cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25391488
title: Dataset for 'High gradient Terahertz-driven ultrafast photogun'

description: Terahertz (THz)-based electron acceleration has the potential as a technology for next-generation cost-efficient compact electron sources. Although proof-of-principle demonstrations have proven the feasibility of many THz-driven accelerator components, THz-driven photoguns with sufficient brightness, energy, and control for use in demanding ultrafast applications have yet to be achieved. Here, we present a novel millimeter scale waveguide-based THz-driven photogun that exploits field enhancement to boost the electron energy, a movable cathode to achieve precise control over the accelerating phase as well as a multifunction cavity for exquisite beam control. The short driving wavelength enables a peak acceleration gradient as high as ~3 GV/m. Using microjoule-level single-cycle THz pulses, we demonstrate electron beams with up to ~14 keV electron energy, 1% energy spread and ~0.02 mm mrad transverse emittance. With a highly integrated rebunching cavity the bunch is further compressed by about 10 times to 167 fs with ~10 fC charge. High-quality diffraction patterns of single crystal silicon and projection microscopy images of the copper mesh are achieved. We are able to reveal the transient radial electric field developed from the charged particles on a copper mesh after photoexcitation with high spatiotemporal resolution, providing a potential scheme for plasma-based beam manipulation. Overall, these results represent a new record in energy, field gradient, beam quality and control for a THz-driven electron gun, enabling for the first-time real applications in electron projection microscopy and diffraction. It is therefore a critical step and milestone in the development of all-optical THz-driven electron devices validating the maturity of the technology and its use in precision applications.
DOI: 10.15132/10000174
title: Udgård 2021 Raw Dataset

description: The Leverhulme Research Centre for forensic science (LRCFS) at the University of Dundee has had several ongoing projects (collectively known by the project name Bruin) in the area of digital documentation of simulated crime scenes, particularly focusing on the 'art of the possible' using Virtual Reality as technology tools for facilitating this.
LRCFS and NKC always welcome collaborations and look forward to positive discussions on how we can work together in partnership in this emerging area. Our collaboration led to the documentation of (training) fire scenes during the spring of 2019 exploring how we could create virtual reality representations of those scenes.
Those early results developed our understanding of the level of refinement of photography that was needed to capture these challenging scenes fully so that an accurate virtual Reality render could be produced.
LRCFS developed a standard operating procedure for scene capture (known as the GT protocol) which was piloted in the collection of the Udgård data. The Udgård project also enabled a range of other photographic and scanning techniques to be used to capture the same scenes across three phases; Alpha (prior to crime scene staging); Beta (pre fire crime scene) and Gamma (post fire crime scene). The scenes were full scale residential properties labelled Elle, Lage and Huge.

Outcomes
The collaboration between LRCFS and NKC has successfully generated virtual reality representations of the photographed scenes, to be used as both investigative and training tools.
Both LRCFS and NKC have agreed that these co-created training and research materials produced during the crime scene exercises and live burns will be shared and that both organisations will use the outcomes for education and training (including revenue-generating courses for both organisations) and the generation of outputs by both organisations that may have exploitable commercialisation potential.
All Udgård partners can access a downloadable database with the results from Udgård, such as LIDAR, Matterport, still photographs, videos, 360 photographs and outcomes from measurements.
Every organisation that accepts a copy of the Udgård database, agrees to:
• refer to the Udgård project and use its DOI each time they use the database,
DOI: 10.15468/s2vncb
title: Holothurians of the genus Peniagone (Elasipodida: Elpidiidae) from the northwest Pacific ocean

description: The dataset contains occurrences of the holothurians of the genus *Peniagone* (Echinodermata:Holothuroidea:Elasipodida:Elpidiidae) in the northwest Pacific ocean published in Kremenetskaia et al. (2021). The specimens were collected in the several cruises of RV *Vityaz* and recent German-Russian cruises SokhoBio, KuramBio and KuramBio II. Specimens were collected with trawls and epibenthic sledges and preserved mainly in ethanol. The database includes records of *Peniagone dubia*, *P. mus*, *P. cf. purpurea* and two new species, *P. minuta* and *P. saveljevae*. Additionally added are the records of *Scotoplanes hanseni* and *Elpidia kurilensis* included into the phylogenetic analysis.
DOI: 10.17188/1349869
title: Materials Data on ZnO by Materials Project

description: ZnO crystallizes in the hexagonal P6_3mc space group. The structure is two-dimensional and consists of two ZnO sheets oriented in the (0, 0, 1) direction. Zn2+ is bonded in a trigonal non-coplanar geometry to three equivalent O2- atoms. All Zn–O bond lengths are 1.96 Å. O2- is bonded in a trigonal non-coplanar geometry to three equivalent Zn2+ atoms.
DOI: 10.17632/5tg7yj92wv.1
title: Land cover, tree density and predicted habitat suitability for 12 species in northern central Victoria used in habitat connectivity analysis

description: The attached files represent the data we used in identifying wildlife corridors for the restoration of regional habitat connectivity in northern central Victoria, Australia. All the files are in Esri ASCII raster format with VICGRID94 projection. All the resolutions are 75m by 75m. The file trees3.asc contains the density of trees in each cell ranging from 0 to 64. The file refined_landcov.asc is the land cover map with ten categories (category 4 was further split into two subcategories 41 and 42). The land cover classes are: (1) Dryland pasture; (2) Dense forest cover; (3) Woodland and open forest; (4) Open woodland (41 Open woodland – open woodland with numerous gaps and 42 Open woodland – where canopy vegetation is relatively continuous); (5) Temperate grassland and chenopod shrubland; (6) Urban – suburbs; (8) Irrigated crops, pasture and horticulture; (9) Plantation forestry; (10) Waterbodies and wetlands; and (11) Dry-land cropping. The other files are the predicted habitat suitability for 12 species. These included three species of mammals (brush-tailed phascogale [Phascogale tapoatafa], sugar glider [Petaurus breviceps] and yellow-footed antechinus [Antechinus flavipes]), five species of birds (buff-rumped thornbill [Acanthiza reguloides], fuscous honeyeater [Lichenostomus fuscus], grey shrike-thrush [Colluricincla harmonica], rufous whistler [Pachycephala rufiventris] and white-throated treecreeper [Cormobates leucophaeus]), and four reptile species (Bougainville’s skink [Lerista bougainville], jacky lizard [Amphibolurus muricatus], tree goanna [Varanus varius] and wood gecko [Diplodactylus vittatus]). Note: those in square brackets are species Latin names, which usually should be displayed in italics.
DOI: 10.25505/fiz.icsd.cc1zmwj0
title: CSD 1836363: Experimental Crystal Structure Determination

description: Related Article: Christian Funk, Jürgen Köhler, Iwona Lazar, Dariusz Kajewski, Krystian Roleder, Jürgen Nuss, Annette Bussmann-Holder, Heiko Bamberger, Joris van Slageren, David Enseling, Thomas Jüstel, Thomas Schleid|2018|Cryst.Growth Des.|18|6316|doi:10.1021/acs.cgd.8b01265
DOI: 10.25919/5ef5a70ae2be9
title: RV Investigator IN2019_V06 BASTA radar and Radiosonde data

description: This record describes the BASTA radar and Radiosonde products by Protat et al. (2020) from observations made in the Northern Territory northwest region. A shipborne 95 GHz Doppler cloud radar mounted on a stabilized platform was used to retrieve vertical profiles of three-dimensional (3D) winds by sequentially pointing the stabilized platform in different directions. The resulting dataset consists of the processed BASTA 3D winds (the subject of the paper), and the radiosonde dataset, which is used for validation. For more details please refer to the cited article (download link provided below): Protat, A. and I. McRobert (2020): Three-dimensional wind profiles using a stabilized shipborne cloud radar in wind profiler mode. Atmos. Meas. Tech., 13, 1–12. https://doi.org/10.5194/amt-13-1-2020.
DOI: 10.5066/p13stasq
title: Postfire Debris-Flow Database (Literature Derived)

description: The data presented in this data release represent observations of postfire debris flows that have been collected from publicly available datasets. Data originate from 13 different countries: the United States, Australia, China, Italy, Greece, Portugal, Spain, the United Kingdom, Austria, Switzerland, Canada, South Korea, and Japan. The data are located in the file called “PFDF_database_sortedbyReference.txt” and a description of each column header can be found in both the file “column_headers.txt” and the metadata file (“Post-fire Debris-Flow Database (Literature Derived).xml”). The observations are derived from areas that have been burned by wildfire and are global in nature. However, this dataset is synthesized from information collected by many different researchers for different purposes, and therefore not all fields are available for each of the observations. Missing information is indicated by the value “-9999” in the ”PFDF_database_sortedbyReference.txt” file. Note that the text file contains special characters and a mix of date-time formats that reflect the original data provided by the authors. The text may not be displayed correctly if it is opened by proprietary software such as Microsoft Excel but will appear correctly when opened in a text editor software.

Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
DOI: 10.5281/zenodo.10160861
title: Application of artificial neural network to forecast indoor air temperature in a building with artificial ventilation: impact of early stopping.

description: Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this study was to analyse the performance of various artificial neural networks with a view to proposing an optimal approach for predicting the indoor temperature of a tertiary building with artificial ventilation. The MLP, CNN, LSTM models and the CNN-LSTM combination (long short-term memory network) were used and coupled with the optimisation algorithms (Adam, SGD) and the independent hyper-parameters early stopping and dropout. The parameters used are outdoor ambient temperature, outdoor relative humidity, indoor relative humidity, wet bulb temperature, black globe temperature and mean radiant temperature. The data is collected in an artificially ventilated building in Yaoundé, Cameroon. A numerical code was developed in Python to run the simulations. In order to study the impact of the parameters on the prediction, two scenarios were distinguished in this work: (1) all the parameters are input to the network, (2) only the parameters whose absolute value of the correlation coefficient was greater than or equal to 0.5 were used. The impact of early stopping is assessed by distinguishing two case studies: the first without early stopping, the second with early stopping. The results showed that without early stopping, the MLP, CNN, LSTM and CNN-LSTM networks are adequate for predicting the temperature with the second scenario, mainly with both the SGD and Adam algorithms, and CNN-LSTM is the most appropriate model because the MSE and MAE values obtained in this case were closer to 0. With early stopping, the learning time is reduced and the learning curves are improved; the models optimised better with the SGD algorithm in general, but the best neural network model was obtained with the Adam algorithm and the LSTM network for the performances MSE=0.0005, MAE=0.0130 with the second scenario.
Keywords: prediction, indoor temperature, artificial neural network, early stopping, artificially ventilated building.
DOI: 10.5281/zenodo.10242299
title: Antarctic crustal Model and radiogenic heat production

description: It supplements to the following publication:
Li, L., & Aitken, A. R. (2023). Crustal heterogeneity of Antarctica signals spatially variable radiogenic heat production.
It contains a crustal model from a model ensemble using gravity inversion Ant_Crust.nc
And a median, 25th, and 75 percentile heat production value based on the density-heat production relationship. Ant_heat_production.nc 
Model extend -3330000 to 3330000 meter, in 20 km resolution. 
Projection: WGS 84 / Antarctic Polar Stereographic - EPSG:3031
DOI: 10.5281/zenodo.10627345
title: Data for "Using shelfedge transport composition and sensitivity experiments to understand processes driving sea level on the Northwest European Shelf"

description: Dataset in support of  and as described in "Using shelfedge transport composition and sensitivity experiments to understand processes driving sea level on the Northwest European Shelf" submitted to JGR:Oceans 11/2023.

Processed data using output from experiments conducted using the AMM7 configuration of NEMO. Monthlys means for year 2006-2015.

exp_sl_residuals_yXX.nc include SSH, Manometric and Steric components of dynamic sea level with linear trend, seasonal and 12-month rolling mean removed.

exp_sl_12m_trends_yXX.nc include 12-month rolling mean of detrended SSH, Manometric and Steric components of dynamic sea level.

along_shelfbreak_windstress_2006_15.nc is the integral of along-shore wind signal as described in the manuscript

exp_vol_tran_2d.nc and exp_vol_tran_3d.nc are cross-shelfedge (200 m isobath) transports as described in the manuscript
DOI: 10.5281/zenodo.10628317
title: Replication materials for "Less Light and More Heavy Precipitation in World Urban Regions Captured through a Two-Way Subgrid Land-Atmosphere Coupling Framework in the NCAR CESM2"

description: The datasets are replication materials for the research "Less Light and More Heavy Precipitation in World Urban Regions Captured through a Two-Way Subgrid Land-Atmosphere Coupling Framework in the NCAR CESM2". "CTL_precipitation.nc" shows daily grid precipitation in the CTL run using the default CESM2. "EXP_precipitation.nc" shows daily grid, urban, and rural precipitation in the EXP run using the proposed two-way subgrid land-atmosphere coupling framework.
DOI: 10.5281/zenodo.10754987
title: Controls over leaf N and P stoichiometry of 121 plant species in a grassland reserve

description: Exploring the controls over leaf nitrogen (N) and phosphorus (P) stoichiometry is critical to understanding the biogeography of plant species and the adaptation of plants to global changes. Studies have revealed major drivers for regional- and global-scale variations in leaf N, P, and N:P ratio. However, the local-scale controls over leaf N and P stoichiometry remains poorly understood.  We adopted a revisiting and resampling approach to compare the leaf N, P, and N:P ratio of the same 121 plant species between 1979 and 2018 in the Xilingol grassland reserve, as a 2 °C increase in the mean annual temperature had occurred during this period. We grouped the 121 species by taxon (family), life form, and habitat type, and evaluated the roles of these factors in affecting leaf N, P, and N:P ratio.
DOI: 10.5281/zenodo.10835753
title: Sprite observations made from Langmuir Lab with a neuromorphic sensor on May 25, 2023

description: This data set contains sprite observations made on the night of May 25, 2023 from the Langmuir Laboratory of Atmospheric Research in central New Mexico. It contains recordings made with an intensified Phantom high-speed camera, a Generation 4 Prophesee neuromorphic sensor, and a Watec low-light level camera. Information about individual sprite captures is summarized in the table Sprite_Data_Summary_Table.xlsx.

Phantom .cine files can be opened with Vision Research's proprietary PCC software. The Prophesee .es files can be loaded and visualized with the Python event stream reader library available at https://github.com/neuromorphicsystems/event_stream.

The table contains information about 15 sprites recorded on that night. However, only 9 were used in the article titled "Sprite durations measured with a neuromorphic sensor". This is because the other 6 occurred outside of the Phantom camera's field of view and, thus, don't have an associated Phantom recording, which served as ground truth for analysis.
DOI: 10.5281/zenodo.10849031
title: 11 April 2019 MRMS Data Subset for Harkema et al. (2024)

description: Subset of Multi-Radar Multi-Sensor (MRMS) data: 3D Reflectivity, Composite Reflectivity, and Isothermal Reflectivity. More specifically, the subset data is between 00 and 16 UTC 11 April 2019. Note: Some time(s) and file(s) maybe be missing between the subset period.
DOI: 10.5281/zenodo.11234843
title: Data and codes for "Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters"

description: Data and codes for "Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters".
DOI: 10.5281/zenodo.12636936
title: CHEESEHEAD CLAMPS observations

description: The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD) experiment was conducted near Falls Park, Wisconsin in mid-summer to early fall 2019 (Butterworth et al., 2021). Primarily sponsored by the National Science Foundation, additional support from NOAA enabled the concurrent deployment of CLAMPS1 and CLAMPS2 at Lakeland and Prentice airports, respectively, for approximately five weeks of data collection. CHEESEHEAD also included radiosonde launches near the WLEF-TV 400-m very tall tower site, which was approximately 45 km away from both CLAMPS locations (north of Prentice, west of Lakeland) and many other instruments. There are additional observations after this time, but the main period of quality data is 19 September 2019 to 10 October 2019.

These data can be accessed on the NOAA National Severe Storms THREDDS Archive. The readme contained here provides information about how to access and available resources for users. These data are also published in the CHEESEHEAD EOL catalog.

The CLAMPS1 (located at Lakeland Airport, 45.92◦ N, 89.73◦ W) DL collected plan position indicator (PPI) scans at 70 ◦ elevation every 20 minutes, and remained in a zenith pointing mode otherwise. The DL provides range-resolved, line-of-sight measurements of radial velocity, intensity (signal-to-noise ratio [SNR]+1), and attenuated backscatter. In the case of PPI scans meant for velocity azimuthal display (VAD) analysis, these data are post-processed to produce profiles of horizontal wind speed and direction. The zenith pointing scans are used for vertical velocity information, and are post-processed to provide turbulence information such as vertical velocity variance. The DL was configured to provide profiles with 18-m vertical spacing. The CLAMPS1 MWR and AERI were both deployed, and thermodynamic profiles as retrieved by the TROPoe physically based algorithm (previous versions known as AERIoe; Turner and Löhnert, 2014) were made available every ten minutes. For different user needs and use cases we provided retrievals using only AERI data, only MWR data, and combining the data from both the AERI and the MWR in the TROPoe retrieval algorithm.

The CLAMPS2 facility (located at Prentice Airport, 45.54◦ N, 90.28◦ W) operated similarly, but the DL collected PPIs at 60 ◦ elevation every 5 minutes. The onboard MWR was deployed similarly, but CLAMPS2’s AERI was damaged in transit and did not operate for CHEESEHEAD. As such, CLAMPS2 retrievals are limited to MWR-only. 

Turner, D. D. and Löhnert, U.: Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI), Journal of Applied Meteorology and Climatology, 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1, 2014.
DOI: 10.5281/zenodo.3359791
title: Global whole-rock geochemical database compilation

description: Dataset associated with the submitted publication - Global whole-rock geochemical database compilation
DOI: 10.5281/zenodo.4023390
title: KWAI-AD-AudVis

description: It consists of 85,432 ads videos from the China popular short-term video app, Kwai. The videos were made and uploaded by commercial advertisers rather than personal users. The reason to use the ads videos lied on two folds: 1) the source guarantees the videos under control to some level, such as high-resolution pictures and intention-ally designed scene; 2) the ads videos mimic the style of the ones uploaded by personal users, as they are played in be-tween the personal videos in Kwai app. It can be seen as a quality controlled UGVs dataset.The dataset was collected in two batches (Batch-1 is our preliminary work), coming with the tags of ads industry cluster. The videos were randomly picked from a pool. The pool was formed by selecting the ads from several contiguous days.Half of the selected ads had click through rate(CTR) in top30000 within that day and the other half had CTR in bottom30000. It should be noticed that the released dataset is a sub-set of the pool. The audio track had2 channels (we mixed to mono channel in the study) and was sampled at 44.1 kHz, while the visual track had resolution of1280×720 and was sampled at 25frame per second(FPS).This dataset is a extension of the KWAI-AD corpus [3]. It is not only suitable for tasks in multimodal learning area, but also for ones in ads recommendation. It shows that the ads videos have three main characteristics: 1) The videos may have very inconsistent information in visual or audio streams. For example, the video may play a drama-like story at first, and then present the product introduction, whose scenes are very different. 2) The correspondence between audio and visual streams is not clear.For instance, similar visual objects (e.g. talking salesman)come with very different audio streams. 3) The relationship between audio and video varies in different industries. For example, game or E-commerce ads will have very different styles. These characteristics make the dataset suitable yet challenging for our study about the AVC learning. In the folder, you will see: audio_features.tar.gz, meta, README, samples, ad_label.npy, video_fetaures.tar.gz. The details are included in README. <strong><em>If you use our dataset, please cite our paper: "Themes Inferred Audio-visual Correspondence Learning" (https://arxiv.org/pdf/2009.06573.pdf)</em></strong>
description: Though the original video is in Chinese, we make a English version of the labels.
DOI: 10.5517/cc819vf
title: CCDC 239594: Experimental Crystal Structure Determination

description: Related Article: P.G.Jones|2004|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.