cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21233/t184-rw57
title: Knardijk Km paal 7 pollen dataset

description: Raw data for the Knardijk Km paal 7 pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/fzgv-0s22
title: Gerzensee pollen dataset

description: Raw data for the Gerzensee pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0kcm-fs14
title: Matembe Marsh pollen dataset

description: Raw data for the Matembe Marsh pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/atwf-f630
title: New Island Bone Piles vertebrate fauna dataset

description: Raw data for the New Island Bone Piles vertebrate fauna dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/emwd-gz24
title: Lake Abijata pollen dataset

description: Raw data for the Lake Abijata pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/3wpr-4214
title: Mamontovy Khayata pollen dataset

description: Raw data for the Mamontovy Khayata pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/bw7x-ee65
title: Wengerkopf pollen dataset

description: Raw data for the Wengerkopf pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/kjhv-dj77
title: Camerons Lagoon pollen dataset

description: Raw data for the Camerons Lagoon pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/eh0t-jf02
title: Canelo Hills Cienega charcoal dataset

description: Raw data for the Canelo Hills Cienega charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/t5fh-qp20
title: Zijderveld pollen dataset

description: Raw data for the Zijderveld pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/qp5k-v939
title: Tamsaa Swamp pollen dataset

description: Raw data for the Tamsaa Swamp pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/me08-9d95
title: Mamontovy Khayata pollen dataset

description: Raw data for the Mamontovy Khayata pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ee50-yb03
title: Lake Sinnda pollen dataset

description: Raw data for the Lake Sinnda pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21227/w9k7-ed68
title: A Multivariable Controller in Synchronous Frame Integrating Phase-Locked Loop to Enhance Performance of Three-Phase Grid-Connected Inverters in Weak Grids: Controller Design and Simulation Files

description: These controller design and simulation files correspond to the paper "A Multivariable Controller in Synchronous Frame Integrating Phase-Locked Loop to Enhance Performance of Three-Phase Grid-connected Inverters in Weak Grids" which presents a new current controller in the synchronous reference frame and its associated design for enhancing the performance of three-phase grid-connected inverters, especially against weak-grid conditions. The existing controllers do not perform strongly during high-impedance grid conditions and lead to oscillations and instability issues due to the interactions between the synchronization and control units. The proposed controller addresses this issue by 1) deriving a linear model of the three-phase phase-locked loop (PLL), 2) integrating the PLL model into the current controller design, 3) using a multivariable control design for multi-input multioutput systems, and 4) designing the controller gains using optimal linear quadratic theory. The proposed controller has superior performance over a substantially wider range of weakgrid conditions compared to conventional controllers. Extensive simulation and experimental results are presented in order to validate and reveal the desirable performance of the proposedcontroller.
DOI: 10.21233/s0hr-1189
title: Land van Vollenhove pollen dataset

description: Raw data for the Land van Vollenhove pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/jhw3-md34
title: Caño Tigre pollen dataset

description: Raw data for the Caño Tigre pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/wy0w-m060
title: New Island Bone Piles vertebrate fauna dataset

description: Raw data for the New Island Bone Piles vertebrate fauna dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/bjgc-8t48
title: Akotivika pollen dataset

description: Raw data for the Akotivika pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/zdaz-b720
title: Labaz lake pollen dataset

description: Raw data for the Labaz lake pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hfx7-fn23
title: Schut- en Grafkampen pollen dataset

description: Raw data for the Schut- en Grafkampen pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.5281/zenodo.5048538
title: COAWST and WRF-only simulated tracks and SST for TCs making landfall in South China

description: This dataset is associated with the following papers: Lok et al. (2021): Importance of Air-Sea Coupling in Simulating Tropical Cyclone Intensity at Landfall,<em> accepted by Advances in Atmospheric Sciences</em> Lok and Chan (2018): Simulating seasonal tropical cyclone intensities at landfall along the South China coast,<br> <em>Climate Dynamics</em>, <strong>50(1-2)</strong>: 2661–2672 Details of the files available in this dataset: sst_cfsr-wrf.zip - Sea surface temperature from CFSR as seen by WRF in Lok and Chan (2018) sst_cwst.zip - Sea surface temperature simulated by COAWST in Lok et al. (2021) tc_track_wrf.zip - Simulated tropical cyclone tracks of Lok and Chan (2018) tc_track_cwst.zip - Simulated tropical cyclone tracks of Lok et al. (2021)
DOI: 10.5281/zenodo.5048537
title: COAWST and WRF-only simulated tracks and SST for TCs making landfall in South China

description: This dataset is associated with the following papers: Lok et al. (2021): Importance of Air-Sea Coupling in Simulating Tropical Cyclone Intensity at Landfall,<em> accepted by Advances in Atmospheric Sciences</em> Lok and Chan (2018): Simulating seasonal tropical cyclone intensities at landfall along the South China coast,<br> <em>Climate Dynamics</em>, <strong>50(1-2)</strong>: 2661–2672 Details of the files available in this dataset: sst_cfsr-wrf.zip - Sea surface temperature from CFSR as seen by WRF in Lok and Chan (2018) sst_cwst.zip - Sea surface temperature simulated by COAWST in Lok et al. (2021) tc_track_wrf.zip - Simulated tropical cyclone tracks of Lok and Chan (2018) tc_track_cwst.zip - Simulated tropical cyclone tracks of Lok et al. (2021)
DOI: 10.21233/96b5-e133
title: Torbiera di Alice pollen dataset

description: Raw data for the Torbiera di Alice pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.13139/ornlnccs/1841347
title: Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

description: This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called "training" and "validation". The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated ("el") or at ground-level ("gl"), and {timeofday} is a two-letter string indicating whether the image was captured during daytime ("dt") or nighttime ("nt").
DOI: 10.21233/91fs-0y05
title: Lomonosovfonna pollen dataset

description: Raw data for the Lomonosovfonna pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/cy05-x304
title: Venhuizen Elbaweg pollen dataset

description: Raw data for the Venhuizen Elbaweg pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/6n9j-8t69
title: Biddinghuizen pollen dataset

description: Raw data for the Biddinghuizen pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hps0-4576
title: Trinity Bay dinoflagellates dataset

description: Raw data for the Trinity Bay dinoflagellates dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ne9m-ny59
title: Enkhuizen Vijzeltuin plant macrofossil dataset

description: Raw data for the Enkhuizen Vijzeltuin plant macrofossil dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/efvb-nw10
title: Noordoostpolder kavel A3 pollen dataset

description: Raw data for the Noordoostpolder kavel A3 pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/xe80-vy98
title: Pretoria Saltpan pollen dataset

description: Raw data for the Pretoria Saltpan pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/91sc-5s52
title: Scot pollen dataset

description: Raw data for the Scot pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.19530496.v1
title: LfPLV_shared_data

description: PDBs and alignment file of LfPLV study by Stuehler Jr. et. al.
DOI: 10.6084/m9.figshare.19530532
title: Subduction erosion revealed by Late Mesozoic magmatism in the Gangdese arc, South Tibet

description: The supporting information includes the Analytical methods (Text S1), Figure S1 of LA-ICP-MS zircon U-Pb concordia diagrams with cathodoluminescence images in this study, Figure S2 of <sup>87</sup>Sr/<sup>86</sup>Sr<sub>(i)</sub> versus ε <sub>Nd</sub>(t) diagram for Mesozoic mantle-derived magma, Figure S3 of Geochemical diagrams for Late Cretaceous and pre-Late Cretaceous subduction-related adakitic rocks, Figure S4 of quantitative calculation of juvenile crust generation rate and tectonic erosion rate from Zedong and nearby area, and Tables S1-S8 of the analytical results.
DOI: 10.5281/zenodo.6420002
title: Dataset for ammonium nitrate study

description: Dataset for ammonium nitrate study.
DOI: 10.5281/zenodo.6420001
title: Dataset for ammonium nitrate study

description: Dataset for ammonium nitrate study.
DOI: 10.25606/surf.e0e251ca21361dce
title: The LOFAR Two-metre Sky Survey (LoTSS) V. Second data release (P001+33)

description: In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) we present 120-168 MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44◦30′ and 1h00m +28◦ 00′ and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451 hrs (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4,396,228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6′′ resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144 MHz have: a median rms sensitivity of 83 μ Jy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2′′; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8 mJy/beam. By creating three 16 MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of &gt; ±0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20′′ resolution 120-168 MHz continuum images have a median rms sensitivity of 95 μ Jy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at 4′ and 2.2mJy/beam at 20′′; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset. This is field P001+33.
DOI: 10.25606/surf.a358184ac4ff93f6
title: The LOFAR Two-metre Sky Survey (LoTSS) V. Second data release (P42Hetdex07)

description: In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) we present 120-168 MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44◦30′ and 1h00m +28◦ 00′ and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451 hrs (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4,396,228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6′′ resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144 MHz have: a median rms sensitivity of 83 μ Jy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2′′; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8 mJy/beam. By creating three 16 MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of &gt; ±0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20′′ resolution 120-168 MHz continuum images have a median rms sensitivity of 95 μ Jy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at 4′ and 2.2mJy/beam at 20′′; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset. This is field P42Hetdex07.
DOI: 10.25606/surf.a89b6f892011d51a
title: The LOFAR Two-metre Sky Survey (LoTSS) V. Second data release (P000+36)

description: In this data release from the ongoing LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) we present 120-168 MHz images covering 27% of the northern sky. Our coverage is split into two regions centred at approximately 12h45m +44◦30′ and 1h00m +28◦ 00′ and spanning 4178 and 1457 square degrees respectively. The images were derived from 3,451 hrs (7.6 PB) of LOFAR High Band Antenna data which were corrected for the direction-independent instrumental properties as well as direction-dependent ionospheric distortions during extensive, but fully automated, data processing. A catalogue of 4,396,228 radio sources is derived from our total intensity (Stokes I) maps, where the majority of these have never been detected at radio wavelengths before. At 6′′ resolution, our full bandwidth Stokes I continuum maps with a central frequency of 144 MHz have: a median rms sensitivity of 83 μ Jy/beam; a flux density scale accuracy of approximately 10%; an astrometric accuracy of 0.2′′; and we estimate the point-source completeness to be 90% at a peak brightness of 0.8 mJy/beam. By creating three 16 MHz bandwidth images across the band we are able to measure the in-band spectral index of many sources, albeit with an error on the derived spectral index of &gt; ±0.2 which is a consequence of our flux-density scale accuracy and small fractional bandwidth. Our circular polarisation (Stokes V) 20′′ resolution 120-168 MHz continuum images have a median rms sensitivity of 95 μ Jy/beam, and we estimate a Stokes I to Stokes V leakage of 0.056%. Our linear polarisation (Stokes Q and Stokes U) image cubes consist of 480 97.6 kHz wide planes and have a median rms sensitivity per plane of 10.8mJy/beam at 4′ and 2.2mJy/beam at 20′′; we estimate the Stokes I to Stokes Q/U leakage to be approximately 0.2%. Here we characterise and publicly release our Stokes I, Q, U and V images in addition to the calibrated uv-data to facilitate the thorough scientific exploitation of this unique dataset. This is field P000+36.
DOI: 10.6084/m9.figshare.19530528
title: ARcode: HPC Application Recognition Through Image-encoded Monitoring Data

description: This tar file contains the docker image for building the ARcode model and baseline models for application recognition for the SC22 paper with the same title.<br>The files/folders in this image contains:<br>notebooks: The notebooks for models and experiment results.-- ARcode.ipynb: The interactive Jupyter Notebook for the ARcode model.-- ARcode_unknown.ipynb: The interactive Jupyter Notebook for the ARcode model for detecting unknown applications.-- ARcode_partial.ipynb: The interactive Jupyter Notebook for the ARcode model on partial job signatures.-- ARcode_channel.ipynb: The interactive Jupyter Notebook for the ARcode model on one channel of job signatures.-- baselines.ipynb: The interactive Jupyter Notebook for the baseline models. These models are Random Forest, LinearSVC and SVC; all of them are implemented through Taxonomist(https://doi.org/10.6084/m9.figshare.6384248.v1).-- baselines_unknown.ipynb: The interactive Jupyter Notebook for the baseline models for detecting unknown applications.<br>dataset: The dataset for training the models mentioned above.-- ARcode_labels.npy: A numpy array of the signatures' labels.-- ARcode_signatures.npy: A numpy array of the generated signatures.-- baseline_labels.npy: A numpy array of the labels for the baseline dataset.-- baseline_features.npy: A numpy array of the statistic features generated from the raw monitoring data.-- knl_app_code.json: Mapping of IDs to application names. This mapping is used when creating the dataset.<br>models: The saved models.-- arcode.h5: An HDF5 file containing the serialized weights for the ARcode model.-- arcode.json: A JSON file describing the ARcode model.<br>results: The saved experiment results.<br>Following these steps to start Jupyter Notebook in the image:<br>1. Load the image into Docker on your local machine:docker load &lt; archive-arcode.tar<br>2. Start the Jupyter notebook in the docker image:docker run --init --user root -p 8888:8888 artlands/arcode<br>3. Copy the URL shown in your terminal and paste in a brower: http://127.0.0.1:8888/?token=your_token<br>Acknowledgement: This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231
DOI: 10.6084/m9.figshare.19530550
title: Raw Source data NMICROBIOL-21123138A

description: Source data for figures in Source data NMICROBIOL-21123138A
DOI: 10.6084/m9.figshare.19530550.v1
title: Raw Source data NMICROBIOL-21123138A

description: Source data for figures in Source data NMICROBIOL-21123138A
DOI: 10.21233/8ka2-8k68
title: Voordrag pollen dataset

description: Raw data for the Voordrag pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/2bdh-nh61
title: Garba Guracha Lake pollen dataset

description: Raw data for the Garba Guracha Lake pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/44vz-p388
title: Duiven Spoorlijn pollen dataset

description: Raw data for the Duiven Spoorlijn pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/pykm-gd72
title: Utsira pollen dataset

description: Raw data for the Utsira pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/22ez-q584
title: Akotivika pollen dataset

description: Raw data for the Akotivika pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/cf0s-5g75
title: Angerlo aardgasleiding pollen dataset

description: Raw data for the Angerlo aardgasleiding pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/5y7n-1b24
title: Lake Selina pollen dataset

description: Raw data for the Lake Selina pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/zps2-8v65
title: Marki Alonia specimen stable isotope dataset

description: Raw data for the Marki Alonia specimen stable isotope dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/khxs-s745
title: Kiizi Marsh pollen dataset

description: Raw data for the Kiizi Marsh pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/k4t9-6092
title: Lake Yoa pollen dataset

description: Raw data for the Lake Yoa pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/gzza-rr30
title: Stangenfilz pollen dataset

description: Raw data for the Stangenfilz pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/w0nz-1356
title: Venhuizen Elbaweg plant macrofossil dataset

description: Raw data for the Venhuizen Elbaweg plant macrofossil dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/2128-d760
title: Mubwindi swamp II pollen dataset

description: Raw data for the Mubwindi swamp II pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/xpjt-sq84
title: Schut- en Grafkampen pollen dataset

description: Raw data for the Schut- en Grafkampen pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hn9z-gj98
title: Hoogwoud pollen dataset

description: Raw data for the Hoogwoud pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ya1a-7k34
title: Lago di Viverone pollen dataset

description: Raw data for the Lago di Viverone pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hfbe-2r29
title: Timan Ridge pollen dataset

description: Raw data for the Timan Ridge pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/pz08-vz56
title: Enkhuizen Westeinde pollen dataset

description: Raw data for the Enkhuizen Westeinde pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/g5nt-7c04
title: Lago di Biandronno pollen dataset

description: Raw data for the Lago di Biandronno pollen dataset obtained from the Neotoma Paleoecology Database.