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30,334 results for “Responsiveness”
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts
<p>Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month).</p> <p>The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv. </p> <p>Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.</p> <p>The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv.</p> <p>A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu.</p> <p><strong>Description of data provided</strong></p> <ol> <li>Achievable_NUTS2_summary.csv <ol> <li>Description <ol> <li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li> </ol> </li> <li>Data input <ol> <li>Full_potential.V9.csv</li> <li>participation_rates_country.csv</li> <li>P_inc_SH.csv</li> <li>P_inc_WH.csv</li> <li>P_inc_HP.csv</li> <li>P_inc_DW.csv</li> <li>P_inc_WM.csv</li> <li>P_inc_TD.csv</li> </ol> </li> <li>Script <ol> <li>NUTS2_acheivable.R</li> </ol> </li> </ol> </li> <li>COP_.1deg_11-21_V1.csv <ol> <li>Description <ol> <li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li> </ol> </li> <li>Data <ol> <li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li> <li>NUTS_RG_01M_2021_3857.shp</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>COP_from_E-OBS.R</li> </ol> </li> </ol> </li> <li>Country dd projections.csv <ol> <li>Description <ol> <li>Assumptions for annual change in CDD and HDD</li> <li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li> <li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li> </ol> </li> </ol> </li> <li>EV NUTS projectionsV5.csv <ol> <li>Description <ol> <li>NUTS2 level EV projections 2018-2050</li> </ol> </li> <li>Data input <ol> <li>EV projectionsV5_ave.csv <ol> <li>Country level EV projections</li> </ol> </li> <li>NUTS 2 regional share of national vehicle fleet <ol> <li>Eurostat - Vehicle Nuts.xlsx</li> </ol> </li> </ol> </li> <li>Script <ol> <li>EVprojections_NUTS_V5.py</li> </ol> </li> </ol> </li> <li>EV_NVF_EV_path.xlsx <ol> <li>Description <ol> <li>Country level – EV share of new passenger vehicle fleet</li> <li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. <em>Carmakers’ Race to Meet the</em>, <em>21</em>.</li> </ol> </li> </ol> </li> <li>EV_parameters.xlsx <ol> <li>Description <ol> <li>Parameters used to calculate future loads from EVs</li> <li>Wunit_EV – represents annual kWh per EV</li> <li>evLIFE_150kkm <ol> <li>number of years</li> <li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li> </ol> </li> <li>Average age/#years assuming 150k life – represents <ol> <li>Number of years</li> <li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li> </ol> </li> </ol> </li> </ol> </li> <li>full_potentialV9.csv <ol> <li>Description <ol> <li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year. <ol> <li>This data has not been adjusted with participation_rates_country.csv</li> <li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li> </ol> </li> </ol> </li> <li>Script <ol> <li>Full_potentialV9.py</li> </ol> </li> </ol> </li> <li>gils projection assumptions.xlsx <ol> <li>Description <ol> <li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li> <li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below. <ol> <li>nflh – full load hours <ol> <li>nflh_ac.csv</li> <li>nflh_cp.csv</li> </ol> </li> <li>wunit – annual energy consumption <ol> <li>Wunit_rf_fr.csv</li> </ol> </li> <li>Pcycle – power demand per cycle <ol> <li>Pcycle_wm.csv</li> <li>Pcycle_dw.csv</li> <li>Pcycle_td.csv</li> </ol> </li> <li>Punit – power damand for device <ol> <li>Punit_ac.csv</li> <li>Punit_cp.csv</li> </ol> </li> <li>r – country level household ownership rates of residential device <ol> <li>rfr.csv</li> <li>rrf.csv</li> <li>rwm.csv</li> <li>rtd.csv</li> <li>rdw.csv</li> <li>rac.csv</li> <li>rwh.csv</li> <li>rcp.csv</li> <li>rsh.csv</li> </ol> </li> <li>Script <ol> <li>openENTRANCE projections.py</li> </ol> </li> </ol> </li> </ol> </li> </ol> </li> <li>heat_pump_hourly_share.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From ENTROS TYNDP – Charts and Figures <ol> <li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li> </ol> </li> </ol> </li> </ol> </li> <li>hourlyEVshares.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From My Electric Avenue Study <ol> <li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li> </ol> </li> </ol> </li> </ol> </li> <li>HP_transitionV2.csv <ol> <li>Description <ol> <li>Used to create Qhp_thermal_MWh_projectedV2.csv</li> <li>Final_energy_15-19 <ol> <li>Average final energy demand for the residential heating sector between 2015-2019</li> </ol> </li> <li>Final_energy_15-19_nonEE <ol> <li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li> </ol> </li> <li>Final_energy_15-19_nonEE_share <ol> <li>share of inefficient heating sources</li> </ol> </li> <li>HP_thermal_2018 <ol> <li>Thermal energy provided by residential heat pumps in 2018</li> </ol> </li> <li>HP_thermal_2019 <ol> <li>Thermal energy provided by residential heat pumps in 2019</li> </ol> </li> <li>See publication for data sources</li> </ol> </li> </ol> </li> <li>Nflh_ac.csv, nflh_cp.csv <ol> <li>See gils projection assumptions.xlsx</li> </ol> </li> <li>nhhV2 <ol> <li>Description <ol> <li>Expected number of households for NUTS2 regions for 2020-2050</li> <li>See publication for data sources</li> </ol> </li> <li>Script <ol> <li>EUROSTAT_POP2NUTSV2.R</li> </ol> </li> </ol> </li> <li>NUTS0_thermal_heat_annum.csv <ol> <li>Description <ol> <li>Country level residential annual thermal heat requirements in kWh</li> <li>Used to determine maximum dispatch in openENTRANCE final V14.py</li> <li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). <em>JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note</em> (No. JRC108244). Joint Research Centre (Seville site).</li> </ol> </li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Description <ol> <li>Maximum capacity – load for a device can never exceed maximum capacity</li> </ol> </li> <li>Data <ol> <li>gils projection assumptions.xlsx</li> </ol> </li> <li>Script <ol> <li>openENTRANCE final V14.py</li> </ol> </li> </ol> </li> <li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv <ol> <li>Description <ol> <li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li> </ol> </li> <li>Data <ol> <li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv <ol> <li>Theoretical maximum reduction / load of the respective device</li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Maximum capacity</li> </ol> </li> </ol> </li> <li>Script <ol> <li>P_increaseV2.py</li> </ol> </li> </ol> </li> <li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv <ol> <li>Description <ol> <li>power demand per cycle kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Punit_ac.csv, Punit_cp.csv <ol> <li>Description <ol> <li>Unit capacities kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Qhp_thermal_MWh_projectedV2.csv <ol> <li>Description <ol> <li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li> <li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li> </ol> </li> <li>Data <ol> <li>HP_transitionV2.csv</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>HP_projection_nuts.py</li> </ol> </li> </ol> </li> <li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv <ol> <li>Description <ol> <li>Household ownership rates</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv <ol> <li>Description <ol> <li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li> <li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li> <li>long run (2011-2021) average NUTS 2 level hdd and cdd</li> </ol> </li> </ol> </li> <li>s_wash nuts_V2.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li> </ol> </li> <li>Data <ol> <li>stamminger_V2.xlsx</li> </ol> </li> <li>Script <ol> <li>S_wash_nuts_V2.py</li> </ol> </li> </ol> </li> <li>Stamminger_2009.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li> <li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li> </ol> </li> </ol> </li> <li>Time_index.csv <ol> <li>Used to create the appropriate timestamp for representative hours</li> </ol> </li> <li>Wunit_rf_fr.csv <ol> <li>Annual energy consumption for refrigeration and freezers</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol>
Light and confocal micrographs on the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, the accumulation of lipid droplets, and the heterologous expression and localisation of signature LD protein homologs to tobacco pollen tubes
<p>These micrographs accompany the work "Environmental gradients reveal stress hubs predating plant terrestrialization", posted as a pre-print on bioRxiv https://doi.org/10.1101/2022.10.17.512551 </p> <p>The light and confocal micrographs show the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, especially their accumulation of lipid droplets (LDs); in confocal micrographs, LDs appeared as distinct structures upon staining with BODIPY.</p> <p>Further confocal micrographs show the heterologous expression and localisation of signature LD protein homologs detected in Mesotaenium endlicherianum SAG 12.97; heterologous expression was carried out in tobacco pollen tubes were also stained with BODIPY and proteins were tagged with mCherry.</p>
Raw data from: "Thermal mismatches explain consumer-resource dynamics in response to environmental warming".
<p>Data from:</p> <p>Álvarez-Codesal, S, Faillace CA, Garreau, A, Bestion, E, Synodinos, AD, & Montoya, JM. 2023. Thermal mismatches explain consumer-resource dynamics in response to environmental warming. Ecology and Evolution.<br> </p> <p>Composed of seven associated datasets:</p> <p>- Algae_Net_Photosynthesis.csv</p> <p>- Daphnia_Ingestion.csv</p> <p>- Algae_Respiration.csv</p> <p>- Daphnia_Ingestion.csv</p> <p>- Daphnia_respiration.csv</p> <p>- Interaction_Strength.csv</p> <p>- Energy_Balance_Ratios_dataset.csv</p> <p> </p> <p>A detailed explanation of the methods is provided in the related Alvarez-Codesal et al. 2023 article, please refer to it for a full understanding of the methods and results.</p> <p>Briefly: our aim was to quantify the impact of warming on consumer-resource interactions, using as consumer <em>Daphnia pulex</em>, and as resources two algae species, <em>Chlamydomonas reinhardtii</em> and <em>Desmodesmus</em> sp.</p> <p>First, we measured thermal dependencies of physiological rates related to energy gain (i.e., ingestion, net photosynthesis) and loss (i.e., respiration) for <em>Daphnia</em> and the algae in basal conditions at eight temperatures. From these, we calculated species energetic balances as net photosynthesis-to-respiration ratio (P/R<sub>r</sub>) for resources, or as ingestion-to-respiration ratio (I/R<sub>c</sub>) for consumers, using predicted values of each (per capita) rate from the models. We used energetic balances as an indicator of how species respond to increasing temperatures, defining intraspecific energetic mismatches when the energetic balance decreases.</p> <p>Second, we inferred interspecific thermal mismatches (inter-TM) by comparing individual energetic balances for both consumer-resource pairs, and identified the thermal mismatch regions, where: 1) trends of energetic balances contrast between the interacting species; and 2) both species reduce their energetic balance with increasing temperature. The inter-TMs were used to increase our qualitative understanding on the outcomes of thermal dependencies of interaction strength.</p> <p>Third, we calculated the natural logarithm of the consumer-resource energetic balance for each interacting pair as the ratio between consumer energetic balance and the resource energetic balance to get qualitative predictions on the trends of interaction strength with temperature.</p> <p>Finally, we measured experimentally thermal dependencies of interaction strength for each consumer-resource pair and verified if our predictions using the thermal mismatches fitted the trends of interaction strengths across temperatures.</p>
Supplemental Data for "Self-similarity of spectral response functions for fractional quantum Hall states"
<p>Scripts and data to supplement the paper "Self-similarity of spectral response functions for fractional quantum Hall states".</p>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
RSE-AUNZ community review: responses to steering committee survey
<p>These are the anonymous responses to the steering committee survey conducted for a review of the Research Software Engineers Australia and New Zealand (RSE-AUNZ) community. <a href="https://doi.org/10.5281/zenodo.8098001">Read the report</a>.<br> <br> This survey is one of 2 surveys conducted for the review, the other being a community survey. <a href="https://doi.org/10.5281/zenodo.8098070">Read the results</a>.</p>
Calcium imaging of odor responses in the fruit fly mushroom body
<p><strong>Abstract</strong></p> <p>This dataset contains olfactory responses in the third stage of the olfactory circuit in fruit flies: the mushroom body. The responses are recorded with the GCaMP3 sensor. The methods used to collect the data and the procedures to process them are presented in detail in Campbell et al., 2013, Journal of Neuroscience. The dataset was also used in a recent manuscript by Srinivasan et al., 2023.</p> <p><strong>Methods</strong></p> <p>Please refer to Campbell et al., 2013, Journal of Neuroscience for details. Here, we present a description of how the data was collected, the odors presented, and the analysis, excerpted from Campbell et al., 2013.</p> <p><strong>Animal preparation</strong></p> <p>Flies carrying the genetically encoded calcium sensor UAS-GCaMP3 (Tian et al., 2009) were crossed with OK107-Gal4 flies (Connolly et al., 1996) to drive GCaMP3 expression in essentially all KCs (Lee and Luo, 1999; Aso et al., 2009). All experiments were conducted on female F1 heterozygotes from this cross, aged 2–5 d post-eclosion. Procedures for animal preparation were as described previously (Turner et al., 2008; Murthy and Turner, 2010; Honegger et al., 2011). Flies were anesthetized temporarily on ice and inserted into a small hole cut in the recording platform. The animal’s head was tilted forward, exposing the olfactory organs to the odor delivery nozzle located on the underside of the plat- form. The fly was fixed in place with fast-drying epoxy (Devcon 5 min epoxy). The top of the fly was bathed in oxygenated saline (Wilson et al., 2004) and the cuticle overlying the brain was dissected away. Air sacs overlying the MBs were pushed aside, but we did not attempt to remove the perineural sheath. To minimize movement of the brain inside the head capsule, we removed the pulsatile organ at the neck and the probos- cis retractor muscles that pass over the caudal aspect of the optic lobes.</p> <p> </p> <p><strong>Odor delivery </strong></p> <p>The following chemicals were used as stimuli: 2-heptanone (CAS #110-43- 0), 3-octanol (CAS #589-98-0), 6-methyl-5-hepten-2-one (CAS #110-93-0), ␣-humulene (CAS #6753-98-6), benzaldehyde (CAS #100-52-7), ethyl lactate (CAS #97-64-3), ethyl octanoate (CAS #106-32-1), hexanal (CAS #66-25-1), isoamyl acetate (CAS #123-92-2), 4-methylcyclo- hexanol (CAS #589-91-3), methyl octanoate (CAS #111-11-5), diethyl suc- cinate (CAS #123-25-1), pentanal (CAS #110-62-3), butyl acetate (CAS #123-86-4), 1-octen-3-ol (CAS #3391-86-4), 1-hepten-3-ol (CAS #4938-52- 7), and pentyl acetate (CAS #628-63-7). Odors were presented using a custom-built delivery system that uses serial air dilutions to control odor concentration while maintaining a constant total airflow of 1 L/min at the fly. Experiments were conducted at an odor dilution of 1:100 or, where appropriate, adjusted to match the concentrations used behaviorally. We used a photo-ionization detector (Aurora Scientific) to match concentrations between the imaging rig and the T-maze and to monitor odor delivery throughout each imaging ex- periment. Odor pulses were created by switching between clean and odorized air streams using a synchronous two-way valve (N-Research). This final valve was located 50 cm from the fly, leading to a delay of 300 ms between valve switching and the odor reaching the fly. The flow path was 1/8 inch in diameter throughout, which enabled the system to work near atmospheric pressure at these flow rates. The distance of the valve from the fly and the large tubing diameter virtually eliminated pressure transients caused by valve switching, as measured by the photo-ionization detector and a hot-wire anemometer.</p> <p><strong>Calcium imaging</strong></p> <p>Two-photon imaging was performed using a Prairie Ultima system (Prairie Technologies) and a Ti-Sapphire laser (Chameleon XR; Coher- ent) tuned to 920 nm delivering 8 –10 mW at the sample. All images were acquired with Olympus water-immersion objectives (LUMPlanFl/IR, 60x, numerical aperture 0.9; LUMPlanFl/IR, 40x, numerical aperture 0.8). Imaging planes were selected to maximize the number of visibleKCs. Typically imaging frames were 300 x 300 pixels, acquired with a pixel dwell time of 1.6 s, yielding frame rates near 3.8 Hz. On average, 120 KCs (range: 60 –170) were monitored in one plane. Custom MATLAB (MathWorks) routines were used to control odor presentation and synchronize stimulus delivery with data acquisition. Data were acquired in 20 s sweeps with a 1 s odor pulse triggered 8 s after sweep onset. The interstimulus interval was 25 s. Stimuli were presented in randomly interleaved fashion, adjusted so that the same odor was never presented twice in succession.</p> <p><strong>Imaging analysis</strong></p> <p>Data were analyzed using MATLAB and R (http://www.R-project.org). To correct for motion within the field of view, frames were aligned using 2D image registration approaches. In many cases, a Fourier-based sub-pixel translation correction was sufficient (Guizar-Sicairos et al., 2008). Some animals required an affine transform to cope with global distortions, such as rotational movement of the brain (Thirion, 1998). Where necessary a nonrigid transform was used to correct more localized dis- tortions (Klein et al., 2010). Fluorescent neural tissue was automatically segmented from the surrounding regions. Pixel intensity values from the area outside this boundary were considered to represent background (tissue autofluorescence plus shot noise) and the mean pixel intensity value from the back- ground was then subtracted from the overall image. To quantify the response of the KCs a small, circular region of interest 6 – 8 pixels in diameter was applied to each cell body. This allowed aver- aging of the pixel intensity values from each cell, treating individual KCs as separate units. Care was taken to ensure that each selected cell re- mained within its region of interest over the whole imaging session. Response amplitudes were calculated as the mean change in fluorescence (dF/F) in the 0.5– 4.5 s window after stimulus onset. A statistical test originally described in Honegger et al. (2011) was used to determine whether a KC responded significantly on a given trial. Briefly, the SD of the baseline activity was obtained 8 s before stimulus onset. The response time course was then smoothed using a five-point running average to control for outliers. The peak dF/F in the 0.5– 4.5 s window after stimulus onset was determined. The response was judged to be significant if this peak was 2.33 SDs greater than the baseline, which corresponds to a one-tailed significance test where alpha = 0.01.</p> <p><br> <strong>References</strong></p> <p>Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers. J Neurogenet 23:156 –172. </p> <p>Connolly JB, Roberts IJ, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ (1996) Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107.</p> <p>Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body. J Neurosci 31:11772–11785.</p> <p>Lee T, Luo L (1999) Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461.</p> <p>Murthy M, Turner GC (2010) In vivo whole-cell recordings in the Drosophila brain. In: Drosophila neurobiology methods: a laboratory manual (Zhang B, Waddell S, Freeman M, eds). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory.</p> <p>Srinivasan, S., Daste, S., Modi, M., Turner, G., Fleischmann, A. & Navlakha, S (2023). Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination. bioRxiv.</p> <p>Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260.</p> <p>Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER, Bargmann CI, Jayaraman V, Svoboda K, Looger LL (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881.</p> <p>Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by Drosophila mushroom body neurons. J Neurophysiol 99:734 –746.</p> <p>Wilson RI, Turner GC, Laurent G (2004) Transformation of olfactory representations in the Drosophila antennal lobe. Science 303:366–370.</p> <p><strong>Usage notes</strong></p> <p>The files are all in csv format, and can be easily opened in R or Python or other programming languages.</p> <p>Please see the README.md file for directions on how to use the data.</p> <p>The dataset included here is broken into two parts. The main dataset was the one that was chiefly used in the Campbell and Srinivasan papers, with the second part containing 7 additional datasets that were used in some figures. A fuller description is available in the README.md file.</p>
Functional potential and evolutionary response to long-term heat selection of bacterial associates of coral photosymbionts
<p>Sequencing reads were assembled using the genome assembler pipeline Shovill v1.1.0. Briefly, the Shovill pipeline included read trimming using Trimmomatic v0.39, de novo assembly with SPAdes v3.15.5 and genome polishing with Pilon v1.24. After the pipeline, additional polishing was performed by mapping the reads back to the contigs with BWA v0.7.17 and sorting the resulting SAM/BAM files using SAMtools v1.15.1. Pilon v1.24 was then used to correct bases, fix mis-assemblies and fill gaps. The reformat.sh script from the Bbmap package v38.76 (-minlength=1000) was used to filter out contigs less than 1000bp. The draft genome assemblies were then annotated with Bakta v1.7.0. </p> <p>Single nucleotide polymorphism (SNP) detection between WT (WTref) and SS (SSref) samples were then performed using snippy v4.6.0, where both WT and SS samples were inputted as the reference genome in turn.</p> <p>A subset of the snippy output files are uploaded here and contain all variants found.</p>
Elevated temperature effects on animal personality: hormonal stress response underlying behavioural differences in the American bullfrog
<p>Dataset for research paper submitted to Animal Behaviour</p> <p>Behavioural_data.csv: raw data for how individual bullfrogs performed in six different trials on an 8-arm maze before and after they were submitted to thermal stress. Behaviours analyzed: movements against the wall of the maze, posture changes, total ambulatory distance (m), and time on the centre of the arena (s).</p> <p>Hormone_data.csv: raw hormone (corticosterone and testosterone) data collected from individual bullfrogs in four different time points: baseline, 12 hours after stress, 24 days after stress, and 47 days after stress.</p> <p>Mass_data.csv: raw mass data collected from individual bullfrogs at the beginning and end of the experiment. SVL = snout-vent length. Body index is calculated as the residuals of a linear regression between mass as dependent variable and SVL as independent variable.</p>
DATASET: Near-Infrared Photothermal Ablation of Biofilms using Protein-Functionalized Gold Nanospheres with a Tunable Temperature Response
<p>This dataset contains the DLS, TEM, temperature data, and other experimental data to accompany the manuscript.</p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2<strong> </strong>lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.</p>
Dataset about response of some ciliate species to resources and light
<p>Communities of seven species of ciliates were grown at 10 different levels of resources and four levels of light. The two treatments were manipulated factorially. Most treatment combinations were replicated once, some twice. The dataset contains the abundances of the seven ciliate species after seven days of growth at the respective levels of resources and light.</p>
Are you scared yet? Variations to cue components elicits differential prey behavioral responses even when gape limited predators are relatively small.
Anti-predator behavior is often evoked based on measurements of risk calculated from sensory cues emanating from predators independent of physical attack. Yet, the exact sensory indices of cues used in risk assessment remain largely unknown. To examine how different predatory cue indices of information are used in risk assessment, we presented prey with various cues from sublethal gape-limited predators. Rusty crayfish (Faxonius rusticus (Girard, 1852)) were exposed to predatory odors from sublethal-sized largemouth bass (Micropterus salmoides (Lacepède, 1802)) to test effects of changing predator abundance, relative size relationships, and total predator length in flow through mesocosms. Foraging, shelter use, and movement behavior were used to measure cue effects. Foraging time depended jointly upon predator abundance and total predator size (p = 0.030). Specifically, high predator abundance resulted in decreased foraging efforts as gape ratio increased. Similarly, sheltering time depended on the interaction between predator abundance and gape ratio when predator abundance was highest (p = 0.020). Crayfish significantly increased exploration time when gape ratio increased (p = 0.010). Thus, this study shows crayfish can use different indices of predatory cues, namely total predator abundance and relative size ratios, in risk assessment but do so in context-specific ways.
Long-term response of wetland plant communities to management intensity, grazing abandonment, and prescribed fire
Isolated, seasonal wetlands within agricultural landscapes are important ecosystems. However, they are currently experiencing direct and indirect effects of agricultural management surrounding them. Because wetlands provide important ecosystem services, it is crucial to determine how these factors affect ecological communities. Here, we studied the long-term effects of land use intensification, cattle grazing, prescribed fires, and their interactions on wetland plant diversity, community dynamics, and functional diversity. To do this, we used vegetation and trait data from a 14-year-old experiment on 40 seasonal wetlands located within semi-natural and intensively managed pastures in Florida. These wetlands were allocated different grazing and prescribed fire treatments (grazed vs. ungrazed; burned vs. unburned). Our results showed that wetlands within intensively managed pastures have lower native plant diversity, floristic quality, evenness, higher non-native species diversity, and exhibited the most resource-acquisitive traits. Wetlands embedded in intensively managed pastures were also characterized by lower species turnover over time. We found that 14 years of cattle exclusion reduced species diversity in both pasture management intensities and had no effect on floristic quality. Fenced wetlands exhibited lower functional diversity and experienced a higher rate of community change both due to an increase in tall, clonal, and palatable grasses. The effects of prescribed fires were often dependent on grazing treatment. For instance, prescribed fires increased functional diversity in fenced wetlands but not in grazed wetlands. Our study suggests that cattle exclusion and prescribed fires are not enough to restore wetlands in intensively managed pastures and further highlights the importance of not converting semi-natural pastures to intensively managed pastures. Our study also suggests that grazing levels applied in semi-natural pastures maintained high plant dive
Data from Citizen science data reveal regional heterogeneity in phenological response to climate in the large milkweed bug, Oncopeltus fasciatus
These data include annotations for life stage, mating behavior, and plant part occupancy of large milkweed bug observations in North America as well as information about climate and environment.
Genome size influences plant growth and biodiversity responses to nutrient fertilization in diverse grassland communities
Experiments comparing diploids with polyploids and in single grassland sites show that nitrogen and/or phosphorus availability influences plant growth and community composition dependent on genome size; specifically plants with larger genomes grow faster under nutrient enrichments relative to those with smaller genomes. However, it is unknown if these effects are specific to particular site localities with speciifc plant assemblages, climates, and historical contingencies. To determine the generality of genome size dependent growth responses to nitrogen and phosphorus fertilisation, we combined genome size and species abundance data from 27 coordinated grassland nutrient addition experiments in the Nutrient Network that occur in the Northern Hemisphere across a range of climates and grassland communities. We found that after nitrogen treatment, species with larger genomes generally increased more in cover compared to those with smaller genomes, potentially due to a release from nutrient limitation. Responses were strongest for C3 grasses and in less seasonal, low precipitation environments, indicating that genome size effects on water-use-efficiency modulates genome size-nutrient interactions. Cumulatively the data suggest that genome size is informative and improves predictions of species’ success in grassland communities.
Comparison of Urban-Rural Tree Growth Response to Climate in the Eastern U.S. for the years 1990-2014
Climate is an important driver of tree growth in forests of the eastern U.S. Urbanization can augment the growing conditions of trees in ways that could change the sensitivity of radial growth to climate stressors such as excessive heat and water stress. This dataset include tree ring chronologies (1990-2014) in the form of basal area increment for canopy oak and maples trees from paired urban and nearby rural reference forest sites in Baltimore, Maryland, New York City, New York, and Boston, Massachusetts. Also included are metrics of heat stress and water stress from 1990-2014.
Nitrogen cycling and metabolic rates of aquacultured and wild Acropora coral from Guam in response to ammonium loading rates during 2020-2022
Rates from aquacultured corals and coral fragments collected in Guam in response to ammonium loading. These data are from two separate experiments, one using aquacultured corals and artificial seawater and light. The second was done with wild collected Acropora pistillata from two reef sites in Guam. The first site was West Hagåtña Bay (N13.479650, E144.741750; N13.479833, E144.741733) which had more nearby urban influences and was near the sewage outfall for the city (Redding et al. 2013). The second site was Luminao Reef (N13.4652417, E144.6477483; N13.465467, E144.648050) which was a more isolated reef on the seaward side of the breakwater for Guam’s major port. We measured respiration, gross primary production, 15N ammonium uptake to corals, related nitrogen cycling fluxes in the tanks (reminerization, net uptake, nitrification).
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.