cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.933939
title: Raw seawater temperature, conductivity and salinity obtained at different depths by CTD buoy 2019O7 as part of the MOSAiC Distributed Network

description: A set of eight ice-tethered buoy systems (2019O1 to 2019O8) were deployed by the Akademik Fedorov in the Northern Laptev Sea in early October 2019 as part of the MOSAiC Distributed Network. Each buoy consisted of 5 Seabird SBE37IMP Microcat CTDs mounted along an inductive modem tether at depths of 10, 20, 50, 75 and 100m. The buoys were installed on stable sea ice floes (designated as “M-sites”) at a distance of 15-35 km around the main MOSAiC ice camp, and co-located with Snow Buoys, Ice Mass Balance Buoys and D-TOP ocean profilers. The individual instruments were programmed to record oceanographic data internally at 2-minute intervals. The surface unit of the buoy prompted the instruments for an additional measurement every 10 minutes, which was then transmitted to a base station via iridium along with GPS position and time, as well as surface temperature. After a several months long drift through the Central Arctic Ocean, 4 out of 8 buoys were recovered in August 2020, and the internally recorded data from the CTDs were secured. The attached zip archive comprises the unprocessed 10-minute data transmitted by buoy 2019O7 (.txt file). Since this buoy was not recovered, additional data from the individual CTDs is not available. A processed and quality controlled version of this dataset will be supplemented and linked to upon completion. A link to a data paper describing the processing will be given below.
description: Acknowledgments: The data were produced as part of the international Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) with the tag MOSAiC20192020 (grant numbers AWI_PS122_00 and AFMOSAiC-1_00). The instruments were funded through the MIDO (Multidisciplinary Ice-based Drifting Observatory) infrastructure program, and built by Pacific Gyre, USA. We thank the crews of the research vessels Akademik Fedorov, Akademik Tryoshnikov and RV Polarstern, and in particular the chief scientists, Thomas Krumpen and Markus Rex. We thank the helicopter company Naryan-Marsky for their great logistical support that made this effort possible. We express our gratitude to all participants of these expeditions, and particularly to the MOSAiC School for their field assistance during the set-up.
DOI: 10.5281/zenodo.4974284
title: Data Set: fMRI Meta-Analyses

description: This data set is the result of a systematic search in <strong>PubMed </strong>and <strong>APA PsycINFO</strong> for <strong>fMRI meta-analyses.</strong> These records would be suitable for: meta-meta analysis on fMRI meta-analyses research questions regarding neuroimaging meta-analysis methodology (especially for those interested in coordination-based meta-analyses (CBMA): activation likelihood estimation (ALE) using GingerALE software, multi-level kernel density analysis (MKDA), seed-based d mapping (SDM) or image-based meta-analyses. <strong>NOTE: These data are the raw search results from PubMed and PsycINFO and have been deduplicated but have NOT been screened for any inclusion criteria. This means you may find records in these results that are not, in fact, meta-analyses but still have the search terms (below) present in the title or abstract of the paper. </strong> The data set is available in three formats: .csv, .ris, and a Zotero shared library <strong>Search documentation</strong>: Search Date: May 21, 2021 Conducted by: Meghan Testerman, Behavioral Sciences Librarian, Princeton University, mtesterman@princeton.edu PubMed: 433 records identified PubMed Search query (exact): (meta-analysis[Title]) AND (fMRI[Title/Abstract]) PsycINFO: 289 records identified PsycINFO Search Query (exact): (TI meta-analysis) AND (TI fMRI OR AB fMRI) <br> <strong>Total Results</strong> Pubmed (433) + PsycINFO (289) = 722 Deduplicates removed: 234 Unique records: 488
DOI: 10.5281/zenodo.6630966
title: CoAID dataset with multiple extracted features (both sparse and dense) and degraded by OCR

description: This is the same datasets as: Guillaume Bernard. (2022). CoAID dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630405 But with texts degraded by OCR as described in: Guillaume Bernard. (2022). CoAID dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630710
DOI: 10.1594/pangaea.924943
title: Concentration of chlorophyll a in microlayer in autumn
DOI: 10.5281/zenodo.5744737
title: Truck Image Dataset

description: Collection of truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github. The dataset includes 725 cropped images of trucks, taken with three different cameras, on five different locations. 725 images Format: JPG Resolution: 1920xVarious, 96dpi, 24bits Naming pattern: &lt;video_name&gt;_&lt;color|gray&gt;-&lt;Region_of_Interest_ID&gt;-&lt;truck_ID&gt;.jpg If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it! Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." <em>arXiv preprint arXiv:2204.01868</em> (2022).
DOI: 10.1594/pangaea.602436
title: Bathymetry grids and maps of Mozambique Basin compiled from cruise SO183 bathymetry

description: Based on data from R/V Sonne multibeam sonar surveys in 2005 a high resolution bathymetry was generated for the Mozambique Basin. The area covers approx. 466,475 sqkm. The mapping area is divided into four sheets with boundaries (west/east/south/north): Sheet I (north-west), 37:00/39:45/-24:00/-20:20; Sheet II (north-east), 39:45/42:30/-24:00/-20:20; Sheet III (south-west), 37:00/39:45/-27:40/-24:00; Sheet IV (south-east), 39:45/42:30/-27:40/-24:00. Basic outcome of the investigation are Digital Terrain Models (DTM), one for each sheet with 0.05 arcmin (~91 meter) grid spacing and one for the entire area with 0.1 arcmin grid spacing. The DTM's were utilized for contouring and generating maps. Moreover the measured bathymetry was combined and compared with GEBCO bathymetry and predicted bathymetry, derived from altimeter satellites. The provided maps have a paper size of DIN A0 (1188.9 x 841 mm).
DOI: 10.1594/pangaea.919574
title: China global Land Surface Air Temperature, version 2.0

description: The updated C-LSAT2.0 includes monthly mean SAT , daily maximum and minimum temperature anomaly data over global land areas. Following are the description for them:tavg_anomaly: mean land surface temperature are stored as temperature anomalies in degrees celsius. Grids are 5x5deg monthly between 1850-2019. latitude ranges from -87.5 to 87.5 north degrees, longitude ranges from -177.5 to 177.5 east degrees.tmax_anomaly: maximum land surface temperature are stored as temperature anomalies in degrees celsius. Grids are 5x5deg monthly between 1900-2019. latitude ranges from -87.5 to 87.5 north degrees, longitude ranges from -177.5 to 177.5 east degrees.tmin_anomaly: minimum land surface temperature are stored as temperature anomalies in degrees celsius. Grids are 5x5deg monthly between 1900-2019. latitude ranges from -87.5 to 87.5 north degrees, longitude ranges from -177.5 to 177.5 east degrees.
DOI: 10.5281/zenodo.3903436
title: Galactic Plane Infrared Polarization Survey: Data Release 4

description: The Galactic Plane Infrared Polarization Survey (GPIPS) used the Mimir near-infrared instrument to survey stellar linear polarization in the H-band (1.6 microns) from Galactic longitude 18 to 56 degrees and latitudes -1 to +1 degrees as 3,237 individual pointings of the 10x10 arcmin field of view of the instrument. This file presents values and uncertainties for Mimir H-band photometry and linear polarimetry for over 13 million stars across this portion of the northern Milky Way. Each of the 13,861,329 stars represented in the file has new H-band photometry reported. Of these, 8,486,028 have linear polarimetry reported. In addition, matching Gaia, 2MASS, GLIMPSE, and WISE matching photometry, parallax, and proper motion values are listed for the tabulated stars. Details regarding the data, data processing, and contents of the file contents may be found in the Astrophysical Journal Supplements article Clemens et al. (2020; DOI 10.3847/1538-4365/ab9f30).
description: The GPIPS project has been made possible by grants AST 06-07500, 09-07790, 14-12269, and 18-14531 from NSF/MPS to Boston University. v1.1 updates GAIA DR2 designations and g-band uncertainties.
DOI: 10.5281/zenodo.3653403
title: wine recommendations dataset crafted by human wine experts

description: wine recommendation dataset crafted by human wine experts
DOI: 10.5281/zenodo.4263268
title: Top 50 World Padel Tour Dataset

description: Este data set contiene información de los 100 mejores jugadores de padel actuales del circuito World Padel Tour: los 50 mejores del ranking masculino y las 50 mejores del ranking femenino. El dataset contiene tanto características personales de los jugadores (nombre completo, altura, fecha de nacimiento. . . ) como deportivas, incluyendo tanto datos relativos a su carrera profesional completa como resultados de competición más destacables obtenidos en los años más recientes (entre 2013 y 2020)
DOI: 10.5281/zenodo.7693853
title: A long-term (1981-2020) 1-km daily extreme and mean near surface air temperature product over Yellow River Basin of China

description: The dataset includes the semless 1-km daily extreme and mean near surface air temperature products over Yellow River Basin of China. The first version is from 1 January 1981 to 31 Decmber 1990.
DOI: 10.5281/zenodo.7696151
title: A novel R744 multi-temperature cycle for refrigerated transport applications with low-temperature ejector: experimental ejector characterization and thermodynamic cycle assessment - Ejector experimental data

description: Experimental data obtained during the characterization of a R744 ejector in low-temperature suction operating conditions. The complete description of the experimental setup and of the R744 cooling unit concept designed to employ the tested ejector, as well as the discussion on the experimental data, are available in: Fabris, F., Pardiñas, Á. Á., Marinetti, S., Rossetti, A., Hafner, A., Minetto, S. (2023). A novel R744 multi-temperature cycle for refrigerated transport applications with low-temperature ejector: experimental ejector characterization and thermodynamic cycle assessment. International Journal of Refrigeration.
DOI: 10.5281/zenodo.2548493
title: Fluorescence Microscopy Data for Cellular Detection using Object Detection Networks.

description: This data accompanies work from the paper entitled: 

<strong>Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis.</strong>

Further details of these datasets can be found in the methods section of the above paper.

<strong>Erythroblast DAPI (+GlyphorinA):</strong> Erythroblast cells were stained with DAPI and for glycophorin A protein. Cells were stained with CD235a antibody (JC159 clone from Dako) and with Alexa Fluor 488 secondary antibody from Invitrogen. DAPI staining was performed through using VectorShield Hard Set mounting solution with DAPI (Vector Lab). Num of images used for training: 80 and testing: 80. Average number of cells per image: 4.5.

<strong>Neuroblastoma phalloidin (+DAPI): </strong>Images of Neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [25]. The images were stained for FITC-phalloidin and DAPI. Images were acquired on a Zeiss Aviovert 200 microscope with filters for DAPI and FITC. Num of images used for training: 180, testing: 180. Average number of cells per image: 11.7.

<strong>Fibroblast Nucleopore</strong>: Fibroblast (GM5756T) cells were stained for Nucleopore protein. Staining was performed using Anti-Nup153 mouse antibody (Abcam) and counterstained using Alex Fluor 488 (anti-mouse). Num of images for training: 26 and testing: 20. Average number of cells per image: 4.8.

<strong>Eukaryote DAPI:</strong> Eukaryote cells were stained with DAPI and fixed and mounted in Vectashield. Num of images for training: 40 and testing: 40. Average number of cells per image: 8.9.

<strong>C127 DAPI:</strong> C127 cells were stained with DAPI and fixed and mounted in Vectashield. Num of images for training: 30 and testing: 30. Average number of cells per image: 7.1.

<strong>Hela peroxisome</strong>: Hela cells expressing peroxisome localised SCP2-GFP protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localisation signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes. Cells were fixed and mounted.  Num of images for training: 55 and testing: 55. Average number of cells per image: 7.9.

<strong>Dataset Annotation</strong>

Datasets were annotated by a skilled user.  Bounding boxes were drawn to encapsulate the various staining of the cells. The dataset labels were then converted into a format compatible both with Faster-RCNN (Pascal) and with YOLOv2.  T
DOI: 10.5281/zenodo.3967879
title: Automotive Fleet Vehicle Data

description: A synthetic research dataset of vehicle data across a fleet of vehicles, including a range of vehicles and drivers and driving conditions. The dataset contains a range of vehicle events, including vehicle telemetry, actuation events (engine start/stop, door lock/unlock, etc.). Vehicle instrumentation is derived from the OpenXC vehicle dynamics model.
DOI: 10.5281/zenodo.815657
title: Chokepoint Dataset

description: The ChokePoint dataset is designed for experiments in person identification/verification under real-world surveillance conditions using existing technologies. An array of three cameras was placed above several portals (natural choke points in terms of pedestrian traffic) to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images (ie. a face set) can be captured. Faces in such sets will have variations in terms of illumination conditions, pose, sharpness, as well as misalignment due to automatic face localisation/detection. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal.

The dataset consists of 25 subjects (19 male and 6 female) in portal 1 and 29 subjects (23 male and 6 female) in portal 2. The recording of portal 1 and portal 2 are one month apart. The dataset has frame rate of 30 fps and the image resolution is 800X600 pixels. In total, the dataset consists of 48 video sequences and 64,204 face images. In all sequences, only one subject is presented in the image at a time. The first 100 frames of each sequence are for background modelling where no foreground objects were presented.

Each sequence was named according to the recording conditions (eg. P2E_S1_C3) where P, S, and C stand for portal, sequence and camera, respectively. E and L indicate subjects either entering or leaving the portal. The numbers indicate the respective portal, sequence and camera label. For example, P2L_S1_C3 indicates that the recording was done in Portal 2, with people leaving the portal, and captured by camera 3 in the first recorded sequence.

To pose a more challenging real-world surveillance problems, two seqeunces (P2E_S5 and P2L_S5) were recorded with crowded scenario. In additional to the aforementioned variations, the sequences were presented with continuous occlusion. This phenomenon presents challenges in identidy tracking and face verification.

This dataset can be applied, but not limited, to the following research areas:


	person re-identification
	image set matching
	face quality measurement
	face clustering
	3D face reconstruction
	pedestrian/face tracking
	background estimation and subtraction


Please cite the following paper if you use the ChokePoint dataset in your work (papers, articles, reports, books, software, etc):


	Y. Wong, S. Chen, S. Mau, C. Sanderson, B.C. Lovell<br>
	<strong>Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition </strong><br>
	<em>IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops</em>, pages 81-88, 2011.<br>
	http://doi.org/10.1109/CVPRW.2011.5981881
DOI: 10.1594/pangaea.78884
title: Chlorophyll-a at UR03_Adria1
DOI: 10.5281/zenodo.6833446
title: Deep Learning-based Method for Automatic Resolution of GC-MS Data from Complex Samples

description: 气相色谱-质谱;多元曲线分辨率
DOI: 10.5281/zenodo.7556020
title: Raw strain and temperature data for a dump truck, IFV, and semi-truck

description: Horizontal strain measurements for a dump truck (D), an IFV (K), and a semi-truck (L). The measurements were taken from DTU smart road, which consists of four sections, each with six horizontal strain gauges placed at the bottom of the AC (150 mm depth). There is a reinforcement grid in section 1, 2, and 3. The data was taken as a part of the Master thesis: Investigation of asphalt pavements loaded by heavy off-road vehicles. The thesis contains further details.
DOI: 10.5281/zenodo.4624096
title: Data from: Politics overwhelms science in the Covid-19 pandemic: evidence from the whole coverage of the Italian quality newspapers

description: "SARS-CoV-2 general corpus" includes Italian newspaper articles published in the timespan between January 1, 2020 and June 15, 2020, containing at least one of the following terms: [covid, corona virus, OR coronavirus]. Sources: Corriere della Sera, La Repubblica, Il Sole – 24 Ore, La Stampa, Avvenire, Il Giornale, Il Mattino di Napoli, Il Messaggero.
DOI: 10.5281/zenodo.4837560
title: Equine Influenza dataset

description: Data used by Melina Ribaud, Edith Gabriel, Joseph Hughes, Samuel Soubeyrand (Identifying potential significant factors impacting zero-inflated proportions data. 2021. &lt;hal-02936779v3&gt;) for studying factors impacting Equine Influenza outbreak in 2003 in race horses. Data are provided in one tab-delimited text files (EquineDiffFactors.txt) and the format is described in the file data-description.pdf.
DOI: 10.5281/zenodo.7602602
title: Infection safe workplace IoT sensor measurements

description: This dataset has been collected and created by "Datu Tehnoloģiju Grupa" and Riga Technical University. This dataset contains data collected from IoT devices scattered in the office workplace. The measurements have been collected in a .csv file, with 150000 records. Project “Platform for the Covid-19 safe work environment” (ID. 1.1.1.1/21/A/011) is founded by European Regional Development Fund specific objective 1.1.1 «Improve research and innovation capacity and the ability of Latvian research institutions to attract external funding, by investing in human capital and infra-structure». The project is co-financed by REACT-EU funding for mitigating the consequences of the pan-demic crisis.
DOI: 10.5281/zenodo.6000557
title: mushrooms

description: Dataset mushrooms
DOI: 10.5281/zenodo.44870
title: Triangular Mesh Of The Brain Of A Nile Crocodile (Crocodylus Niloticus)

description: Triangular Mesh of the Brain of a Nile Crocodile (<i>Crocodylus niloticus</i>) from http://braincatalogue.org/Nile_crocodile
DOI: 10.6084/m9.figshare.27193146
title: SFS files for C. sulphurea subspecies and C. galerita, C. ducorpsii, C. sanguinea, C. tenuirostris used to calculate genetic distance.

description: SFS files for C. sulphurea subspecies and C. galerita, C. ducorpsii, C. sanguinea, C. tenuirostris used to calculate genetic distance.
DOI: 10.6084/m9.figshare.27193146.v1
title: SFS files for C. sulphurea subspecies and C. galerita, C. ducorpsii, C. sanguinea, C. tenuirostris used to calculate genetic distance.

description: SFS files for C. sulphurea subspecies and C. galerita, C. ducorpsii, C. sanguinea, C. tenuirostris used to calculate genetic distance.
DOI: 10.5281/zenodo.6827095
title: The spatiotemporal data

description: The file "2000-2020" is the RSEI data in the study area in the past 20 years. The file "patch" is the plots of farmland consolidation.
DOI: 10.5281/zenodo.4092826
title: Dataset for the manuscript "Potential Seismicity by Impoundment of the Baihetan Reservoir, Xiaojiang Fault Zone, Southwest China"

description: Files corresponding to submitted manuscript "Potential Seismicity by Impoundment of the Baihetan Reservoir, Xiaojiang Fault Zone, Southwest China". Dataset used for the numerical calculation of Baihetan Reservoir. Model parameters, mesh file and the main simulation results are included.
DOI: 10.5281/zenodo.6827094
title: The spatiotemporal data

description: The file "2000-2020" is the RSEI data in the study area in the past 20 years. The file "patch" is the plots of farmland consolidation.
DOI: 10.1594/pangaea.886593
title: Microplastic and environmental data from Arctic sea ice

description: Microplastics (MP) are recognized as a growing environmental hazard and have been identified as far as the remote Polar Regions, with particularly high concentrations of microplastics in sea ice. Little is known regarding the horizontal variability of MP within sea ice and how the underlying water body affects MP composition during sea ice growth. Here we show that sea ice MP has no uniform polymer composition and that, depending on the growth region and drift paths of the sea ice, unique MP patterns can be observed in different sea ice horizons. Thus even in remote regions such as the Arctic Ocean, certain MP indicate the presence of localized sources. Increasing exploitation of Arctic resources will likely lead to a higher MP load in the Arctic sea ice and will enhance the release of MP in the areas of strong seasonal sea ice melt and the outflow gateways.
description: Accompanying data to the publication "Arctic sea ice is an important temporal sink and means of transport for microplastic"Includes data for microplastic concentrations and composition and ancillary data e.g. chlorophyll, POC, T, S.The backtracking of sampled sea ice is given in a shapefile see Further details reference.
description: Supplement to: Peeken, Ilka; Primpke, Sebastian; Beyer, Birte; Guetermann, Julia; Katlein, Christian; Krumpen, Thomas; Bergmann, Melanie; Hehemann, Laura; Gerdts, Gunnar (2018): Arctic sea ice is an important temporal sink and means of transport for microplastic. Nature Communications, 9(1)
DOI: 10.5281/zenodo.6547953
title: BVOCs emission data

description: BVOCs emission data in different synoptic weather patterns