Peak amplitude values recorded at source and receive hydrophones during a two-vessel marine sparker seismic survey conducted by the U.S. Geological Survey (USGS) in April of 2021 off the coast of Santa Cruz, California (USGS field activity 2021-619-FA) are presented. On the source vessel (R/V Parke Snavely; RVPS), near-field data were recorded using a broadband spherical reference Reson TC4034 hydrophone positioned 1-meter below the sparker source (either a SIG ELP790 or an Applied Acoustics Delta sparker) along seven depth site transects ranging between 25 and 600 meters. On the nearly stationary receive vessel (R/V San Lorenzo; RVSL), omnidirectional Cetacean Research CR3 hydrophones were positioned between 10- and 20-meters water depth below the vessel to record the far-field signal. Data are presented in csv format, accompanied by combined scatter plots per depth site and mean-filtered curve plots for visualization purposes.

The abstract of the paper [1] is: This paper describes differential sampling measurements of an ac source and a Josephson arbitrary waveform synthesizer (JAWS). A new iterative approach for aligning the phases of the JAWS and the source waveforms was implemented to minimize the differential voltage at the digitizer. A type-A uncertainty of 45 nV/V after 10 min was measured for a commercial ac source at 1 V rms amplitude and 1 kHz. [1] "Differential Measurements of an AC Source with a Josephson Arbitrary Waveform Synthesizer"submitted to Conference on Precision Electromagnetic Measurements (CPEM) 2024; will be published and available on IEEE website at a later date. Data for figures 2 to 4 of the manuscript. Files included in this publication: Fig 2 FFT of the digitizer signal.csv Figure 2 Fig. 2. 1 kHz component of the FFT of the digitizer signal (amplitude and phase) for Delta_V1=Source-JAWS1 and Delta_V2=Source-JAWS2 over 3.5 hours Five columns: The first column is the time (x-axis), the second column is the amplitude in volt of the first measured difference voltage (shown as black solid circle in Fig. 2), the third column is the phase in degree of the first measured difference voltage (shown as black open circle in Fig. 2), the fourth column is the amplitude in volt of the second measured difference voltage (shown as red solid circle in Fig. 2), the fifth column is the phase in degree of the second measured difference voltage (shown as red open circle in Fig. 2). Format: CSV Fig 3 Source rms amplitude and environment data.csv Figure 3 Fig. 3. Room environment conditions recorded (temperature, atmospheric pressure, and relative humidity) and Reconstructed rms amplitude for the source at 1 kHz. Five columns: The first column is the time (x-axis), the second column is the reconstructed amplitude in volt - 1 V (shown as blue solid circle in Fig. 3 bottom), the third column is the temperature in degree C (shown as orange solid square in Fig. 3 top), the fourth column is the atomsepheric pressure in hecto Pascal (shown as green open triangle in Fig. 3 top), the fifth column is the relative humidity in percent (shown as puple open circle in Fig. 2). Format: CSV Fig 4 Allan variance.csv Figure 4 Fig. 4. Allan deviation of the source amplitude measured at 1 V and 1 kHz. Five columns: The first column is the time (x-axis), the second column is the calculated Allan Deviation in volt (shown as blue solid circle in Fig. 4), the third column is the fit on the results, representing the white noise with slope -0.5 (shown as black dash line in Fig. 4), the fourth column is the is the time (x-axis) for the 1/f noise floor plot and the fifth column is the 1/f noise floor (shown as a black solid line in Fig. 4) Format: CSV

Annual temperature amplitude (°C) dataset at about 10 km resolution at the equator, using different climate data source and based on different Representative Concentration Pathways (RCPs) according to the time period as follows: - climate data source CRUTS32 based on historical data for the time period 1981-2010; - climate data source ENSEMBLE based on the Representative Concentration Pathway RCP8.5 for time periods 2041-2070 and 2071-2100. The Annual temperature amplitude (°C) dataset is part of the GAEZ v4 Agro-climatic Resources - Thermal Regime sub-theme. For additional information, please refer to the GAEZ v4 Model Documentation.

Supplementary dataset for a paper "Variations in the characteristic amplitude of tectonic tremor induced by long-term slow slip events" published in Journal of Geophysical Research - Solid Earth by K. Nakamoto, Y. Hiramatsu, T. Matsuzawa, and T. Minakami. This file includes a tremor source catalog in the western Shikoku region. The tremor sources are located by the National Research Institute for Earth Science and Disaster Resilience (NIED), using the meathod of Maeda and Obara (2009; doi:10.1029/2008JB006043). The files are in text format and time zone of the record is JST (UTC+9).

The main results file are saved separately:- ASSR2.html: R output of the main analyses (N = 33)- ASSR2_subset.html: R output of the main analyses for the smaller sample (N = 25)FIGSHARE METADATACategories- Biological psychology- Neuroscience and physiological psychology- Sensory processes, perception, and performanceKeywords- crossmodal attention- electroencephalography (EEG)- early-filter theory- task difficulty- envelope following responseReferences- https://doi.org/10.17605/OSF.IO/6FHR8- https://github.com/stamnosslin/mn- https://doi.org/10.17045/sthlmuni.4981154.v3- https://biosemi.com/- https://www.python.org/- https://mne.tools/stable/index.html#- https://www.r-project.org/- https://rstudio.com/products/rstudio/GENERAL INFORMATION1. Title of Dataset:Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones2. Author Information A. Principal Investigator Contact Information Name: Stefan Wiens Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/swiens-1.184142 Email: sws@psychology.su.se B. Associate or Co-investigator Contact Information Name: Malina Szychowska Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.researchgate.net/profile/Malina_Szychowska Email: malina.szychowska@psychology.su.se3. Date of data collection: Subjects (N = 33) were tested between 2019-11-15 and 2020-03-12.4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden5. Information about funding sources that supported the collection of the data:Swedish Research Council (Vetenskapsrådet) 2015-01181SHARING/ACCESS INFORMATION1. Licenses/restrictions placed on the data: CC BY 4.02. Links to publications that cite or use the data: Szychowska M., & Wiens S. (2020). Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Submitted manuscript.The study was preregistered:https://doi.org/10.17605/OSF.IO/6FHR83. Links to other publicly accessible locations of the data: N/A4. Links/relationships to ancillary data sets: N/A5. Was data derived from another source? No 6. Recommended citation for this dataset: Wiens, S., & Szychowska M. (2020). Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.12582002DATA & FILE OVERVIEWFile List:The files contain the raw data, scripts, and results of main and supplementary analyses of an electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.ASSR2_experiment_scripts.zip: contains the Python files to run the experiment. ASSR2_rawdata.zip: contains raw datafiles for each subject- data_EEG: EEG data in bdf format (generated by Biosemi)- data_log: logfiles of the EEG session (generated by Python)ASSR2_EEG_scripts.zip: Python-MNE scripts to process the EEG dataASSR2_EEG_preprocessed_data.zip: EEG data in fif format after preprocessing with Python-MNE scriptsASSR2_R_scripts.zip: R scripts to analyze the data together with the main datafiles. The main files in the folder are: - ASSR2.html: R output of the main analyses- ASSR2_subset.html: R output of the main analyses but after excluding eight subjects who were recorded as pilots before preregistering the studyASSR2_results.zip: contains all figures and tables that are created by Python-MNE and R.METHODOLOGICAL INFORMATION1. Description of methods used for collection/generation of data:The auditory stimuli were amplitude-modulated tones with a carrier frequency (fc) of 500 Hz and modulation frequencies (fm) of 20.48 Hz, 40.96 Hz, or 81.92 Hz. The experiment was programmed in python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mnThe EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format.For more information, see linked publication.2. Methods for processing the data:We conducted frequency analyses and computed event-related potentials. See linked publication3. Instrument- or software-specific information needed to interpret the data:MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html#Rstudio used with R (R Core Team, 2020): https://rstudio.com/products/rstudio/Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v34. Standards and calibration information, if appropriate:For information, see linked publication.5. Environmental/experimental conditions:For information, see linked publication.6. Describe any quality-assurance procedures performed on the data:For information, see linked publication.7. People involved with sample collection, processing, analysis and/or submission:- Data collection: Malina Szychowska with assistance from Jenny Arctaedius.- Data processing, analysis, and submission: Malina Szychowska and Stefan WiensDATA-SPECIFIC INFORMATION:All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.

This Data Release contains an updated version of the San Andreas catalog of low-frequency earthquakes (LFEs): Shelly, D. R. (2017), A 15 year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault, J. Geophys. Res. Solid Earth, 122, 3739–3753, doi:10.1002/2017JB014047. This catalog contains 88 LFE families, with each family consisting of events detected by cross-correlation with the associated waveform template. These templates were identified and located by Shelly and Hardebeck (2010): Shelly, D. R., and J. L. Hardebeck (2010), Precise tremor source locations and amplitude variations along the lower-crustal central San Andreas Fault, Geophys. Res. Lett., 37, L14301, doi:10.1029/2010GL043672. For completeness, we repeat the original catalog information provided in the supplement of Shelly (2017) below, with minor modifications: Catalog Time-Period and Format: The low-frequency earthquake catalog spans from April 2001 to 30 April 2024 and contains 1,528,117 events. Format: YYYY MM DD s_of_day HH mm ss.ss ccsum meancc med_cc seqday ID latitude longitude depth n_chan Explantions: YYYY MM DD (year month day) - Event time (template start time in UTC - ~1s prior to first S-wave arrival time at an HRSN station) s_of_day - Event time (template start time in UTC - ~1s prior to first S-wave arrival time at an HRSN station), second of the day (i.e. 0-86400), HH mm ss.ss (hour, minute, second) - Event time (template start time in UTC- ~1s prior to S-wave arrival time at first HRSN station) ccsum - correlation sum across all stations (must exceed 4.0) meancc - mean correlation among stations with data med_cc - median correlation seqday - sequential day since March 1, 2001 ID - reference ID of family latitude longitude depth - estimated location for that family (Shelly and Hardebeck, 2010) n_chan - number of data channels existing for event (some channels may exist, but not have good data) Family IDs: Each family has an associated identification code, which is a number followed by 1-4 ‘s’. The family IDs are almost meaningless and are simply used as unique identifiers. Originally the numeric code was taken from the second of the day at which the initial template for this family occurred. The number of ‘s’ indicates the number of iterations of stacking and cross-correlation that were applied to derive the template waveforms (see Methods). The lower amplitude and more distant sources typically benefitted from multiple iterations of stacking and cross correlation, before the final template stabilized in its detection set. Data channels Used (station.channels): GHIB.13, EADB.123, JCSB.1, FROB.123, JCNB.123, VCAB.123, MMNB.123, CCRB.123, LCCB.123, SMNB.123, RMNB.123, SCYB.123 JCNB failed in 2008 and was replaced by a shallow sensor. New sensor not used. RMNB failed in 2011 and was not replaced. GHIB.2 was never operational JCSB.23 have poor signal to noise and are not used. VARB was replaced with a new sensor at a new depth in 2003, and this station was not used in original template formation. As of 2024, detection capabilities were substantially degraded with a maximum of 16 channels of data available for detection. This is due to outages in GHIB (since 2020), FROB (since 2023), VCAB (since 2023), and CCRB (since 2022), in addition to the outages described above. It is unclear when/if these stations might be repaired in the future. Channel swap on FROB, VCAB (after BP->SP channel swap, before 2011-7-14): 2011/4/21-2011/7/14: Swap VCAB.2 and VCAB.3 2010/11/10-2011/7/14: Swap FROB.2 and FROB.3 Disregard mean correlation, enforce network correlation sum only (because of poor but present data): 2012/2/13-2014/4/23 Polarity corrections during initial processing: CCRB.123, correct for reversed polarity from 2001-6-1 to 2001-12-13. FROB.123, correct for reverse polarity from 2010/12/10-2011/4/7 MMNB.123, correct for reverse polarity from 2010/12/10-2011/4/7 FROB.23, correct for reverse polarity from 2010/4/8 to 2011-7-14 Polarity corrections applied in post-processing (these are minor and done after initial detection stage): 2011-4-7 to 2011-5-27: zero FROB.2 channel (wiring mistake, FROB.2 duplicates FROB.3) 2005/4/11-2005/5/13: reverse GHIB.13 2005/12/15-present: reverse GHIB.3 2002/11/22-2003/1/16: reverse EADB.2 2002/11/21-2003/1/17: reverse VCAB.3

VizieR Online Data Catalog: VVV high amplitude NIR variable stars(Contreras Pena C.+, 2017)

This table contains results from the selection and classification of over a thousand ultraviolet (UV) variable sources discovered in ~ 40 deg² of GALEX Time Domain Survey (TDS) NUV images observed with a cadence of 2 days and a baseline of observations of ~ 3 years. The GALEX TDS fields were designed to be in spatial and temporal coordination with the Pan-STARRS1 Medium Deep Survey, which provides deep optical imaging and simultaneous optical transient detections via image differencing. The authors characterize the GALEX photometric errors empirically as a function of mean magnitude, and select sources that vary at the 5-sigma level in at least one epoch. They measure the statistical properties of the UV variability, including the structure function on timescales of days and years, and report classifications for the GALEX TDS sample using a combination of optical host colors and morphology, UV light curve characteristics, and matches to archival X-ray, and spectroscopy catalogs. The authors classify 62% of the sources as active galaxies (358 quasars and 305 active galactic nuclei), and 10% as variable stars (including 37 RR Lyrae, 53 M dwarf flare stars, and 2 cataclysmic variables). They detect a large-amplitude tail in the UV variability distribution for M-dwarf flare stars and RR Lyrae, reaching up to |Delta-M| = 4.6 and 2.9 magnitudes, respectively. The mean amplitude of the structure function for quasars on year timescales is five times larger than observed at optical wavelengths. The remaining unclassified sources include UV-bright extragalactic transients, two of which have been spectroscopically confirmed to be a young core-collapse supernova and a flare from the tidal disruption of a star by a dormant super-massive black hole. The authors calculate a surface density for variable sources in the UV with NUV < 23 mag and |Delta-M| > 0.2 mag of ~ 8.0, 7.7, and 1.8 deg^-2 for quasars, AGN, and RR Lyrae stars, respectively, and a surface density rate in the UV for transient sources, using the effective survey time at the cadence appropriate to each class, of ~15 and 52 deg^-2 yr^-1 for M dwarfs and extragalactic transients, respectively. The GALEX observations were made using the NUV detector which has an 1.25 degree diameter field of view and an effective wavelength of 2316 Angstroms. During the window of observing visibility of each GALEX TDS field (from two to four weeks, one to two times per year), they were observed with a cadence of 2 days, and a typical exposure time per epoch of 1.5 ks (or a 5-sigma point-source limit of m_AB(NUV) ~ 23.3 magnitude), with a range from 1.0 to 1.7 ks. Table 2 in the reference paper lists the RA and Dec of their centers, the Galactic extinction E(B - V ) for each field from the Schlegel et al. (1998, ApJ, 500, 525) dust maps, and the number of epochs per field. This table was created by the HEASARC in May 2013 based on a machine-readable version of Table 4 from the paper which was obtained from the ApJ web site. This is a service provided by NASA HEASARC .

The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping.

Provided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.

Known Issues

• Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags.
• Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur.
• Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.

Improvements/Changes from Previous Version

• Modest changes were made to the spline fitting algorithm used to estimate the MSLSP30NA product in V011. Only gaps greater than 20 days were filled using observations from the outside of the target year to reduce the computational burden. Sensitivity analyses demonstrated that this change had negligible impact on product results.
• The Quality Assurance (QA) fields were updated to reflect the changes in the gap-filling. Version 1 included QA values from 1-7, whereas Version 1.1 includes QA values from 1-6, 9, and 10.
• Peak date corresponding to the maximum EVI2 value in a growth cycle (Peak and Peak_2) and number of days with clear observations in calendar year (numObS) layers were added.

Data for Figure 3.32 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.32 shows relative change in the amplitude of the seasonal cycle of global land carbon uptake in the historical CMIP6 simulations from 1961-2014.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

List of data provided

• Observed seasonal cycle amplitude of global land carbon uptake
• Simulated seasonal cycle amplitude of global land carbon uptake

Data provided in relation to figure

fig_3_32_main.nc:

• Multi-Model Mean: dim0 = 0, red solid line. [red shaded region: (dim0=0) +- (dim0=1))]
• JMA-TRANSCOM: dim0 = 2, black dotted line.
• CO2-MLO: dim0 = 3, black solid line. [black shaded region: (dim0=3) +- (dim0=4))]
• CO2-GLOBAL: dim0 = 5, black dashed line.

fig_3_32_inset.nc:

• Multi-Model Mean for 1961-1970 (orange): dim0 = 0 (shaded region(dim0=0) +- (dim0=1))
• Multi-Model Mean for 2005-2014 (green): dim0 = 2 (shaded region(dim0=2) +- (dim0=3))

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website.

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on a short, cylindrical sample of Westerly granite (diameter = 4 inches, height ~ 2 inches). Liquid nitrogen was poured in a copper cup attached to the top of the sample, and the resulting acoustic emissions (AEs) and temperature changes on the surface of the sample were monitored. The obtained AEs were used to determine the microcracking source locations and amplitude, and the associated moment tensors. Included in this submission is an animation of the AEs, a graphic displaying the temperature changes, and the measured data.

The ALOHA Cabled Observatory (ACO) is a system of hardware and software that extends electric power and the Internet offshore, supporting sustained real-time observations in the deep ocean. The ACO is connected to Oahu, Hawaii by the HAW-4 telecommunications cable transferred to the project by AT&T in 2007. On June 6th, 2011, the ACO was deployed on the ocean bottom (depth ~ 5 kilometers - 3 miles) near Station ALOHA , 100 kilometers (60 nautical miles) north of Oahu, Hawaii. Station ALOHA is the site of the long-term Hawaii Ocean Time-series (HOT) open ocean measurement program, visited by research vessels 10-12 times each year since October 1988.

There are five modules that are connected together on the seafloor. The Junction Box is connected to the HAW-4 cable and to the Observatory module. Together, they supply 1200 watts of power and 100 Megabits per second of Ethernet communications to sensor systems on these two modules, and to the other three modules. The other modules are the Camera tripod, the AMM bottom node, and the TAAM mooring. Sensors provide live video of the ocean bottom around the ACO, sound from local and distant sources, currents, pressure, temperature, and salinity.

This data set contains the 2011-06 to 2013-12 rapidly sampled current velocity data, which are collected at the ACO using a SonTek 250 kHz Acoustic Doppler Profiler. The instrument is located at a depth of 4726.2 m below the mean surface, 1.83 m above the bottom. There are 20 depth bins in each profile, which reaches upward to at most 100 m above the bottom. The data have been quality controlled and are stored in NetCDF format. The files contain current velocity in the east, north, and upward directions in m/s. Ancillary fields include mean echo amplitude and temperature. The project is ongoing with data from 2014- to become available.

Title of program: FREINT Catalogue Id: ACUB_v1_0 Nature of problem FREINT evaluates the integral A(x) exp(i q (x'-x)^2) dx [i= (-1)^1/2, x = xi,xf] occurring in optical transfer theory. By application of this integral the wave amplitude A(t+d;x') at a plane z = t+d can be calculated on some premises from the amplitude field A(t;x), e.g. in electron microscopy if an object is imaged in a defocussed mode (catchword : Fresnel diffraction). A(x) is given by a complex vector. CORRECTION SUMMARY: Vol:Year:Page 0:unpublished:unpublished "000ACORRECTION 27/11/78" "Unpublished correction to FREINT: an integration routine calculating Fresnel diffraction." W.J. Gruschel Note: correction instructions are contained in source code Versions of this program held in the CPC repository in Mendeley Data ACUB_v1_0; FREINT; 10.1016/0010-4655(79)90086-9 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

This data publication contains an optimized mosaic of PALSAR-2 L-band dual-polarized radar backscatter summer composite for the year 2020 across Canada (excluding the Arctic Archipelago). Its primary purpose is to offer the best possible L-band radar summer-like composite mosaic mostly tailored for i) classifying natural treed or shrubby vegetation covers, and ii) estimating their structural attributes, such as height and biomass. ## Methodology: This product is based on the freely available and open dataset of yearly JAXA Global PALSAR-2/PALSAR Mosaics ver. 1 (hereafter JAXA GPM v1). They were generated by the Japanese space agency (JAXA) using PALSAR L-band synthetic aperture radar sensors aboard the Advanced Land Observing Satellites (ALOS): ALOS-2 PALSAR-2 (2015 to 2020) and ALOS PALSAR (2007 to 2010). JAXA GPM v1 provide yearly mosaics orthorectified and slope-corrected L-band HH- and HV-polarized gamma naught (γ°) backscatter amplitude with 25-m pixel size and scaled as 16-bit data (Shimada et al. 2014). JAXA GPM v1 are accessible as a Google Earth Engine image collection at https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR. The yearly 2007 to 2020 JAXA GPM v1 dataset across Canada underwent a post-processing and compositing methodology implemented in Google Earth Engine, as detailed in Pontone et al. 2024 and summarized in a pdf “Readme” file provided with the dataset. In summary, the method involves these three steps: Post-processing of yearly γ° HH and HV datasets: handling data gaps, filtering speckle noise, and generating two radar vegetation indices, the HV/HH ratio (HVHH) and the radar forest degradation index (RFDI). Temporal compositing from 2015 to 2020 of post-processed yearly γ° PALSAR-2 HH, HV, HVHH, and RFDI backscatter data aimed to i) address data gaps and ii) mitigate detrimental backscatter fluctuations across ALOS-2 orbits resulting from numerous out-of-summer acquisitions. Generating the final PALSAR-2 L-band γ° radar backscatter summer composite circa 2020 raster files. ## Performance et limitations: The resulting Canada-wide, excluding the Arctic Archipelago, gap-free and radiometrically optimized mosaic of circa 2020 PALSAR-2 L-band backscatter summer composite was found significantly improved compared to the single-year 2020 JAXA GPM v1 mosaic, particularly in northern boreal Canada (Pontone et al. 2024). However, this product should be considered as a summer-like composite and users should be mindful of the following known limitations: • In northwestern Canada, there were often minimal to no summer PALSAR-2 acquisitions, resulting in residual backscatter fluctuations across ALOS-2 orbits. • The composite may exhibit patchy radiometric noise in areas that experienced major disturbances (fires, harvesting) between 2015 and 2020 despite they were accounted for in our compositing methodology. • This product is deemed less performant, or possibly not suitable, for i) characterizing highly dynamic land cover types such as grasslands, croplands, and water bodies, or for ii) estimating soil and/or vegetation moisture content for the year 2020. As a final note, JAXA released an improved GPM ver. 2 that was not available at the time of this study. A preliminary analysis shows that the circa 2020 PALSAR-2 composite product still seems to outperform the 2020 JAXA GPM v2 in northern Canada. ## Additional Information on Dataset: This dataset comprises four raster geotiff files of circa 2020 L-band PALSAR-2 summer temporal composites as mosaics of orthorectified and radiometrically slope corrected dual-polarized HH and HV gamma naught (γ°) backscatter amplitude, along with two radar vegetation indices (HVHH, RFDI), all scaled as 16-bit Digital Number (DN) values with 30-m pixel size in Lambert conformal conic projection. An additional 8-bit RGB quick-view file is also provided. A companion pdf ”Readme” file provides further details about these geotiff files and equations to convert DN values to γ° absolute intensity values. ## Dataset Citation: Beaudoin, A., Villemaire, P., Gignac, C., Tolszczuk, S., Guindon, L., Pontone, N., Millard, C. (2024). Canada’s PALSAR-2 dual-polarized L-band radar summer backscatter composite, circa 2020. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209 In addition, please provide credits to the Japanese space agency JAXA with the mention “Original Global PALSAR-2/PALSAR Mosaics v1 provided by JAXA (©JAXA)” ## Publication Reference for Product Development and Use in Wetland Mapping: Pontone, N., Millard, K., Thompson, D., Guindon, L., Beaudoin, A. (2024). A hierarchical, Multi-Sensor Framework for Peatland Sub-Class and Vegetation Mapping Throughout the Canadian Boreal Forest. Remote Sensing for Ecology and Conservation (accepted for publication). ## Cited reference: Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Tomohiro, S., Thapa, T., Lucas, R. (2014). New Global Forest/Non-Forest Maps from ALOS PALSAR Data (2007-2010). Remote Sensing of Environment, 155, pp. 13-31. https://doi.org/ 10.1016/j.rse.2014.04.014

These data will appear in [1]. The abstract for that paper is given below:We report on the design, fabrication, and measurement of a Very High Frequency band Josephson Arbitrary Waveform Synthesizer (VHF-JAWS) at frequencies from 1~kHz to 50.05~MHz. The VHF-JAWS chip is composed of a series array of 12,810 Josephson junctions (JJs) embedded in a superconducting coplanar waveguide. Each JJ responds to a pattern of current pulses by creating a corresponding pattern of voltage pulses, each with a time-integrated area related to fundamental constants as $ extit{ extbf{h/2e}}$. The pulse patterns are chosen to produce quantum-based single-tone voltage waveforms with an open-circuit voltage of 50~mV~rms (\mbox{-19.03~dBm} output power into 50~$\Omega$ load impedances) at frequencies up to 50.05~MHz, which is more than twice the voltage that has been generated by previous RF-JAWS designs at 1~GHz. The VHF-JAWS is "quantum-locked", that is, it generates one quantized output voltage pulse per input current pulse per JJ while varying the dc current through the JJ array by at least 0.4~mA and the amplitude of the bias pulses by at least 10~\%. We use the large bias pulse quantum-locking range to investigate one source of error in detail: the direct feedthrough of the current bias pulses into the DUT at VHF frequencies. We reduce this error by high-pass filtering the current bias pulses and measure the error as a function of input pulse amplitude using two techniques: by measuring small changes over the quantum-locking range and by passively attenuating the input pulse amplitude so that the nonlinear JJs no longer generate voltage pulses while the error is only linearly scaled.

MSLSP V1 data was decommissioned on December 14, 2021. Users are encouraged to use the improved MSLSP V1.1 data product.

NASA’s Multi-Source Land Imaging (MuSLI) Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel 2A and 2B Multispectral Instrument (MSI) provide the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy. MSLSP30NA is aligned with the Military Grid Reference System (MGRS) at 30 m spatial resolution. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, land cover, land use, and land cover change mapping.

Provided in the MSLSP product are variables for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low-resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.

Known Issues * Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags. * Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur. * Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.