Find research datasets worth reusing

SBC LTER: Reef: Kelp Forest Community Dynamics: Fish abundance

These data describe the abundance and size of fish species as part of SBCLTER's kelp forest monitoring program to track long-term patterns in species abundance and diversity. This study began in 2000 in the Santa Barbara Channel, California, USA. The abundance and size of all taxa of resident kelp forest fish encountered along permanent transects are recorded at nine reef sites located along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. In these surveys, fish were counted in either a 40x2m benthic quadrat, or in the water parcel 0-2m off the bottom over the same area. The table in this data package documented the annual benthic fish community survey which was conducted once a year around late July or early August. The time period of data collection for the annual benthic fish community survey varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. The monthly fish survey at ABUR (Transect 1, 2, and 3), AQUE (Transect1), and MOHK (Transect 1) in 2002. The Transect 2 and 3 at ABUR were discontinued in June 2006. See Methods for more information.

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Detritus biomass

These data describe the abundance of macroalgal detritus (grams wet mass m-2) as part of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. Detritus was collected in six permanent 1 m2 quadrats positioned uniformly along 40 m transects in each sampling plot. Sample collections were brought back to the laboratory, identified to species and weighed wet. The seasonal surveys were initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel, California, US.

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Urchin size frequency distribution

These data describe the size frequency distribution of red (Mesocentrotus franciscanus) and purple (Strongylocentrotus purpuratus) sea urchins within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The diameter of the test (shell without spines) was recorded to the nearest 0.5 cm for 50 red and 50 purple sea urchins located within a 40 m x 2 m area of each plot. Size frequency data of red and purple sea urchins are not collected in the continual kelp removal plots. When combined with size-mass relationships established in the laboratory these data were used to provide a non-destructive, in situ estimate of the dry mass per unit area of bottom for each species. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact

These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of bottom substrate and sand depth

These data describe the percent cover of eight bottom substrate types within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The type of bottom substrate was recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Percent cover of each substrate type on transect was estimated as the proportion of the 80 points contacted by the substrate type x 100. In cases where the substrate type was sand, the depth of the sand was measured. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel. The two tables in this data package include: 1) The percent cover of eight bottom substrate types; and 2) the sand depth of each sampling point (sand depth = 0 if substrate type is not sand)

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Abundance and size of Giant Kelp

These data describe the abundance and size of giant kelp (Macrocystis pyrifera) within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The number of giant kelp > 1 m tall were recorded within four contiguous 20 m x 1m permanent plots located within a 40 m x 2 m area. The number of fronds > 1 m tall were counted for each individual and used as an estimate of its size. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Fish abundance

These data describe the abundance and size of reef-associated fish within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The number, size and species identity of reef fish were recorded within a 2 m wide swath centered along a 40 m long transect extending up to 2 m off the bottom. Fish size was measured as total length estimated to the nearest cm. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.

SBC LTER: Reef: Kelp Forest Community Dynamics: Abundance and size of Giant Kelp (Macrocystis Pyrifera), ongoing since 2000

These data describe the abundance of giant kelp, Macrocystis pyrifera, and are part of the SBCLTER kelp forest monitoring program. The study was initiated in 2000, in the Santa Barbara Channel, California, USA, and this dataset is updated once per year. The number and size (number of fronds and diameter of holdfasts) of plants were recorded along permanent transects at nine reef sites located along the mainland coast of the Santa Barbara Channel and at two sites on the north side Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information.

SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Invertebrate and algal density

These data describe the abundance of common reef associated species of macro invertebrates and macroalgae within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The number of individuals of approximately 50 taxa were recorded by divers along 40 m transects within each plot. Small species of macroalgae and macinvertebrates were counted within six permanent 1 m2 quadrats positioned uniformly along the 40 m transect, while larger species were counted within four contiguous 20 m2 sub-sections of each 40 m x 2 m transect. Also included at the quadrat scale are estimates of an average size-related measurement of each species, which was developed specifically for each species for the purpose of estimating its biomass. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.

SBC LTER: Reef: Kelp Forest Community Dynamics: Invertebrate and algal density

These data describe the density and size of a select group of about 50 species of benthic invertebrates and understory algae in fixed plots (either 1m x 1m or 20m x 1m) along permanent transects. These data are part of SBCLTER’s kelp forest monitoring program to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset are at nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. Sampling began in 2000, and these data are updated annually. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. See Methods for more information.

SBC LTER: Reef: Kelp Forest Community Dynamics: Transect geospatial metadata

The data table provides summaries of depth information (mean, standard deviation and coefficient of variation) for all of the transects surveyed as part of the SBC LTER Kelp Forest Monitoring program. All data are expressed in meters, reference to mean lower low water (MLLW). Each value is the result of 160 observations, four at each meter (n=160). The sampling locations in this dataset are 40 meter transects at nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. The PDF document provides descriptive information of the transect such as headings and relative locations of the points along the transect. Data were recorded in 2010 and 2011, except that IVEE transect 3, 4, 5, 6, 7, and 8 were recorded in 2024.

openCC0Sep 2023View details →

Long-Term Core Site Grasshopper Dynamics for the Sevilleta National Wildlife Refuge, New Mexico

Grasshoppers are important animals in semi-arid environments, both as herbivores and as food resources for higher level consumer animals. Grasshoppers tend to be numerous and speciose in semi-arid environments, especially desert grasslands, where they range from environmental specialists to environmental generalists. Grasshopper populations tend to change considerably from year to year, often in response to annual variation in rainfall and plant production. The purpose of this study was to monitor grasshopper species composition and abundance over large temporal and spatial dimentions which include black grama grassland, blue grama grassland, creosotebush shrubland, and pinyon/juniper woodland environments at the Sevilleta, in relation to seasonal and annual variation in precipitation and plant production. Data were collected for all individual species to provide information on community dynamics as well as population dynamics, starting in 1992 and continuing to the present. The working research hypothesis for this study was that grasshopper populations in all environments will correlate positively to seasonal and annual variation in precipitation and plant production. Spring grasshopper populations will be especially high during El Nino years, and late summer populations especially high during La Nina years.

opencc-by-4.0Oct 2019View details →

Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool

This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu

opencc-by-4.0Oct 2020View details →

Posterior samples of a dynamical fit for planetary system HD128311

This dataset contains the posterior samples from a dynamical fit of the multi-planetary system HD128311. See <a href="https://doi.org/10.1093/mnrasl/slu202">https://doi.org/10.1093/mnrasl/slu202</a> for a description of the fit. The dataset is one numpy array, consisting of <ul> <li>500 walkers </li> <li>1250 generations</li> <li>3 bodies (1 star + 2 planets)</li> <li>7 parameters per body (mass, x, y, z, vx, vy, vz)</li> </ul>

Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"

Data from the paper: Dalagnol, R. et al. Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. Sci Rep 11, 1388 (2021). https://doi.org/10.1038/s41598-020-80809-w Link: https://www.nature.com/articles/s41598-020-80809-w   This repository contains: 1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL. 2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data. 3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).   If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).

Roots Carbon Dynamics in Temperate forest roots, Thuringia, Germany

These files contain radiocarbon, d13C, NSC concentrations, and CO2 efflux rates measured for aspen (Populus tremula hybrids) roots collected during 2018 growing season in the Großer Hermannsberg Mountain, Germany (50°42’50’’ N, 10°36’13’’ E, 616 m a.s.l). Coarse (> 2 mm) and fine (2 ≤ mm) roots collected from three 'treatments': before stem girdling (Pre-girdling), ~3 months after girdling (Girdling) and ~3 months after girdling but in un-girdled trees (Control). The files with the relevant results: '13C', '14C', 'CO2_efflux', 'NSC'. Few roots from the 'Pre-girdling' treatment were incubated for respiration measurements 7 d after harvest. The files with the relevant results: 'Repeated_incubations_isotopes', 'Repeated_incubations_fluxes'.  Results of incubations used for Q10 calculations presented in the file 'CO2_efflux_Q10'. Temperature and rainfall in the site during 2018 growing season are presented in the file 'Field_temperature_rainfall'. Results used to reconstruct local atmospheric D14C-CO2 record are presented in the file 'Local_atmospheric_CO2_D14C'. The file 'Metadata' contains information about the headers in the other files.

Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050

******************* Please view the README.txt or README.md file for detailed documentation of data. ******************** Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050 Date of release: 25/11/2020 Identifier: 10.5281/zenodo.4275759 Permalink: http://dx.doi.org/10.5281/zenodo.4275759 Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, Under Review, 2020. Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files. Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi   Dates of data: 2030, 2050 Type of data: Outdoor Temperature Geographic location: Jyväskylä Time resolution: hourly, full year Format: All data is stored in .csv files Number of files: 1 .zip --> 50 files + README.txt + README.md This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files   Notifications: Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data). Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate). Contains modified Ruosteenoja et al. [2016] information. Other data and information sources are described in README.txt, README.md, references and on the associated publication.

Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)

Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. NeuroImage, 226, 117569. Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces evolve over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.

opencc-by-4.0Dec 2020View details →

Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"

Dataset documentation The archives in hdf5 format provided at this link contain the datasets used in the manuscript Dynamic differential reflectivity calibration using vertical profiles in rain and snow, submitted to Remote Sensing (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.   File content Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are: <ul> <li> idx, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan; </li> <li> t, the timestamp of the scan, in seconds since seconds since Jan 01, 1970; </li> <li> r or rg (depending on the file), the distance from the radar in meters; </li> <li> az, the azimuth angle in degrees; </li> <li> el, the elevation angle in degrees; </li> <li> zdr, the uncalibrated differential reflectivity, in dB; </li> <li> zh, the horizontal reflectivity, in dBZ; </li> <li> rhovh or rho, the co-polar correlation coefficient, unitless; </li> <li> snr_h or snr, the signal to noise ratio for the horizontal channel in dB; </li> <li> snrv, the signal to noise ratio for the vertical channel, in dB, </li> <li> ngates, the number of unique range gates. </li> </ul> For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives: <ul> <li> x the horizontal distance from the current radar, computed on a line passing through the location of two radars; </li> <li> z the vertical distance from the current radar. </li> </ul>   Usage The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language. They archives were created using the vaex library for Python 3: https://github.com/vaexio/vaex The function vaex.open from the same library can be used for accessing the archives and converting them to vaex.DataFrame.   Campaign-specific information Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.   dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5 Contains PPI scans at 90° elevation for the HYMEX campaign. Location information: <ul> <li> Latitude: 44.61° N </li> <li> Longitude: 4.55° E </li> <li> Altitude: 604 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: Z-PHI method </li> <li> Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign. </li> </ul>   dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5 Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign. Location information: <ul> <li> Latitude: 46.81° N </li> <li> Longitude: 6.94° E </li> <li> Altitude: 496 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5 Contains PPI scans at 90° elevation for the DAVOS campaign. Location information: <ul> <li> Latitude: 46.82° N </li> <li> Longitude: 9.82° E </li> <li> Altitude: 2220 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5 Contains PPI scans at 90° elevation for the APRES3 campaign. Location information: <ul> <li> Latitude: 66.66 S </li> <li> Longitude: 140.00 E </li> <li> Altitude: 40 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 354.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5 Contains PPI scans at 90° elevation for the VALAIS campaign. Location information: <ul> <li> Latitude: 46.12 N </li> <li> Longitude: 7.10 E </li> <li> Altitude: 460 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.27° </li> </ul> Scan setup: <ul> <li> Range resolution: 30 m </li> <li> Range to the first gate: 226.95 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5 Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign. Location information: <ul> <li> Latitude: 46.84° N </li> <li> Longitude: 6.92° E </li> <li> Altitude: 450 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.459 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.273° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 0.0 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.0 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DX50_RHI_201405.hdf5 Contains RHI scans from DX50 the PAYERNE campaign. The remaining information equal to the ones listed for dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5.   dataframe_MXPol_RHI_201405.hdf5 Contains RHI scans from MXPol the PAYERNE campaign. The remaining information is equal to the ones listed for dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5.