Find research datasets worth reusing

opencc-by-4.0Feb 2017View details →

Poynting Robertson Drag timescales

Taken from my thesis: Time it takes for dust at a stellocentric radial distance to spiral into the star due to P-R drag. the model for these curves are based on the derivations of Burns et al. (1979): t_pr = 400 * (rd^2/Mstar)/beta. This relations can be derived from Equation 1.2 in that paper. Large values of beta represent smaller particles and vice-versa. I have plotted the curves for different values of beta and for different spectral types (stellar masses).

opencc-by-4.0Nov 2021View details →

Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."

Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument." The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea.  This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour.  We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle.  This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.

opencc-by-4.0Jul 2021View details →

RE-Lab-Projects/TRY_DE_2015_2045: Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale

Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale Summary: The data set contains the updated test reference years (TRY) of the German Weather Service (DWD). By subdividing into 15 TRY regions, each postcode area can be assigned a representative weather data set. It should be emphasized that in addition to a mean, current test reference year for a region, there is also a year with extreme summer and extreme winter weather. To take climate change into account, there is then a time series for the year 2045 for each test reference year based on the IPCC climate models. This means that a total of 90 weather data sets are available with a one-hour time resolution. In order to use the data in simulations with a temporal resolution of 1min or 15min, the data set was extended by linear interpolation. While this approach is justifiable for air pressure and temperature, for example, it does not depict high fluctuations in solar radiation. Therefore, based on the one-minute open data measurement data set of the Baseline Surface Radiation Network, with an algorithm by Hofmann et. al. the time series of global radiation are newly generated for all test reference years. Another algorithm by Hofmann et. al. was used to calculate the corresponding diffuse radiation times series. Sources: <ul> <li>Raw data from DWD: <a href="https://kunden.dwd.de/obt/">https://kunden.dwd.de/obt/</a> -> <code>1_raw-data</code></li> <li>Synthetic 1min radiation data: <a href="http://pvmodelling.org/">http://pvmodelling.org/</a> -> <code>2_synthetic-radiation</code></li> </ul> How to use or recreate the final dataset: <ol> <li>clone/download this repository</li> <li>unzip the files from the data.zip file <ol> <li><a href="https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip">https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip</a></li> </ol> </li> <li>Use or recreate the final dataset <ol> <li>use: Final datasets are then located in -> <code>3_processed-data</code></li> <li>recreate: run the <code>process-data.py</code></li> </ol> </li> </ol> Test reference stations / regions No. | lon | lat | station | region 1 | 53.5591 | 8.5872 | Bremerhaven | Nordseeküste 2 | 54.0878 | 12.1088 | Rostock | Ostseeküste 3 | 53.5299 | 10.0078 | Hamburg | Nordwestdeutsches Tiefland 4 | 52.3938 | 13.0651 | Potsdam | Nordostdeutsches Tiefland 5 | 51.4562 | 7.0568 | Essen | Niederrheinisch-westfälische Bucht und Emsland 6 | 550.6461 | 7.9426 | Bad Marienburg | Nördliche und westliche Mittelgebirge, Randgebiete 7 | 51.3334 | 9.4725 | Kassel | Nördliche und westliche Mittelgebirge, zentrale Bereiche 8 | 51.7239 | 10.6069 | Braunlage | Oberharz und Schwarzwald (mittlere Lagen) 9 | 50.8233 | 12.9181 | Chemnitz | Thüringer Becken und Sächsisches Hügelland 10 | 50.3226 | 11.9124 | Hof | Südöstliche Mittelgebirge bis 1000 m 11 | 50.4312 | 12.9522 | Fichtelberg | Erzgebirge, Böhmer- und Schwarzwald oberhalb 1000 m 12 | 49.4902 | 8.4637 | Mannheim | Oberrheingraben und unteres Neckartal 13 | 48.2432 | 12.5286 | Mühldorf | Schwäbisch-fränkisches Stufenland und Alpenvorland 14 | 48.6536 | 9.8666 | Stötten | Schwäbische Alb und Baar 15 | 47.4945 | 11.1046 | Garmisch Partenkirchen | Alpenrand und -täler Content <ul> <li>files: 90 test reference years (TRY) <pre><code>15 test reference regions x 3 reference conditions (average year, extreme summer, extreme winter) x 2 reference projections (year 2015 and year 2045) </code></pre> </li> <li>columns per file: <pre><code>datetime [yyyy-MM-dd hh:mm:ss+01:00/02:00] temperature [degC] pressure [hPa] wind direction [deg] wind speed [m/s] cloud coverage [1/8] humidity [%] direct irradiance [W/m^2] diffuse irradiance [W/m^2] synthetic global irradiance [W/m^2] synthetic diffuse irradiance [W/m^2] clear sky irradiance [W/m^2] </code></pre> </li> <li>length: 1 year</li> <li>time increment: 60s / 900s / 3600s</li> </ul> Important hints: <ul> <li>all files in <code>3_processed-data</code> were calculated with the skript <code>process-data.py</code></li> <li>A value with, for example, a timestamp 12:00:00 represents the mean value from this timestamp until the following timestamp.</li> <li>datetime column is in CET / CEST</li> </ul>

opencc-by-4.0May 2021View details →

Theta and alpha power across fast and slow timescales in cognitive control

This dataset contains the raw EEG data collected in a study investigating the neural signatures of extensive training. Specifically, 30 subjects performed a simple stimulus-action association task in which they learned to respond with one out of two key presses to a stimulus presented on screen. In each block, four different stimuli were presented, meaning that half of the stimuli was associated with a right response (press 'j') and the other half was associated with a left response (press 'f'). To investigate distinct timescale, stimuli repeated both within experimental blocks (each stimulus 8 times), and one set of stimuli repeated across experimental blocks (8/16 blocks contained the same four stimuli). This dataset contains the raw EEG data collected using a BioSemi 64 channel setup. Also, six external electrodes were used to measure eye activity (left and right mastoid; lateral canthi of both eyes; above and below left eye). The electrodes were placed using the 10-20 system, and this system contained a posterior CMS-DLR electrode combination. The SUBID.npy files contain the behavioral data of the experiment phase (512 trials; i.e. exercise phase data is not included).The SUBID.bdf files contain EEG data of the experiment phase (512 trials; no data was collected during the exercise phase). Finally, the files titled "theta_alpha_beta_behavioural" are used in the analysis and plotting scripts (see GitHub).

opencc-zeroNov 2023View details →

Data from: Multiple timescales of streambed flux variability in two perennial mountain-front streams

Streambed fluxes are highly variable through time and space, having a range of implications for stream‐aquifer processes. This study investigated streambed fluxes over a 3‐year study period to characterize short‐ and long‐term variations and related implications for seepage recharge and hyporheic exchange. Time series of streambed fluxes were estimated through Darcy‐based methods using water level and temperature inputs from shallow (<1.5 m) nested streambed piezometers installed in two mountain‐front streams in Colorado, USA. Three predominant temporal scales of variability were characterized: sub‐daily (<1 day), daily (>1 day; <1 year), and interannual (>1 year). Temporal variability was quantified using the median absolute deviation (MAD), a statistical measure that is resistant to extreme values associated with short‐duration events. Sub‐daily variability (MADS‐D) was related to ET, temperature‐induced changes in hydraulic conductivity, and variable stream stage, and was quantified using the MAD of detrended fluxes (daily mean subtracted). Daily variability (MADD), calculated as the MAD of daily median fluxes for a given year, exceeded sub‐daily variability, and was influenced by strong seasonality at some sites. For individual sites and water years, the ratio of MADS‐D to MADD ranged from 0.03 to 0.70, with an average of 0.25. Annual median fluxes at each site varied across years, but typically remained consistent in order of magnitude and direction. Results reveal a strong linear correlation between the daily variability and the median annual flux at individual sites. Within a year, summer months characterized by stronger losing conditions showed greater overall variability. 1D numerical heat and flow modelling was performed to calibrate hydraulic parameters and to investigate temperature‐related controls for sub‐daily variations. We discuss implications of documented temporal variability for analyses of hyporheic exchange and groundwater recharge. Results provide a basis for quantifying temporal variations in streambed fluxes and highlight the extent to which fluxes vary over multiple timescales.

opencc-zeroJan 2024View details →

Lifetimes and timescales of tropospheric ozone: Ozone emission experiments

The lifetime of tropospheric O3 is difficult to quantify because we model O3 as a secondary pollutant, without direct emissions. For other reactive greenhouse gases like CH4 and N2O, we readily model lifetimes and timescales that include chemical feedbacks based on direct emissions. Here, we devise a set of artificial experiments with a chemistry-transport model where O3 is directly emitted into the atmosphere at a quantified rate. We create three primary emission patterns for O3, mimicking secondary production by surface industrial pollution, that by aviation, and primary injection through stratosphere-troposphere exchange (STE). The perturbation lifetimes for these O3 sources includes chemical feedbacks and varies from 6 to 27 days depending on source location and season. Previous studies derived lifetimes around 24 days estimated from the mean odd-oxygen loss frequency. The timescales for decay of excess O3 varies from 10–20 days in NH summer to 30–40 days in NH winter. For each season, we identify a single O3 chemical mode applying to all experiments. Understanding how O3 sources accumulate (the lifetime) and disperse (decay timescale) provides some insight into how changes in pollution emissions, climate, and stratospheric O3 depletion over this century will alter tropospheric O3. This work incidentally found two distinct mistakes in how we diagnose tropospheric O3, but not how we model it. First, the chemical pattern of an O3 perturbation or decay mode does not resemble our traditional view of the odd-oxygen family of species that includes NO2. Instead, a positive O3 perturbation is accompanied by a decrease in NO2. Second, heretofore we diagnosed the importance of STE flux to tropospheric O3 with a synthetic 'tagged' tracer O3S, which had full stratospheric chemistry and linear tropospheric loss based on odd-oxygen loss rates. These O3S studies predicted that about 40 % of tropospheric O3 was of stratospheric origin, but our lifetime and decay experiments show clearly that STE fluxes add about 8 % to tropospheric O3, providing further evidence that tagged tracers do not work when the tracer is a major species with chemical feedbacks on its loss rates, as shown for CH4.

Fig. 4 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale

Fig. 4 - Paleogeographic distribution of the conodont Chiosella timorensis around the Olenekian-Anisian/Early-Middle Triassic boundary (based on Map 48 from Scotese, 2014): Numbers of figured occurrences are those from Fig. 3. A – Tethys occurrences; B – Panthalassa occurrences; C – Arctic occurrences, uncertain; D and E – primary location of allochthonous occurrences in Japanese Islands and Far East Russia, and their tectonic transport pathways. 1 - Chios, Greece; 2 - Perşani Mountains; 3 - Capelluzzo, Southern Apennines; 4 - Sosio Valley, Sicilia; 5 - Kçira, Albania; 6 - Deşli Caira, Romania; 7 - Gebze, Turkey; 8 - Wadi Alwa, Oman; 9 - Salt Range, Pakistan; 10 - Dolpo, Nepal; 11- Spiti, India; 12 - Kashmir, India; 13 - Southeastern Pamirs, Tajikistan; 14 – Tulong and Dibucuo, Tibet; 15 - South China, Guandao, Ganheqiao, Qingyan; 16 -South China, Wantou and Youping; 17 - Kodiang, Malaysia; 18 - Western Thailand; 19 - Kamura and Taho-attol carbonates; 20 - Honshu Island-pelagic chert; 21 - Koryak Upland; 22 - Zyryanka, Kolyma river; 23 - Dalnegorsk, Sikhote-Alin; 24 - Chernaya River, South Primorye; 25 - Mount Lilu, Timor-Leste; 26 - Nifukoko, West Timor; 27 - Western Australia, Carnarvon, Perth & Canning basins; 28 - Northwestern Nevada; 29 - Great Valley, California; 30 - Sheep Creek, Idaho; 31 - Ursula Creek and Subsurface British Columbia; 32 - Quesnellia; 33 - Stikinia; 34 - Brooks Range, Alaska; 35 - Svalbard.

Fig. 3 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale

Fig. 3 - Present-day geographic distribution of the conodont Chiosella timorensis occurrences and basic references. A – Tethys-originating occurrences (1-18, 25-27); B – Panthalassa-originating occurrences (19-24, 28-33; C – Arctic occurrences, uncertain (34-35). Europe: 1 - Chios, Greece (Nicora, 1977; Assereto et al., 1980); 2 - Perşani Mountains, Romania (Mirăuţă & Gheorghian, 1978); 3 - Capelluzzo, Southern Apennines, Italy (Mietto et al., 1991); 4 - Sosio Valley, Sicilia, Italy (Kozur et al.,1995); 5 - Kçira, Albania (Muttoni et al., 1996, 2019); 6 - Deşli Caira, Romania (Grădinaru et al., 2007; Orchard et al., 2007a); Southwest Asia: 7 - Gebze, Turkey (Kiliç, 2021); 8 - Wadi Alwa, Oman (Orchard, 1994a); Himalayas: 9 - Salt Range, Pakistan (Sweet 1970a, 1973); 10 - Dolpo, west Nepal (Kovács & Kozur, 1980); 11 - Spiti, India (Krystyn et al., 2007; Sue et al., 2021); Garzanti et al., 1995); 12 - Kashmir, India (Chhabra, 1981; Matsuda, 1983); Central Asia: 13 - Southeastern Pamirs, Tajikistan (Bragin et al., 2016); Eastern Asia: 14 – Tulong and Dibucuo, Tibet (Tian 1982; Chen A-F et al., 2021; Wu G C. et al., 2007); 15 - South China, Guandao (Orchard et al., 2007b), Ganheqiao and Qingyan (Yao et al., 2011); 16 - South China, Wantou and Youping (Chen Y et al., 2020); Southeast Asia: 17 - Kodiang, Malaysia (Koike, 1973, 1982); 18 - Western Thailand (Kemper et al., 1976); Japanese Islands: 19 - Kamura and Taho attol carbonates, Kyushu Island and Shikoku Island (Hirsch & Ishida, 2002; Zhang L et al., 2019a; Ha et al., 2021); 20 - Honshu Island-pelagic chert (Muto et al., 2018; Muto, 2021); Far East Russia: 21 - Koryak Upland (Bragin, 1991); 22 - Zyryanka, Kolyma river; Klets (1998); 23 – Dalnegorsk, Sikhote-Alin (Buryi, 1989, 1997; Klets, 1995); 24 - Chernaya River, South Primorye (Buryi, 1979); Timor-Leste: 25 - Mount Lilu (Nogami, 1968); West Timor: 26 - Nifukoko (Orchard, 1994a); Western Australia: 27 - Carnarvon, Perth & Canning basins (McTavish, 1973; Nicoll et al., 2007; Gorter et al., 2019); Western United States: 28 - Northwestern Nevada (Collinson & Hasenmueller, 1978; Orchard, 1994a; Paull & Paull, 1998; Goudemand et al., 2012); 29 - Great Valley, California (Wardlaw & Jones, 1980); 30 - Sheep Creek, Idaho (Paull, 1988); Western Canada: 31 - Ursula Creek, British Columbia (Orchard & Tozer, 1997a); Subsurface British Columbia (Golding, 2014, 2021b); 32 - Quesnellia (Orchard & Tozer, 1997a); 33 - Stikinia (Orchard & Tozer, 1997a); Arctic North America: 34 - Brooks Range, Alaska (Wardlaw & Jones, 1980). Arctic Europe: 35 - Svalbard (Nakrem et al., 2008).

Fig. 2 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale

Fig. 2 - Suggested Olenekian-Anisian/Early-Middle Triassic chronostratigraphy and the presumed OAB, marked in blue, are added to the Chinese Wantou section (Chen Y et al., 2020, fig. 3), based on the re-interpretation of conodont events.

Fig. 5 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale

Fig. 5 - Chronostratigraphic calibration and the Spathian-Aegean/Olenekian-Anisian/Early-Middle Triassic boundary in the Deşli Caira section, Romania. Conodont biochronology - (A) after Orchard et al. (2007a), and (B) after Golding (2021). The boundary is constrained by the ammonoid biochronology – (C) after Grădinaru, in Grădinaru & Gaetani (2019). Legend: 1 - thick-bedded limestone; 2 - ammonoid occurrence.

Fig. 4 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale

Fig. 4 - Paleogeographic distribution of the conodont Chiosella timorensis around the Olenekian-Anisian/ Numbers of figured occurrences are those from Fig. 3. A – Tethys occurrences; B – Panthalassa occurrences; C – Arctic occurrences, uncertain; D and E – primary East Russia, and their tectonic transport pathways. 1 - Chios, Greece; 2 - Perşani Mountains; 3 - Capelluzzo, Southern Apennines; 4 - Sosio Valley, Sicilia; 5 - Alwa, Oman; 9 - Salt Range, Pakistan; 10 - Dolpo, Nepal; 11- Spiti, India; 12 - Kashmir, India; 13 - Southeastern China, Guandao, Ganheqiao, Qingyan; 16 -South China, Wantou and Youping; 17 - Kodiang, Malaysia; 18 shu Island-pelagic chert; 21 - Koryak Upland; 22 - Zyryanka, Kolyma river; 23 - Dalnegorsk, Sikhote-Alin 26 - Nifukoko, West Timor; 27 - Western Australia, Carnarvon, Perth & Canning basins; 28 - Northwestern Ursula Creek and Subsurface British Columbia; 32 - Quesnellia; 33 - Stikinia; 34 - Brooks Range, Alaska;

Fig. 1 in The Olenekian-Anisian/Early-Middle Triassic Boundary, And Assessment Of The Potential Of Conodonts For Chronostratigraphic Calibration Of The Triassic Timescale

Fig. 1 - Revised Lower-Middle Triassic chronostratigraphy in the Albanian Kçira-A section, marked ord. 1 – ammonoid record in fig. 5 of Muttoni et al. (2019); 2 – conodont record in fig. 4 of Muttoni et fig. 8 of Muttoni et al. (2019). Line A - The Olenekian-Anisian/Early-Middle Triassic boundary in Muttoni et al. (2019), based on proxy for the nominated boundary; Line B - the herein assumed base of the Aegean Substage (AEG), Procarnites kokeni (Arthaber, 1908), with the FO of Ch. timorensis positioned well below the Line B; indicated by Germani et al. (1997); Line D - Bithynian-Pelsonian boundary, as indicated by Germani

opencc-by-4.0Nov 2021View details →

Data for Roles of Granularity and Timescales in Debris Flow Hazards on Alluvial Fans

This dataset includes the digital elevation models (DEM) for the 9 debris flow fan experiments and the slope map data for the 9 debris flow fan experiments and 2 field cases (the Straight Fan and Piute Fan in White Mountain, CA). These data are stored as GeoTIFF files that include information on mesh coordinates. Please read the Data_Information.pdf for the details of the data file contents, duration, sediment contents, flow/discharge/input rates, and mesh size.

opencc-by-4.0Jan 2022View details →

Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"

Processed data and code for reproducing the main results and figures of the paper "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions". We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019). Reference A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, ‘‘Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms’’ in proceedings of the ACM/IEEE Design, Automation, and Test in Europe Conference (DATE), Florence, Italy, March 25-29, 2019.

opencc-zeroJun 2022View details →

A target capture approach for phylogenomic analyses at multiple evolutionary timescales in rosewoods (Dalbergia spp.) and the legume family (Fabaceae)

Understanding the genetic changes associated with the evolution of biological diversity is of fundamental interest to molecular ecologists. The assessment of genetic variation at hundreds or thousands of unlinked genetic loci forms a sound basis to address questions ranging from micro- to macro-evolutionary timescales, and is now possible thanks to advances in sequencing technology. Major difficulties are associated with i) the lack of genomic resources for many taxa, especially from tropical biodiversity hotspots, ii) scaling the numbers of individuals analyzed and loci sequenced, and iii) building tools for reproducible bioinformatic analyses of such datasets. To address these challenges, we developed a set of target capture probes for phylogenomic studies of the highly diverse, pantropically distributed and economically significant rosewoods (Dalbergia spp.), explored the performance of an overlapping probe set for target capture across the legume family (Fabaceae), and built a general-purpose bioinformatics pipeline. Phylogenomic analyses of Dalbergia species from Madagascar yielded highly resolved and well supported hypotheses of evolutionary relationships. Population genomic analyses identified differences between closely related species and revealed the existence of a potentially new species, suggesting that the diversity of Malagasy Dalbergia species has been underestimated. Analyses at the family level corroborated previous findings by the recovery of monophyletic subfamilies and many well-known clades, as well as high levels of gene tree discordance, especially near the root of the family. The new genomic and bioinformatics resources will hopefully advance systematics and ecological genetics research in legumes, and promote conservation of the highly diverse and endangered Dalbergia rosewoods.

opencc-zeroJul 2022View details →

Timescale reverses the relationship between host density and infection risk

Host density shapes infection risk through two opposing phenomena. First, when infective stages are subdivided among multiple hosts, greater host densities decrease infection risk through "safety in numbers". Hosts, however, represent resources for parasites, and greater host availability also fuels parasite reproduction. Hence, host density increases infection risk through "density-dependent transmission". Theory proposes that these phenomena are not disparate outcomes but occur over different timescales. That is, higher host densities may reduce short-term infection risk, but because they support parasite reproduction, may increase long-term risk. We tested this theory in a zooplankton-disease system with laboratory experiments and field observations. Supporting theory, we found that negative density-risk relationships ('safety in numbers') sometimes emerged over short timescales, but these relationships reversed to 'density-dependent transmission' within two generations. By allowing parasite numerical responses to play out, time can shift the consequences of host density, from reduced immediate risk to amplified future risk.

opencc-zeroApr 2024View details →

Data for: Timescales of Autogenic Noise in River Bedform Evolution and Stratigraphy

Bedforms are ubiquitous features on alluvial river channels. Bedform deposits—fluvial cross strata— are the fundamental sedimentary structures of the rock record on Earth and Mars. Bedform evolution and preserved cross strata respond to floods; however, it is unclear which flood durations are likely to be represented in bedform evolution and cross strata. To address this, we quantified the structure of autogenic noise in bedform evolution using high-resolution spatiotemporal data from a steady-state, physical experiment of bedform evolution. The data herein accompanies the manuscript "Timescales of Autogenic Noise in Bedform Evolution and Fluvial Cross Strata " by Vamsi Ganti, Madeline M. Kelley, Debsmita Das and Robert C. Mahon. In this manuscript, we quantified the scales of autogenic noise in sediment efflux, bedform evolution, and preserved deposition rates in fluvial cross strata. We accomplish this using a steady-state experiment of bedform evolution and perform spectral analysis of bed elevation, sediment efflux and preserved deposits. We find that bedform-group (quasi-stable collection of bedforms) turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata.