top left) view. The fluorescence and reference videos are captured by their corresponding cameras on the system whereas the overlay is a false color overlay of the fluorescence on top of the reference video. Each individual video has resolution of 768x1024 and is captured at 15 fps. ## Code/Software Code is available for simulating noisy video and training the neural networks used to generate the results from the main paper. The readme file within the code explains how to use the code and where the data should be placed within the code file structure.
DOI: 10.15468/dl.h9xvf4
title: Occurrence Download

description: A dataset containing 221 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Calocedrus formosana",
 "Year 2001-2026"
 ]
}

 The dataset includes 221 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003936-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zxm2ux
title: Occurrence Download

description: A dataset containing 571 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Petrelaea dana"
 ]
}

 The dataset includes 571 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003937-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.cjsxksndv
title: Centimetre radar reflectance signal for material classification

description: Detecting and classifying materials is crucial for many real-world
 applications such as industrial robots, and precision agriculture. In
 recent years, radar sensor-based material classification become more and
 more popular. In this work, we employ a portable centimeter-wave
 radar sensor to collect the reflectance signal from nine
 different kinds of materials.
description: There are 956 observations in the dataset, categorized into 10
 classes: 1) Air, 2) Beans, 3) Dirt, 4) Flour, 5) Lentils, 6) Oats, 7)
 Rice, 8) River Rocks, 9) Sand, and 10) Sugar. For one
 observation, it contains 8119 data points. 
description: # Centimetre radar reflectance signal for material classification ##
 Description of the data and file structure Nine classes of materials were
 considered in this experiment. The signal from the empty container was
 considered as 'Air'. In total, ten classes of materials were
 used, which are 1) Air, 2) Beans, 3) Dirt, 4) Flour, 5) Lentils, 6) Oats,
 7) Rice, 8) River Rocks, 9) Sand, and 10) Sugar. Each test material is
 placed in 3 containers, and the following procedure is used to collect
 data: 1. Place the cup under the sensor. Make sure the cups are always
 placed in the same spot. This was done by placing the stand and cup on a
 sheet of paper. Trace the position of the stand legs and the cup which is
 centered under the stand 2. Take 40 rounds of data collection for the
 first cup, as one round results in three scans 3. After each round, shake
 the cup to change the material surface. 4. Repeat the same steps from 1-3
 for the second test cup. 5. Sample the third cup for 20 rounds with each
 round resulting in three scans. After each round, shake the cup to change
 the material surface. 6. Repeat steps 1-5 for each material except for the
 empty cup. 7. Scan a single empty cup 15 times with each round also
 performing three scans. ## Description of the Data The data was averaged
 by three to reduce the noise, totally we have 956 observations(Some
 samples may get additional scans). Two file types are provided:
 `AllData.csv` and `AllData.mat`. `AllData.csv` is a table with dimensions
 956 by 8193. Each row represents an observation, with the final column
 containing the label. Conversely, `AllData.mat` is a struct containing the
 data in `AllData.Data` and the labels in `AllData.Labels`.
DOI: 10.15468/dl.jtpwhj
title: Occurrence Download

description: A dataset containing 8102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mammalia, Reptilia, Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1990",
 "Year is less than or equal to 2025",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)",
 "CoordinateUncertaintyInMeters is less than or equal to 500",
 "Geometry POLYGON ((-120.2668 34.82019, -120.1432 34.68484, -120.0301 34.61333, -119.8986 34.56829, -119.8515 34.55977, -119.7408 34.49743, -119.607 34.45778, -119.4654 34.44798, -119.3255 34.46868, -119.251 34.49342, -119.1467 34.43474, -119.0133 34.3944, -118.8719 34.38387, -118.732 34.40384, -118.6029 34.45298, -118.4934 34.52799, -118.4109 34.62381, -118.3808 34.67988, -118.278 34.7507, -118.195 34.84634, -118.1447 34.95639, -118.1306 35.07338, -118.1673 35.26602, -118.1704 35.29585, -118.1639 35.41325, -118.1948 35.52803, -118.2612 35.63232, -118.3587 35.71898, -118.4806 35.78203, -118.6186 35.81712, -118.667 35.82202, -118.6813 36.03752, -118.7489 36.19644, -118.8297 36.29408, -118.9393 36.37141, -119.0701 36.42309, -119.2132 36.44553, -119.3859 36.43766, -119.5259 36.40495, -119.6504 36.34402, -119.7379 36.28321, -119.8757 36.29752, -120.02 36.28225, -120.1007 36.2592, -120.0584 36.35964, -120.047 36.47673, -120.0637 36.60773, -120.06 36.72513, -120.076 36.85609, -120.1251 36.96675, -120.2085 37.0634, -120.3206 37.13942, -120.3459 37.15165, -120.4821 37.19611, -120.6282 37.21076, -120.7741 37.19457, -120.8952 37.15503, -121.036 37.15629, -121.1787 37.12755, -121.307 37.07017, -121.4121 36.98812, -121.4869 36.88707, -121.5262 36.77396, -121.5436 36.65304, -121.5222 36.53686, -121.4643 36.42907, -121.3739 36.33697, -121.3132 36.29298, -121.2421 36.19052, -121.1408 36.10633, -120.9503 36.01422, -120.811 35.98191, -120.61 35.97881, -120.4693 36.00688, -120.3773 36.04097, -120.419 35.9404, -120.4298 35.82324, -120.4028 35.70781, -120.3755 35.65326, -120.4494 35.56312, -120.4968 35.45215, -120.5074 35.33494, -120.4805 35.21949, -120.4181 35.11364, -120.3408 35.03704, -120.3219 34.92867, -120.2668 34.82019))"
 ]
}

 The dataset includes 8102 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003938-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qhbext
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Hemionitis concolor"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003940-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23668/psycharchives.21609
title: Dissociating task selection and response selection in dual task contexts: Evidence from a novel trial-by-trial analysis of temporal overlap between tasks

description: Raw data for a PRP experiment on the interaction between SOA and the task sequence between Task 1 and Task 2. In this experiment, we examined whether switch costs are affected by the SOA.
DOI: 10.5061/dryad.dncjsxm6m
title: Size and isotopic ratios of macroinvertebrates in mangroves with and without seabirds

description: This dataset is used to investigate the trophic-response of mangrove
 invertebrate communities to nutrient subsidies supplied by seabird
 populations. It provides nitrogen (δ15N) and carbon (δ13C) isotopic
 composition of seabird guano, basal food sources (sediment, mangrove
 leaves, epiphytic macroalgae) and macroinvertebrate consumers (littornid
 gastropod, sesarmid crab, portunid crabs), as well as sizes of consumers,
 from mangroves with and without nesting seabirds. The data is used to
 determine the impacts of seabird-derived nutrients on mangrove
 macroinvertebrate sizes and resource contributions.
description: This dataset was collected within the mangrove forests of Aldabra
 Atoll (Seychelles) between November 2022 and February 2023.
 Details for each dataset are provided in the README
 file.
description: # Size and isotopic ratios of macroinvertebrates in mangroves with and
 without seabirds
 [https://doi.org/10.5061/dryad.dncjsxm6m](https://doi.org/10.5061/dryad.dncjsxm6m) The dataset contains the following raw data : Foodweb isotopic ratios (1 file), Gastropod size and isotopic ratios (1 file) and Crab size and isotopic ratios (1 file). ## Description of the data and file structure Sample collection for this dataset occurred at 10 sites (5 seabird, 5 non-seabird sites) on Aldabra Atoll (Seychelles). Mangrove leaves, sediment, epiphytic macroalgae and gastropod samples were collected within 8 quadrats at each site (suffix A-H), while mangrove crabs were sampled randomly at the sites. Raw data files: 1. Foodweb isotopic ratios: foodweb_isotope.csv: isotope values of nitrogen (d15N) and carbon (d13C) and nitrogen (XN) and carbon (XC) of foodweb compartments - mangrove leaves, sediment, epiphytic macroalgae, littorinid gastropod (Littoraria spp.), sesarmid crab (Sesarma leptosoma), portunid crab (Thalamita crenata). d15N and d13C values are in ppm (‰) and XN are in percent (%). Cells containing na = not applicable or no data. 2. Gastropod size and isotopic ratios: * mollusc.csv: shell height, isotope values of nitrogen (d15N) and carbon (d13C) and nitrogen content (XN) of littorinid gastropods. Isotope and nitrogen values are similar to foodweb_isotope.csv. Shell height is in mm. 3. Crab size and isotopic ratios: * crab.csv: carapace width, isotope values of nitrogen (d15N) and carbon (d13C) and nitrogen content (XN) of sesarmid and portunid crabs. Isotope and nitrogen values are similar to foodweb_isotope.csv. Carapace width is in mm. ## Sharing/Access information Data on nitrogen content and nitrogen isotopic ratios of some food web components has been used in this study: Appoo, J., N. Bunbury, S. Jaquemet, and N. A. J. Graham. 2024. “Seabird Nutrient Subsidies Enrich Mangrove Ecosystems and Are Exported to Nearby Coastal Habitats.” *iScience* 27 (4): 109404 * [https://doi.org/10.1016/j.isci.2024.109404](https://doi.org/10.1016/j.isci.2024.109404) The above study is linked to this Dryad dataset * [https://doi.org/10.5061/dryad.0rxwdbs79](https://doi.org/10.5061/dryad.0rxwdbs79)
DOI: 10.5061/dryad.5tb2rbpc4
title: Data underlying the publication: Gut bacteria-derived volatiles mediate the Drosophila melanogaster (Diptera: Drosophilidae) attraction

description: Bacteria-originated volatile molecules play a crucial role in chemical
 communications between insects, representing their promising application
 as odor bait traps in pest control. In this study, we investigated the
 behavioral preferences of the fruit fly Drosophila melanogaster (Diptera:
 Drosophilidae) towards the fermentation broth of seven gut-associated
 bacteria using trap choice assays. All seven bacterial fermentations
 significantly attracted adults and larvae compared to the medium control.
 We assessed the effects of bacterial fermentations on bayberry fruits’
 olfaction attractiveness to fly adults and found that the bayberry fruits
 sprayed with fermentation broth from seven bacteria were all significantly
 more attractive to insects than the non-sprayed fruits, resulting in
 increased egg numbers. We also compared the attractive effect of bacterial
 fermentations with a sugar-vinegar mixture and a commercial odor-bait. The
 commercial odor bait proved more enticing than the unconcentrated 5-day
 fermentation broths. However, out of the seven bacteria, 64-fold
 concentrated bacterial fermentation of Corynebacterium (Actinomycetota
 phylum) was significantly more attractive than commercial bait. Finally,
 we chemically identified the predominant compounds 2-methylpropanal and
 acetaldehyde, which are likely responsible for the behavioral preference
 of fruit flies. Our findings provide a deeper understanding of how gut
 microbes affect insect behavior and offer a potential bacteria-originated
 odor bait for fly control in the orchard.
description: # Data underlying the publication: Gut bacteria-derived volatiles mediate
 the *Drosophila melanogaster* (Diptera: Drosophilidae) attraction
 [https://doi.org/10.5061/dryad.5tb2rbpc4](https://doi.org/10.5061/dryad.5tb2rbpc4) ## Description of the data and file structure We examined the attractiveness of 5th-instar adults and 3rd-instar larvae by behavioural traps and double choice tests in a climate chamber using artificial recordings. Adult choices were identified based on the number of adults entering the trap-choice device after 12 hours. Larval choices were identified based on first substrate contact and the number of larvae present in a substrate over time. Oviposition behaviours were identified based on the number of eggs entering the trap-choice device after 36 hours. ## Sharing/Access information NA ## Code/Software NA
DOI: 10.5061/dryad.w9ghx3fxj
title: A diadectid skin impression and its implications for the evolutionary origin

description: Corneous skin appendages are common and diverse in crown-group amniotes
 but are also present in some modern amphibians. This raises the still
 unresolved question of whether the ability to form corneous skin
 appendages is an apomorphy of a common ancestor of amphibians and amniotes
 or evolved independently in both groups. So far, there is no
 palaeontological contribution to the issue due to the lack of keratin soft
 tissue preservation in Palaeozoic anamniotes. New data is provided by a
 recently discovered ichnofossil specimen from the early Permian of Poland
 that shows monospecific tetrapod footprints associated with a partial
 scaly body impression. The traces can be unambiguously attributed to
 diadectids and are interpreted as the globally first evidence of horned
 scales in tetrapods close to the origin of amniotes. Taking hitherto
 little-noticed scaly skin impressions of lepospondyl stem amniotes from
 the early Permian of Germany into account, the possibility has to be
 considered that the evolutionary origin of epidermal scales deeply roots
 among anamniotes.
description: # A diadectid skin impression and its implications for the evolutionary
 origin The data structured into columns represent a point cloud, carrying
 spatial information and data about the scanned slab surface. The first
 three columns contain information about the location of the points in
 virtual space (local coordinate space) and within the X, Y, and Z axes.
 The following columns describe the color component of the point cloud,
 representing the actual appearance of the slab.