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518 results for “cycling data”
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Data from: Adaptive division of growth and development between hosts in helminths with two-host life cycles
<p>Parasitic worms (helminths) with complex life cycles divide growth and development between successive hosts. Using data from 597 species of acanthocephalans, cestodes, and nematodes with two-host life cycles, we found that helminths with larger intermediate hosts were more likely to infect larger, endothermic definitive hosts, although some evolutionarily shifts in definitive host mass occurred without changes in intermediate host mass. Life-history theory predicts parasites to shift growth to hosts in which they can grow rapidly and/or safely. Accordingly, helminth species grew relatively less as larvae and more as adults if they infected smaller intermediate hosts and/or larger, endothermic definitive hosts. Growing larger than expected in one host, relative to host mass/endothermy, was not associated with growing less in the other host, implying a lack of cross-host tradeoffs. Rather, some helminth orders had both large larvae and large adults. Within these taxa, though, size at maturity in the definitive host was unaffected by changes to larval growth, as predicted by optimality models. Parasite life-history strategies were mostly (though not entirely) consistent with theoretical expectations, suggesting that helminths adaptively divide growth and development between the multiple hosts in their complex life cycles.</p>
Hybrid Life-Cycle Assessment Literature Review Data
<p>Results from a literature review of hybrid life-cycle assessment. Details the underestimation of purely process-based life-cycle assessment and the diversity in terminology used by authors.</p>
Data for "High-temperature low-cycle fatigue and fatigue-creep behaviour of Inconel 718 superalloy: Damage and deformation mechanisms"
<p>Title of dataset: Data for "High-temperature low-cycle fatigue and fatigue-creep behaviour of Inconel 718 superalloy: Damage and deformation mechanisms"<br>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz<br>Date of data collection: The data were collected from the start of 2021 to the end of 2023.<br>File name structure: The data within the folder "SEM" are images of microstructural observations of selected specimens. The data within the folder "FATIGUE_LIFE" include the fatigue lifetimes, as well as the stress and strain amplitudes at mid-life, of all investigated specimens.</p> <p>See https://doi.org/10.1016/j.ijfatigue.2024.108369 for the associated article and a detailed description of the methods.</p>
Data for: Activity of predators in seabird colonies decreases during the darkest compared to the brightest phase of the diel cycle below, but not above, the Arctic Circle
<p>The description of the data and how they were collected is in the associated open access publication: Huffeldt, N.P., F.M. van Beest, H.L. Kenyon, J. Danielsen, and T. Guilford. (2024) Activity of predators in seabird colonies decreases during the darkest compared to the brightest phase of the diel cycle below, but not above, the Arctic Circle. <em>Arctic, Antarctic, and Alpine Research</em> 56: 2367262. https://doi.org/10.1080/15230430.2024.2367262</p>
Daily solar data and sunspot region summary of 23-24 solar cycle
<p>This dataset contains records of daily solar data as well as data collected from magnetic classes...</p> <p>This dataset was assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.</p> <p>The date the data was assembled is 2017-01-15 (yyyy-mm-dd).</p> <p>The original data source is provided by the Space Weather Prediction Center - SWPC, which is linked to the National Oceanic and Atmospheric Administration - NOAA from US Department of Commerce.</p> <p>Data description:</p> <ul> <li><strong>radio_flux_10.7cm</strong>: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.</li> <li><strong>sesc_sunspot_number</strong>: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.</li> <li><strong>sunspot_area</strong>: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.</li> <li><strong>goes15_xray_bkgd_flux</strong>: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.</li> <li><strong>mwl_alpha</strong>: binary attribute indicating the presence of apha magnetic class in any observed spot.</li> <li><strong>mwl_beta</strong>: binary attribute indicating the presence of beta magnetic class in any observed spot.</li> <li><strong>mwl_gamma</strong>: binary attribute indicating the presence of gamma magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma</strong>: binary attribute indicating the presence of beta-gamma magnetic class in any observed spot.</li> <li><strong>mwl_delta</strong>: binary attribute indicating the presence of delta magnetic class in any observed spot.</li> <li><strong>mwl_beta_delta</strong>: binary attribute indicating the presence of beta-delta magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma_delta</strong>: binary attribute indicating the presence of beta-gamma-delta magnetic class in any observed spot.</li> <li><strong>mwl_gamma_delta</strong>: binary attribute indicating the presence of gamma-delta magnetic class in any observed spot.</li> <li><strong>c_class_flares</strong>: number of c class flares observed.</li> <li><strong>m_class_flares</strong>: number of m class flares observed.</li> <li><strong>x_class_flares</strong>: number of x class flares observed.</li> </ul> <p>The data collected refer to the period between january 01, 1997 to january 15, 2017.</p> <p> </p>
Data from paper: "Life cycle of bamboo in the southwestern Amazon and its relation to fire events".
<p>This is the dataset from the paper "Life cycle of bamboo in the southwestern Amazon and its relation to fire events".</p> <p> </p> <p>It contains:</p> <p>- The processed MODIS (MAIAC) time series for the southwest Amazon, already processed and ready to use for the modeling (files such as bamboo_ts_annual_2000-2017_band[...]). MODIS (MAIAC) composites for south america is not provided here because of its huge file size (contact ricds@hotmail.com).</p> <p>- Final bamboo die-off data from 2001-2017 using the simple bilinear model (combination from band 2 and 5, p-value < 0.001). The data used for Figure S3. File: "Theoric_death_merged_band2_band5.tif"</p> <p>- Bamboo die-off detected from 2001-2017 using the simple bilinear model (files Theoric_death_year_band2 and Theoric_death_year_band5, and Theoric_pvalue_band2 and Theoric_pvalue_band5). To obtain the same map as in the paper, must apply the p < 0.001 over the death year map.</p> <p>- Bamboo spatial distribution obtained by the die-off detection and live detection. In this map, values equal to 0, 1 and 2 correspond to non-bamboo, live bamboo and dead bamboo forests.</p> <p>- Bamboo die-off predictions from 2000-2028 using the empirical curves (files Empirical_death_year_band2_curves2 and Empirical_death_year_band5_curves2, and Empirical_p_value_band5_curves2 and Empirical_p_value_band5_curves2). To obtain the same map as in the paper, must apply the p < 0.001 over the death year map.</p> <p> </p> <p>More information contact Ricardo Dalagnol (ricds@hotmail.com).</p>
Short-term solar data stream of 23-24 cycle
<p>These datasets contain records of daily solar data as well as data collected from magnetic classes.</p> <p>The datasets were assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.</p> <p>The date the data was assembled is 2017-01-15 (yyyy-mm-dd).</p> <p>The original data source is provided by the Space Weather Prediction Center - SWPC, which is linked to the National Oceanic and Atmospheric Administration - NOAA from US Department of Commerce.</p> <p>The data collected refer to the period between january 01, 1997 to january 15, 2017.</p> <p>Features included:</p> <ul> <li><strong>radio_flux_10.7cm</strong>: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.</li> <li><strong>sesc_sunspot_number</strong>: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.</li> <li><strong>sunspot_area</strong>: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.</li> <li><strong>goes15_xray_bkgd_flux</strong>: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.</li> <li><strong>mwl_alpha</strong>: binary attribute indicating the existence of apha magnetic class in any observed spot.</li> <li><strong>mwl_beta</strong>: binary attribute indicating the existence of beta magnetic class in any observed spot.</li> <li><strong>mwl_gamma</strong>: binary attribute indicating the existence of gamma magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma</strong>: binary attribute indicating the existence of beta-gamma magnetic class in any observed spot.</li> <li><strong>mwl_beta_delta</strong>: binary attribute indicating the existence of beta-delta magnetic class in any observed spot.</li> <li><strong>mwl_beta_gamma_delta</strong>: binary attribute indicating the existence of beta-gamma-delta magnetic class in any observed spot.</li> <li><strong>mwl_gamma_delta</strong>: binary attribute indicating the existence of gamma-delta magnetic class in any observed spot.</li> <li><strong>mwl_delta</strong>: binary attribute indicating the existence of delta magnetic class in any observed spot.</li> </ul> <p>We performed missing data imputation using k-NN over all features. The k-NN used the Gower's distance as its distance coefficient. In addition, we also performed z-score standardization in all features.</p> <p>We designed the data into a sliding time window stream. In other words, we designed the data stream regarding four days before a<em> </em><em>t1 </em>instant (i.e. <em>t5, t4, t3,</em> and <em>t2</em>). Hence, new features were created considering the evolution of data along five days:</p> <ul> <li><em>radio_flux_10.7cm_[t5, t4, t3, t2, t1];</em></li> <li><em>sesc_sunspot_number_[t5, t4, t3, t2, t1];</em></li> <li><em>sunspot_area_[t5, t4, t3, t2, t1];</em></li> <li><em>goes15_xray_bkgd_flux_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_alpha_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_gamma_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_gamma_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_delta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_beta_gamma_delta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_gamma-delta_[t5, t4, t3, t2, t1];</em></li> <li><em>mwl_delta_[t5, t4, t3, t2, t1].</em></li> </ul> <p>We designed our target variable as being the occurrence of at least one flare phenomenon of M or X class in the next 24, 24-48, and 48-72 hours ahead of the <em>t1</em> instant:</p> <ul> <li><em>flare_t1d</em>: occurrence of at least one flare of class M or X in the next 24 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t2d</em>: occurrence of at least one flare of class M or X 24-48 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t3d</em>: occurrence of at least one flare of class M or X 48-72 hours ahead of the <em>t1 </em>instant.</li> </ul>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Short-term solar data stream of 23-24 cycle
<p>These datasets contain records of daily solar data as well as data collected from magnetic classes.</p> <p>The datasets were assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.</p> <p>The date the data was assembled is 2017-01-15 (yyyy-mm-dd).</p> <p>The original data source is provided by the Space Weather Prediction Center (SWPC) linked to the National Oceanic and Atmospheric Administration (NOAA) from US Department of Commerce.</p> <p>The data collected refer to the period between january 01, 1997 to january 15, 2017.</p> <p>Features included:</p> <ul> <li><strong>radio_flux_10.7cm</strong>: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.</li> <li><strong>sesc_sunspot_number</strong>: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.</li> <li><strong>sunspot_area</strong>: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.</li> <li><strong>goes15_xray_bkgd_flux</strong>: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.</li> <li><strong>daily z component wmfr</strong>: daily McIntosh class z component weighted mean flare rate.</li> <li><strong>daily p component wmfr</strong>: daily McIntosh class p component weighted mean flare rate.</li> <li><strong>daily c component wmfr</strong>: daily McIntosh class c component weighted mean flare rate.</li> <li><strong>daily magnetic type wmfr</strong>: daily magnetic type weighted mean flare rate.</li> </ul> <p>We performed missing data imputation using k-NN over all features. In addition, we also performed z-score standardization in all features.</p> <p>We designed the data into a sliding time window stream. In other words, we designed the data stream regarding four days before a<em> </em><em>t1 </em>instant (i.e. <em>t5, t4, t3,</em> and <em>t2</em>). Hence, new features were created considering the evolution of data along five days:</p> <ul> <li><em>radio_flux_10.7cm_[t5, t4, t3, t2, t1];</em></li> <li><em>sesc_sunspot_number_[t5, t4, t3, t2, t1];</em></li> <li><em>sunspot_area_[t5, t4, t3, t2, t1];</em></li> <li><em>goes15_xray_bkgd_flux_[t5, t4, t3, t2, t1];</em></li> <li><em>z_component_wmfr_[t5, t4, t3, t2, t1];</em></li> <li><em>p_component_wmfr_[t5, t4, t3, t2, t1];</em></li> <li><em>c_component_wmfr_[t5, t4, t3, t2, t1];</em></li> <li><em>mag_type_wmfr_[t5, t4, t3, t2, t1].</em></li> </ul> <p>We designed our target variable as being the occurrence of at least one flare phenomenon of C-, M-, or X-class in the next 24, 24-48, and 48-72 hours ahead of the <em>t1</em> instant:</p> <ul> <li><em>flare_t1d</em>: occurrence of at least one flare of class C, M, or X in the next 24 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t2d</em>: occurrence of at least one flare of class C, M, or X 24-48 hours ahead of the <em>t1 </em>instant;</li> <li><em>flare_t3d</em>: occurrence of at least one flare of class C, M, or X 48-72 hours ahead of the <em>t1 </em>instant.</li> </ul>
Data: Testing the mating system model of parasite complex life cycle evolution reveals demographically driven mixed mating
<p>Abstract: Many parasite species use multiple host species to complete development; however, empirical tests of models that seek to understand factors impacting evolutionary changes or maintenance of host number in parasite life cycles are scarce. Specifically, Brown et al.’s (2001) mating system model, which posits multi-host life cycles are an adaptation to prevent inbreeding in hermaphroditic parasites and thus, preclude inbreeding depression, remains untested. The model assumes loss of a host results in parasite inbreeding and predicts host loss can only evolve if there is no parasite inbreeding depression. <a name="_Hlk169780726"></a>We provide the first empirical tests of this model using a novel approach we developed for assessing inbreeding depression from field-collected, parasite samples. The method compares genetically-based, selfing-rate estimates to a demographic-based selfing rate, which was derived from the closed mating system experienced by endoparasites. Results from the hermaphroditic trematode <em>Alloglossidium renale</em>, which has a derived 2-host life cycle, supported both the assumption and prediction of the mating system model as this highly inbred species had no indication of inbreeding depression. Additionally, comparisons of genetic and demographic selfing rates revealed <a name="_Hlk169781073"></a>a mixed mating system that could be explained completely by the parasite’s demography, i.e., its infection intensities.</p>
CESM2.2-8P4Z data supporting Yu et al. (2024): Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types
<p><span>This dataset contains the model output from CESM2.2-8P4Z, used in Yu et al. (2024) and</span><span> </span><span>submitted to</span><span> the Journal of Advances in Modeling Earth Systems (JAMES). These are the last 20-year averaged output files from 310 years of the model simulations, which are analyzed in Yu et al., (2024). CESM2.2-8P4Z contains twelve plankton groups, including eight types of phytoplankton:</span><span> </span><span>1</span><span>) picophytoplankton groups: <em>Prochlorococcus</em>, <em>Synechococcus</em>, picoeukaryotes and diazotrophs; 2) nanophytoplankton groups:</span><span> </span><em><span><em>P</em></span></em><em><span><em>haeocystis</em></span></em><span>, <em>coccolithophores</em></span><span> </span><span>and a generic other nanophytoplankton; 3) micro-sized phytoplankton: diatoms</span><span>; and four types of zooplankton:</span><span> </span><span>small microzooplankton (5-20 u</span><span>m, such as ciliates, nanoflagellates), large microzooplankton (20-200 u</span><span>m, such as copepod nauplii, small dinoflagellates etc.), mesozooplankton (200-2000 u</span><span>m, such as smaller copepod, large dinoflagellates) and macrozooplankton (>2000 u</span><span>m, such as larger copepod, krill).</span><span> </span><span>The MARBL-8P4Z model improves seasonal simulation of the spring bloom compared with more simplified MARBL configurations, benefiting from dampened diatom blooms at higher latitudes due to a combination of bottom-up and top-down drivers.</span></p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Data Related to Osorio-Forero, Foustoukos, Cardis et al., "Noradrenergic locus coeruleus activity functionally partitions NREMS to gatekeep the NREM-REM cycle"
<p>This Zenodo Upload contains the Transparent Data Files for an updated version of the manuscript currently published in Nature Neuroscience</p> <p>and entitled </p> <p><em>'</em>Infraslow noradrenergic locus coeruleus activity fluctuations control are gatekeepers of the NREM–REM sleep cycle' </p> <p>published by the authors as indicated in the author list.</p>
Supplementary data and simulation code - "ADCY10 is a key regulator of cell cycle control"
<p>Experimental FACS data of cell cycle analysis: The folder raw_data_FACS contains the FACS data for different concentrations of bicarbonate (hco3-_rawdata_all2.csv) and for the KH7 experiments (kh7_rawdata_all.csv). The folder summarized_data_input4analysis contains the csv-files that where used for the Bayesian analysis of cell cycle control. Details are given in the headings of the files.</p> <p>We also set up a webpage, where simulations of the suggested mathematical model of the cell cycle can be performed:</p> <p><a href="https://cellcycle-simulation2.herokuapp.com/">https://cellcycle-simulation2.herokuapp.com/</a></p> <p>The results may be used for prediction or experimental design.</p> <p> </p>
EEG/LFP/EMG data from freely-behaving rats across the sleep/wake cycle (includes manual identification of behavioral state in 4s epochs)
<p>Open-access data set that contains raw EEG/LFP/EMG data recorded from freely-behaving rats. 40 complete 24-hr recordings across 10 male, Sprague Dawley rats are included. Additionally, manual behavioral state classification is provided for all recordings in 4s epochs. These data form the testing and training set for a recently accepted article (Ellen J.G. and Dash, M.B. <em>An automated neural network for automated behavioral state classification rats</em>, peerJ (<em>in press)</em>) which details an open-source artificial neural network for automated classification of behavioral state. R-code for the automated classifier and detailed instructions for its implementation are freely available at: <a href="https://github.com/jellen44/AutomaticSleepScoringTool"><em> https://github.com/jellen44/AutomaticSleepScoringTool</em></a></p> <p>For additional details about the data set, please see the uploaded readme file.</p> <p> </p>
Datasets and OpenLCA foreground data processes for the article: Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment
<p>This data set contains the supplementary data sets (1-3) and exported foreground data processes from OpenLCA for the manuscript “Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment”, submitted to Nature Energy.</p> <p>This repository contains:</p> <ul> <li>Supplementary data set 1: Ancillary calculations and numerical values for HT-Aq DAC</li> <li>Supplementary data set 2: Ancillary calculations and numerical values for TSA DAC</li> <li>Supplementary data set 3: Ancillary calculations and numerical values shown in plots and table 3</li> <li>Foreground data from OpenLCA. OpenLCA process model for different cases of HT-Aq DAC and TSA DAC. To re-run the LCA calculations, OpenLCA (freeware) and the Ecoinvent 3.5 database (license required) need to be installed on a standard desktop computer or laptop with at least 8 GB RAM.</li> </ul>
Data for: Intergenerational genotypic interactions drive collective behavioural cycles in a social insect
<p>Many social animals display collective activity cycles based on synchronous behavioural oscillations across group members. A classic example is the colony cycle of army ants, where thousands of individuals undergo stereotypical biphasic behavioural cycles of about one month. Cycle phases coincide with brood developmental stages, but the regulation of this cycle is otherwise poorly understood. Here, we probe the regulation of cycle duration through interactions between brood and workers in an experimentally amenable army ant relative, the clonal raider ant. We first establish that cycle length varies across clonal lineages using long-term monitoring data. We then investigate the putative sources and impacts of this variation in a cross-fostering experiment with four lineages combining developmental, morphological, and automated behavioural tracking analyses. We show that cycle length variation stems from variation in the duration of the larval developmental stage, and that this stage can be prolonged not only by the clonal lineage of brood (direct genetic effects), but also of the workers (indirect genetic effects). We find similar indirect effects of worker line on brood adult size and, conversely but more surprisingly, indirect genetic effects of the brood on worker behaviour (walking speed and time spent in the nest).</p>
Data from: Non-breeding sites, loop migration and activity patterns over the annual cycle in the Lesser Grey Shrike Lanius minor from a western edge of its range
<p>Raw data from three tracked individuals. Two were tracked with light geolocators (22UL and an incomplete track of 22UH) and one (16KN) with GDL3-PAM multi-sensor logger. All produced by Swisss Ornithological Insitute.</p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.