cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16589867
title: Data for Slow Light Nanocoatings for Ultrashort Pulse Compression

description: <b>Data for </b><b>Slow light nanocoatings for ultrashort pulse compression</b><br>M. Ossiander1,* Y.-W. Huang1,2, W. T. Chen1, Z. Wang3, X. Yin1, Y. A. Ibrahim1,4, M. Schultze3, 
F. Capasso1*1 John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St, Cambridge, MA 02138, USA. <br>2 Department of Photonics,
National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.<br>3 Institute of Experimental Physics, Graz University of Technology, Petersgasse 16, 8010
Graz, Austria.<br>4 University of Waterloo, Waterloo, ON N2L 3G1, Canada.<br>* Corresponding Authors: mossiander@g.harvard.edu, capasso@seas.harvard.edu<br>Manuscript published in <b>Nature Communications 12, 6518 (2021),</b> https://www.nature.com/articles/s41467-021-26920-6Details of the data files are described below and in the description.txt files. Methods are described in the publication.-- <br><b>Folder frog_data: </b><br>This folder contains the frequency resolved optical gating data for the 162 nm nanopillar diameter coating.<br>Reference data for the incoming femtosecond laser pulses and the incoming laser pulses transmitted through the uncoated substrate only are also provided.The variables in the files are:<br>wavelength_nm: wavelength axis for frequency resolved optical gating spectrogram in nanometers.<br>delay_fs: delay axis for the frequency resolved optical gating spectrogram in femtoseconds.<br>spectrogram: the raw frequency resolved optical gating spectrogram in arbitrary units.<b>Folder white_light_data: </b><br>This folder contains the raw white light interferometer data.<br>Folders are named according to the nanopillar diameter of the examined compressor coating.<br>For each coating, reference data of the empty interferometer, and reference data of the substrate in one interferometer arm (double pass) is also provided.The variables in the files are:<br>wavelength: wavelength axis for the recorded spectral intensities in nanometers.<br>background: Spectral intensity after the interferometer with both interferometer arms blocked in arbitrary units.<br>A: Spectral intensity after the interferometer with sample arm blocked in arbitrary units.<br>B: Spectral intensity after the interferometer with reference arm blocked in arbitrary units.<br>AB: Spectral intensity after the interferometer in arbitrary units.<br>wavelengthGD: wavelength axis for the calculated group delay in nanometers.<br>frequencyGD: frequency axis for the calculated group delay in petahertz.<br>GDnm: calculated group delay in femtoseconds (plot versus wavelengthGD).<br>GDPHz: calculated group delay in femtoseconds (plot versus frequencyGD).
DOI: 10.6084/m9.figshare.16589867.v2
title: Data for Slow Light Nanocoatings for Ultrashort Pulse Compression

description: <b>Data for </b><b>Slow light nanocoatings for ultrashort pulse compression</b><br>M. Ossiander1,* Y.-W. Huang1,2, W. T. Chen1, Z. Wang3, X. Yin1, Y. A. Ibrahim1,4, M. Schultze3, 
F. Capasso1*1 John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St, Cambridge, MA 02138, USA. <br>2 Department of Photonics,
National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.<br>3 Institute of Experimental Physics, Graz University of Technology, Petersgasse 16, 8010
Graz, Austria.<br>4 University of Waterloo, Waterloo, ON N2L 3G1, Canada.<br>* Corresponding Authors: mossiander@g.harvard.edu, capasso@seas.harvard.edu<br>Manuscript published in <b>Nature Communications 12, 6518 (2021),</b> https://www.nature.com/articles/s41467-021-26920-6Details of the data files are described below and in the description.txt files. Methods are described in the publication.-- <br><b>Folder frog_data: </b><br>This folder contains the frequency resolved optical gating data for the 162 nm nanopillar diameter coating.<br>Reference data for the incoming femtosecond laser pulses and the incoming laser pulses transmitted through the uncoated substrate only are also provided.The variables in the files are:<br>wavelength_nm: wavelength axis for frequency resolved optical gating spectrogram in nanometers.<br>delay_fs: delay axis for the frequency resolved optical gating spectrogram in femtoseconds.<br>spectrogram: the raw frequency resolved optical gating spectrogram in arbitrary units.<b>Folder white_light_data: </b><br>This folder contains the raw white light interferometer data.<br>Folders are named according to the nanopillar diameter of the examined compressor coating.<br>For each coating, reference data of the empty interferometer, and reference data of the substrate in one interferometer arm (double pass) is also provided.The variables in the files are:<br>wavelength: wavelength axis for the recorded spectral intensities in nanometers.<br>background: Spectral intensity after the interferometer with both interferometer arms blocked in arbitrary units.<br>A: Spectral intensity after the interferometer with sample arm blocked in arbitrary units.<br>B: Spectral intensity after the interferometer with reference arm blocked in arbitrary units.<br>AB: Spectral intensity after the interferometer in arbitrary units.<br>wavelengthGD: wavelength axis for the calculated group delay in nanometers.<br>frequencyGD: frequency axis for the calculated group delay in petahertz.<br>GDnm: calculated group delay in femtoseconds (plot versus wavelengthGD).<br>GDPHz: calculated group delay in femtoseconds (plot versus frequencyGD).
DOI: 10.6084/m9.figshare.16589867.v1
title: Raw data for the manuscript Slow Light Nanocoatings for Ultrashort Pulse Compression

description: Raw data for the manuscript Slow Light Nanocoatings for Ultrashort Pulse Compression. Details of the data file are described in the description.txt files. Methods are described in the publication.
DOI: 10.5281/zenodo.8411701
title: Experimental signals and processed data used in the research work "On-chip phonon-magnon reservoir for neuromorphic computing"

description: The data set includes the raw experimental data, processed experimental data, and numerically modeled dependencies in the respective folders:
The raw experimental data (magnon readout, as measured) are given for all processed signals presented in the respective figures (folders 'Figure2', 'Figure3' and 'Sup Figure1') and used for the ANN training (folder '3x3Sets & AugmentedVisualShapes').      
The signals presented in the folders '\3x3Sets & AugmentedVisualShapes\3x3SET##' are named following the scheme shown in Fig. 3a. The signals in the folder \3x3Sets & AugmentedVisualShapes\RandomizedShapes' are simulated using the procedure described in the Methods section as "Drawing of randomized visual shapes". The sets of calculated statistical parameters used for the shapes' recognition are in the folder ''\3x3Sets & AugmentedVisualShapes\Parameters'
The waveforms for the trajectories formed by randomly selected 4, 5, 6, and 7 discrete positions and their statistical parameters are presented in the corresponding folder.    
description: The code used for the ANN simulation is available under https://doi.org/10.5281/zenodo.10080655
DOI: 10.5281/zenodo.8411702
title: Experimental signals and processed data used in the research work "On-chip phonon-magnon reservoir for neuromorphic computing"

description: The data set includes the raw experimental data, processed experimental data, and numerically modeled dependencies in the respective folders:
The raw experimental data (magnon readout, as measured) are given for all processed signals presented in the respective figures (folders 'Figure2', 'Figure3' and 'Sup Figure1') and used for the ANN training (folder '3x3Sets & AugmentedVisualShapes').      
The signals presented in the folders '\3x3Sets & AugmentedVisualShapes\3x3SET##' are named following the scheme shown in Fig. 3a. The signals in the folder \3x3Sets & AugmentedVisualShapes\RandomizedShapes' are simulated using the procedure described in the Methods section as "Drawing of randomized visual shapes". The sets of calculated statistical parameters used for the shapes' recognition are in the folder ''\3x3Sets & AugmentedVisualShapes\Parameters'
The waveforms for the trajectories formed by randomly selected 4, 5, 6, and 7 discrete positions and their statistical parameters are presented in the corresponding folder.    
description: The code used for the ANN simulation is available under https://doi.org/10.5281/zenodo.10080655
DOI: 10.17031/65796de33e68d
title: CPR Data request - Chris Lynam - INSITE 2 - 13/12/2023

description: Gridded products (0.25*0.25) of annual means using CPR data. Data from 1958 to 2020. Data from 48°N to 62.5°N and -12°E to 9°E. Taxa list: Acartia_spp._(unidentified), Calanus finmarchicus, C. helgolandicus, Decapoda_larvae_(Total), Copepods large, Copepods Small, Para-Pseudocalanus_spp., Temora_longicornis, Total diatoms (sum of 112 taxa), Total dinoflagellates (sum of 95 taxa).
DOI: 10.6084/m9.figshare.18675887
title: Data for The speed limit of optoelectronics

description: <b>Data fo</b><b>r</b><b> </b><b>The speed limit of optoelectronics</b><br>M. Ossiander1,6,*, K. Golyari1,2, K. Scharl1,2, L. Lehnert1,2, F. Siegrist1,2, J. P. Bürger1,2, D. Zimin1,2, J. A. Gessner1,2, M. Weidman1,2, I. Floss3, V. Smejkal3, S. Donsa3, C. Lemell3, F. Libisch3, N. Karpowicz4, J. Burgdörfer3, F. Krausz1,2,*, M. Schultze2,51 Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Str. 1, 85748 Garching, EU, Germany. <br>2 Fakultät für Physik, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, EU, Germany. <br>3 Institute for Theoretical Physics, Vienna University of Technology, Wiedner Hauptstrasse 8-10, 1040 Vienna, EU, Austria. <br>4 CNR NANOTEC Institute of Nanotechnology, via Monteroni, 73100 Lecce, EU, Italy. <br>5 Institute of Experimental Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, EU, Austria. <br>6 Present address: John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford St, Cambridge, MA 02138, USA. <br>* Corresponding Authors: mossiander@g.harvard.edu; ferenc.krausz@mpq.mpg.deManuscript published in <b>Nature Communications 13, 1620 (2022),</b> https://www.nature.com/articles/s41467-022-29252-1The data in the files time_domain_comparison_lpps_streaking.hdf5 and spectral_domain_comparison_lpps_streaking.hdf5 is the base for Fig. 2.<br>The data in files powerscan_lif.hdf5 and powerscan_neon.hdf5 is the base for Fig. 3.<br>Methods are described in the publication.
DOI: 10.6084/m9.figshare.18675887.v1
title: Data for the manuscript The speed limit of optoelectronics

description: Data for the manuscript The speed limit of optoelectronics (Nature Communications, 2022, https://doi.org/10.1038/s41467-022-29252-1). The data in the files time_domain_comparison_lpps_streaking.hdf5 and spectral_domain_comparison_lpps_streaking.hdf5 is the base for Fig. 2.<br>The data in files powerscan_lif.hdf5 and powerscan_neon.hdf5 is the base for Fig. 3.
DOI: 10.18167/dvn1/tzvgls
title: Sorghum WCA-BCNAM data

description: Data used for the publication: "Characterization of adaptation mechanisms in sorghum using a multi-reference back-cross nested association mapping design and envirotyping" Genetic data: 51'545 SNP markers for 3901 genotypes from three population (Grinkan, Kenin-Keni, Lata3) Phenotypic data: multi-environment phenotypic data for several traits covering: flag leaf appearance (FLAG), plant height (PH), number of internode (NODE_N), average length of the internode (NODE_L), pedundle lenght (PED_L), panicle lenght (PAN_L), grain weight (GWGHT), grain yield (YIELD) For further details see: Access_sorghum_BCNAM_data.html (in the data folder).
DOI: 10.15485/1845448
title: Dynamically Downscaled (WRF) 1km, Hourly Meteorological Conditions 1987-2020. East/Taylor Watersheds

description: This dataset contains meteorological output from the Weather Research and Forecasting (WRF) version 3.8.1. This dataset has been created to 1) investigate hydrometeorological processes impacting the East River and water-delivery to the Critical Zone, and 2) provide meteorological forcing data for distributed Earth-science modeling applications in the East River watershed. Variables have a 1 kilometer spatial resolution and hourly temporal resolution and encompass a rectangular region encompassing the East and Taylor River watersheds, Colorado, near the town of Crested Butte. WRF was forced using Climate Forecast System Reanalysis (CFSR) lateral boundary conditions. Each .zip file contains one "water year" of data (October 1 -- September 30; i.e. water year 2017 starts October 1, 2016 and ends September 30, 2017). Each zip folder contains 12 netcdf (.nc) files containing one month of hourly data each and are approximately 250mb. Model timestamps are in UTC time.The files contain the following data variables:EAST_MASK: binary mask of the watershed regionTAYLOR_MASK: binary mask of the watershed regionGLW downwelling longwave radiation (w/m2)HR_PRCP: Hourly Precipitation Rate (mm/hr). Includes all hydrometeors (solid+liquid). HFX NoahMP LSM total grid-cell modelled sensible heat flux (w/m2) [positive towards atmosphere]LH NoahMP LSM total grid-cell modelled latent heat flux (w/m2) [positive towards atmosphere; can be converted to ET]PSFC Surface Barometric Pressure (hPa)Q2 Two-meter specific humidity (kg/kg)SWDOWN Downwelling shortwave solar radiation (w/m2)SWNORM Terrain-normal downwelling shortwave radiation (w/m2)T2 Two-meter air temperature (deg K)U10 10-m U-component of wind velocity (m/s)V10 10m V-component of wind velocity (m/s)XLAT Latitude of grid-center point XLONG Longitude of grid-center point XTIME Model timestamp, **in UTC**
DOI: 10.17632/wr9mprpj5k.2
title: Hybrid Input-Output tables for Saudi Arabia at the year 2013

description: - The IOT_KSA_Val file gives the input-output table in thousands of Riyals for 13 sectors. - The IOT_KSA_Qtities gives the input-output table in quantities for 13 sectors. The energy sectors are in ktoe, the cement and petrochemical sectors in tons, and the others in "pseudo-quantities". - The IOT_KSA_Prices gives the unitary prices that are consistent with IOT in volume and value for intermediate consumption, final consumption, production, and imports. - Income_Distribution gives the secondary income distribution among the represented institutional agents in thousands of Riyals. - The IOT_KSACO2Emis gives the energy-consumption related emissions in KtCO2 for intermediate consumption and final consumption.
DOI: 10.5439/1855341
title: ARM: Corrected Moments in Antenna Coordinates, Version 2 (CMAC2)

description: cmac2
DOI: 10.15482/usda.adc/1519179
title: Asian giant hornet, Vespa mandarinia, genome assembly
DOI: 10.17189/1519567
title: MRO CRISM DERIVED DATA RECORD V1.0
DOI: 10.6073/pasta/e8481c34667b4bbfdf9cd57f9e9698e5
title: Data in Support of Effects of Urbanization and Forest Fragmentation on Atmospheric Nitrogen Inputs and Ambient Nitrogen Oxide and Ozone Concentrations in Mixed Temperate Forests.

description: Urban ecosystems around the globe experience greater atmospheric nitrogen (N) deposition compared to rural areas and are particularly vulnerable to fragmentation due to land-use change. However, while the influences of urbanization and forest fragmentation on atmospheric inputs to temperate forests have been determined separately, the combined effects of the two changes on temperate forest ecosystems have yet to be assessed. To investigate these combined effects, we deployed throughfall collectors to measure atmospheric N inputs and passive samplers to measure nitrogen oxides (NOx) and ozone (O3) throughout the 2018 and 2019 growing seasons in seven temperate forest sites along an urbanization gradient from Boston to central Massachusetts. We found a positive relationship between the amount of impervious surface area surrounding each site (% ISA) and throughfall nitrate (NO3-) inputs at the forest edge, with urban edge NO3- inputs nearly double the rate at rural edge sites. There were higher rates of NO3- inputs in the rural forest interior than edge sites. Urban sites experienced significantly higher concentrations of NOx and O3 both in the interior and at the edge compared to rural sites. Atmospheric N inputs were significantly elevated in the early (May-July) compared to the late (August-November) growing season and concentrations of NOx and O3 were also elevated in the mid-growing season (June-September). Our results demonstrate that together, urbanization and forest fragmentation lead to greater rates of atmospheric N inputs and ambient pollutant concentrations of NOx and O3 in temperate forests of the northeastern U.S.
DOI: 10.21954/ou.rd.23676801.v1
title: Ultraviolet and visible reflectance spectra of Phobos and Deimos

description: Here we provide the ultraviolet and visible reflectance spectrum of Phobos and Deimos measured by the NOMAD-UVIS spectrometer.
DOI: 10.5281/zenodo.8310513
title: GloUTCI-M: A Global Monthly 1 km Universal Thermal Climate Index Dataset from 2000 to 2022

description: The GloUTCI-M comprises global monthly UTCI data at a spatial resolution of 1km, spanning from March 2000 to October 2022. The dataset is expressed in degrees Celsius (°C) and is stored as an integer type (Int16). To utilize it appropriately, one must divide the values by 100.
DOI: 10.5281/zenodo.10060861
title: Serra et al., "Frequency dependence of ocean kinetic energy and respective changes over the period 1983-2018"

description: Data needed to reproduce the figures presented in the article "Frequency dependence of ocean kinetic energy and respective changes over the period 1983-2018" by Serra et al.
DOI: 10.5281/zenodo.8062282
title: ABCdata

description: Training data and drivers for the SRI Intenational, AtmoSense Background Characterization (ABC) project phase 1 from the AtmoSense program funded by Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001121C0026.. The data sets have been ingested to create the Atmospheric Background Characterization Generative Adversarial Network (ABCGAN) atmosense-abcgan.readthedocs.io. and consist of data from the Poker Flat Incoherent Scatter Radar (PFISR) and GNSS receivers around Poker Flat Research Range in Fairbanks, Alaska as well as geophysical drivers. The data coverage is from years 2010 through 2020. PFISR is owned by the National Science Foundation (NSF) and operated by SRI International under NSF Cooperative Agreement AGS-1840962. File Contents. ABC_PFISR_BV_v03.04.h5 Contains binned and curated fitted parameters, i.e. Ne, Te, Ti, and Plasma drift velocities as a function of altitude and time above Poker Flat. ABC_PFRR_DRIVERS_v02.00.h5 A series of geophysical drivers covering all hours from 2010 through 2020. ABC_PFRR_GNSS_HFP_v2.0.h5 GNSS wave parameters. ABC_PFISR_HFP_v01.50.h PFISR wave parameters.
DOI: 10.5281/zenodo.7797769
title: Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data

description: Python figures in pickle format matplotlib version 3.5.1
DOI: 10.17632/zv53k6vg65.3
title: Wake flow and angular oscillation behavior of two cylinders in tandem arrangement forming a van der Pol oscillator

description: Data result from careful measurements with hot wires and a high-speed camera in an aerodynamic channel with a rectangular cross-section of 0.146 m x 0.193 m (height × width), 2.29 m long, to study the wake behavior and the oscillating pattern of two cylinders in tandem arrangement assembled on a free-to-oscillate table. The cylinders were made from commercial PVC (PolyVinyl Chloride) with 25.1 mm diameter and pitch-to-diameter ratio p/d = 1.26. One of the cylinders is aligned with the table axis, while the second one is placed eccentrically. The table is coupled on the upper and lower channel walls using low wear bearings. The blockage ratio is 13% when the set is perfectly aligned with the flow.
The airflow, from a 0.75 kW centrifugal blower, passes through a diffuser, two honeycombs, and two screens, to reduce the flow turbulence intensity to about 1. Upstream of the test section, a Pitot tube measures the free stream velocity, used for the calculation of the Reynolds number. The Reynolds numbers (Re) of the experiments were 8.47 × 10³, 12.7 × 104, and 16.6 × 104, based on the cylinder diameter and a flow velocity of respectively 5, 7.5, and 10 m/s.
The axial component of the wake flow velocity behind the cylinder set and its fluctuations is measured with a DANTEC StreamLine constant hot-wire anemometry, with a DANTEC 55P11 single wire probe. The sampling frequency was 1 kHz, low pass filter at 300 Hz, and time acquisition of 32.768 s. Data acquisition was performed by a USB 16-bit NI USB-9162 A/D board.
The oscillation of the cylinder set was studied using a high-speed Phantom V411 – Ametek camera with a 50 mm AF-S VR Micro-Nikkor lens, with a resolution of 640 × 480 pixels and 1000 frames per second, corresponding to the 1 kHz sampling frequency of the hot-wire signal. The camera was triggered and synchronized with the CTA via an Ethernet cable. A mirror inclined 45° was mounted at the top of the channel to track the position of a pointer, which indicated the angle between the cylinders and the main flow. The free software Tracker© (https://physlets.org/tracker/) was used to analyze the digital movies to generate time series of the angular displacement of the cylinders. The mean error for this analysis is 0.0239 rad.
The experimental procedure was as follows: data acquisition and video recording started while the air blower was turned on. After the setup achieved an apparent stabilization, a disturbance device was inserted from downstream the cylinders, tangent to the walls up to be aligned with the cylinder’s setup and immediately removed. The overall perturbation lasts about 2.4 seconds.

	Velocity and angular position files for each Reynolds number: time (s), wake flow velocity (m/s), angular displacement (rad).
Re8470_time_u_theta.txt
Re12700_time_u_theta.txt
Re16600_ time_u_theta.txt
	Digital movies of the cylinder set for each Reynolds number: upper and side view:
Re8470_Ang_displ.mp4
Re12700_Ang_displ.mp4 
Re16650_Ang_displ.mp4
DOI: 10.5281/zenodo.7074544
title: Engineering random spin models with atoms in a high-finesse cavity

description: Data corresponding to the article "Engineering random spin models with atoms in a high-finesse cavity".
DOI: 10.21335/nmdc-432083304
title: Timeseries from Ocean Weather Station M (OWSM) 2023

description: Time series of inorganic carbon (DIC), alkalinity (TA), and hydrography from the fixed station Ocean Weather Station M (OWSM) in the Norwegian Sea at 66 degree N 2 degree E. Measurements collected over the full water depth of 0-2000 m. These data are from 2022 and 2023 and represent a continuation of the timeseries of carbon which started in 2001. Monthly sampling frequency between 2001 and 2009 and seasonal resolution from 2010 and onwards. Data from 2001 to 2009 were collected by the weathership Polarfront, and from 2010 and onwards, various research vessels collected samples from the station. DIC and TA data are from discrete bottle samples while hydrography are measured using sensors. The timeseries at OWSM started in 1948 with daily hydrography masurements over the full water depth.