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47 results for “resource efficiency”
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
Developing circularity, renewability and efficiency indicators for sustainable resource management : propanol production as a showcase
<p>The data used for the exergy calculations in the associated article.</p>
Asymmetric responses of resource use efficiency to previous-year precipitation in a semi-arid grassland
<p>1. Intensified inter-annual fluctuations in precipitation could profoundly impact terrestrial ecosystems. However, how changes previous-year precipitation influence current ecosystem functioning (e.g., resource use efficiency) in semi-arid regions remains unclear.</p> <p>2. In this study, water use efficiency (WUE) and light use efficiency (LUE) were investigated in a multi-year precipitation gradient experiment with seven treatment levels: 20%, 40% and 60% decreases and 20%, 40% and 60% increases in the amount of natural rainfall plus ambient precipitation. Plots receiving 60% less precipitation were representative of extreme dry years whereas the other treatment levels fell within the normal year-to-year range in precipitation change. Measurements made in both the post-treatment period (2013-2015) and the treatment period (2010-2012) provided an opportunity to quantify the legacy effects of precipitation on resource use efficiency (RUE).</p> <p>3. Sensitivities of LUE to previous-year precipitation were not changed among treatments in 2013. However, asymmetric responses of RUEs (i.e., WUE and LUE) to previous-year precipitation were found in 2014-2015. WUE<sub>2014</sub>, WUE<sub>2015</sub>, LUE<sub>2014</sub>, and LUE<sub>2015</sub> responded more strongly to previous normal decreased than increased precipitation. Importantly, they were more sensitive to previous extreme dry year (represented by 60% precipitation reduction) than normal wet year (represented by 60% precipitation increment). Aboveground net primary productivity (ANPP) rather than resource absorption (R<sub>uptake</sub>) drove these asymmetric responses of RUE, and biomass of grasses further explained the asymmetric responses of ANPP.</p> <p>4. This study reveals the non-linear responses of RUE to previous-year precipitation and highlighted that the legacy effects of precipitation on RUE can be ascribed to the changes in vegetation composition. Our findings can facilitate the prediction of the legacy effects of precipitation variation on grassland ecosystem functions in the future.</p>
Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production
<p>These datasets were utilized to generate the figures and tables presented in the paper '<strong>Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production</strong><strong>'</strong>:</p> <p>(1) Fig.1.html: Code for generating Fig. 1.<br>(2) Fig.2.xlsx: Data for Fig. 2.<br>(3) Fig.3.xlsx: Data for Fig. 3.<br>(4) Fig.4.xlsx: Data for Fig. 4.<br>(5) Fig.5.xlsx: Data for Fig. 5.</p> <p>(6) Other .html files are Python code corresponding to different tables and figures.</p>
Environmental stoichiometry mediates phytoplankton diversity effects on communities' resource use efficiency and biomass
<p>Positive biodiversity-ecosystem functioning (BEF) relationships are predicted to increase in strength when high environmental variability allows for complementarity between resource use strategies in diverse communities. This environmental variability can be represented by spatial or temporal variation in nutrient ratios, but resource use efficiency (RUE) and therefore biomass build up of primary producers might be restricted when nutrient ratios are highly imbalanced (i.e., limitation by one nutrient and beyond optimal ratios for growth). Whereas the linkages between ecosystem functioning, diversity and nutrient availability are theoretically well understood, we lack experimental evidence on how phytoplankton diversity affects resource use and biomass under variable nutrient ratios (N:P ratios).</p> <p>Combining a mesocosm and a microcosm experiment we tested diversity effects on ecosystem functioning by exposing a species diversity gradient generated by the loss of rare species in a natural community to different N:P ratios (uniform vs a gradient). The N:P supply ratio gradient also allowed us to evaluate responses across balanced and imbalanced ratios.</p> <p>We found that increased species diversity led to increased community RUE when supplied a gradient of N:P ratios; but restricted to the highest diversity level. However, diversity did not affect RUE under uniform nutrient ratios. The overall phytoplankton biomass and carbon:nutrient ratios responses to diversity reflected the patterns detected for RUE. Contrary to theoretical predictions, RUE was maintained under highest N:P supply ratios (extreme phosphorous limitation) suggesting that imbalanced N:P ratios do not necessarily decrease function. Thus, we showed that the nutrient context influences diversity effects on RUE and biomass.</p> <p><i>Synthesis</i>. Overall, our results suggest that the effect of rare phytoplankton species losses on community RUE and biomass can be compensated by the persistent species when nutrient ratios are uniform, but leads to decreases in ecosystem functioning under variable nutrient ratios. This work provides a first attempt for testing interactions between the nutrient context (including concentrations and ratios) and the diversity of (natural) communities experimentally, which is conceptually understood but poorly tested for phytoplankton.</p>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
Support data for conference paper "The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices"
<p>upport data for conference paper<br>Kovtun, and O. Kovtun, “The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices.” In Proc. 8th International Conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning Workshop, CEUR-WS, vol. 3664, 2024; pp. 273-283.<br>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
Efficient Real-Time Selective Genome Sequencing on Resource-Constrained Devices
<p>This dataset contains the curated nanopore raw signal data in <a href="https://www.nature.com/articles/s41587-021-01147-4">BLOW5 format </a>used to benchmark <a href="https://github.com/beebdev/HARU/">Hardware Accelerated Read Until (HARU)</a>. This dataset was created by using the publicly available datasets: <a href="https://community.artic.network/t/links-to-raw-fast5-fastq-data-for-artic-protocol/17">SARS-CoV-2 SP1</a> (1.382M reads) and <a href="https://ncbi.nlm.nih.gov/sra/SRX11368475">NA12878 PromethION subset</a> (500,000 reads). The tarball when extracted will have the following directory structure:</p> <p>haru-data<br> ├── na12878-rfc1<br> │ ├── blow5-rawsignal<br> │ │ ├── na12878_dna_0.blow5<br> │ │ ├── na12878_dna_100.blow5<br> │ │ ├── na12878_dna_101.blow5<br> │ │ ├── na12878_dna_102.blow5<br> │ │ ├── ...<br> │ └── reference<br> │ └── rfc1.fa<br> └── SARS-CoV-2-sp1<br> ├── blow5-rawsignal<br> │ ├── readgroup0<br> │ │ ├── reads_0_0.blow5<br> │ │ ├── reads_0_10.blow5<br> │ │ ├── reads_0_11.blow5<br> │ │ ├── reads_0_12.blow5<br> │ │ ├── ...<br> │ └── readgroup1<br> │ ├── reads_1_0.blow5<br> │ ├── reads_1_10.blow5<br> │ ├── reads_1_11.blow5<br> │ ├── ....<br> └── reference<br> └── nCoV-2019.reference.fasta</p> <p>nCoV-2019.reference.fasta is the SARS-CoV-2 MN908947.3 reference genome. rfc1.fa is the genomic region hr4:39262456-39391375 extracted from hg38 human genome. </p> <p> </p> <p> </p>
HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. Study dataset
<p>The publication and research associated with this dataset are currently under review in the Journal of Network and Computer Applications.</p> <p>In that research, we present HADES, an NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. HADES is an OSM (Open Source MANO) extension that allows the configuration of virtual network functions and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring the quality of service.</p> <p>This dataset contains the following:</p> <ul> <li>1 CSV file with execution time results of the iTAREA module for (60) different problem sizes and (3) execution environments.</li> <li>1 CSV file with energy consumption results of HADES deployments and other 5 assignment policies: First-fit, Random-fit, Fastest-fit, Best-fit, and kube-scheduler (the Kubernetes’ assignment policy).</li> </ul> <p>This work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15 and IRIS PID2021-122812OB-I00 (MCI/AEI).</p>
ODYM-RECC Copper dataset, used for sector-level estimates for global future copper demand and the potential for resource efficiency
<p>Complete Model database with 105 parameter files used in the ODYM-RECC Copper model (github: https://github.com/SteffiKlose/ODYM-RECC-Copper.git) used for Sector-level estimates for global future copper demand and the potential for resource efficiency (<a href="https://doi.org/10.1016/j.resconrec.2023.106941">https://doi.org/10.1016/j.resconrec.2023.106941</a>)</p> <p>This dataset is based on the Database of the ODYM-RECC v2.4 model, used for the GLOBAL case study on material efficiency and climate change mitigation (https://doi.org/10.5281/zenodo.4671644)</p>
Data from: Nitrogen reduction causes shifts in winter and spring phytoplankton composition and resource use efficiency in a large subtropical lake in China
<p>Aquatic ecosystems occasionally show ecological thresholds, defined as the point at which there is a sudden shift in production, trait or biomass or where changes in an environmental driver create nonlinear responses at the ecosystem level. Previous studies of lakes have mainly focused on how reduction in particularly phosphorus (P) concentrations helps to create a shift in lakes from a turbid to a clear state (re-oligotrophication), whereas the effect of nitrogen (N) reduction is less well studied. Here, by analysing a 28-year monthly monitoring dataset (from December 1991 to November 2019) from the subtropical, large eutrophic Lake Taihu, China, we identified a sudden shift in phytoplankton biomass and composition that coincided with a pronounced change in ecosystem functions, for example, resource use efficiency (RUE), during a period with reduction of the external nutrient loading. The changes were particularly strong in winter–spring where a sudden decrease in N concentrations was accompanied by a sudden increase in diatom biomass and phytoplankton RUE and a shift from green algae and flagellate co-dominance to dominance of diatoms. Structural equation modelling further indicated that ammonium reduction led directly to increases in winter–spring phytoplankton RUE and diatom biomass. Repeated fish stocking likely also contributed to the changes in biomass and RUE. Our study provides new insight into the ecological responses to N loading reduction and contributes to the understanding of lake responses to early re-oligotrophication, which was pronounced mainly in the colder seasons in subtropical Lake Taihu, similar to findings in the early phase of re-oligotrophication in numerous temperate lakes.</p>
Data from: genetic resources of macroalgae: development of an efficient method using microsatellite markers in non-model organisms
<p><span>Red and brown seaweeds are species with high ecological and economic importance. Here we report the feasibility of cost-effective molecular marker development in 6 species from different clades. Microsatellites markers of two brown seaweed species <em>Alaria esculenta</em>, <em>Pylaiella littoralis</em>, and of four red seaweed species <em>Calliblepharis jubata</em>, <em>Gracilaria gracilis</em>, <em>Gracilaria dura </em>and <em>Palmaria palmata</em> were identified and characterized using genomic sequences of Double-Digest Restriction site Associated DNA (ddRAD). A total of 64,623,186 reads were generated from two runs of multiplexed Illumina Miseq sequencing for which 30,636 reads containing microsatellites and 15,443 microsatellite loci with primers pairs were found. Five hundred seventy-six primers pairs were selected for amplification trials and levels of polymorphism. From the 338 that gave a positive amplification, 142 primers pairs were polymorphic. For genetic analyses two or three populations per species from 13 different geographic locations were used. A total of 28 usable polymorphic markers for <em>A. esculenta</em>, 18 for <em>P. littoralis</em>, 11 for <em>C. jubata</em>, 14 for <em>G. gracilis</em>, 21 for <em>G. dura </em>and 13 for <em>P. palmata </em>were developed. The overall number of alleles per locus ranged from 2 to 22. These 105 new microsatellite markers will be useful for further studies of population genetics, breeding programs and conservation genetics of these species. Compared with traditional approaches, our study yielded thousands of microsatellite loci in a short tim</span><span>e with affordable costs in six different species. This study based on ddRAD-sequencing for the development of microsatellite markers provides preliminary data u</span><span>sing a few individuals from two distinct populations on the genetic structure and reproduction mode of a non-model species as shown </span>with the detection of clonality for the two red algae, <em>C. jubata </em>and <em>G. dura</em> and the detection of highly genetically divergent populations corresponding probably to different cryptic species under the name of<em> P. littoralis</em>.</p>
Data from: Nitrogen reduction causes shifts in winter and spring phytoplankton composition and resource use efficiency in a large subtropical lake in China
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Asymmetric responses of resource use efficiency to previous-year precipitation in a semi-arid grassland
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Data from: genetic resources of macroalgae: development of an efficient method using microsatellite markers in non-model organisms
Open the record for dataset details and reuse information.
Environmental stoichiometry mediates phytoplankton diversity effects on communities’ resource use efficiency and biomass
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Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
<p>The quality of human life increases with increased usage of the Internet of Things (<i>IoT</i>). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012.</p> <p>Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide promising solutions to deal with compute-intensive applications. The scheduling challenges faced by the Fog service provider are to fulfill user requests with the maximum resource utilization of Fog devices while reducing the monetary cost of Cloud resource usage. In this research, a resource-aware scheduler has been proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications, and minimum bandwidth usage.</p> <p>The proposed scheduler Resource Aware Cost-Efficient Scheduler (RACE) consists of two algorithms: ModuleScheduler: which categorizes the incoming application modules according to their computation and bandwidth requirements. The ModuleScheduler also creates a prioritized list considering the categories of application modules. Compare Module: Finds the suitable Fog device for modules execution in the prioritized list. The proposed scheduler (RACE) schedule the modules at the Fog layer based on their computation requirement and bandwidth requirement until the devices at the Fog layer have enough CPU capacity to accommodate the modules.</p>
Data from: Convergence in resource use efficiency across trees with differing hydraulic strategies in response to ecosystem precipitation manipulation
1. Plants are expected to respond to drought by maximizing the efficiency of the most limiting resource, the water use efficiency (WUE), at the expense of nitrogen and carbon use efficiencies (NUE and CUE). Therefore, plants resource use efficiencies are viewed as indicators of species drought tolerance. 2. We tested these predictions by measuring leaf-level intrinsic WUE (WUEi, the ratio of net assimilation to stomatal conductance), photosynthetic NUE (PNUE, the ratio of daily maximum net assimilation to leaf nitrogen content) and leaf-scale CUE (approached by the ratio of nighttime respiration to daytime net assimilation, Rd/An) in piñon pine and juniper, two tree species that differ in drought tolerance and vulnerability to drought-induced mortality. Variations in resource use efficiency in the two species were measured in response to seasonal drought and in response to an ecosystem-scale precipitation manipulation experiment comprising three precipitation treatments: ambient, irrigation (+30%) and partial rainfall exclusion (-45%). 3. Increasing water limitation, either seasonally or across treatments, resulted in increased WUE and decreased PNUE and CUE in both species. WUE, PNUE and CUE varied more strongly in response to water limitation than across species and converged to the same relationships against precipitation for piñon and juniper. 4. Plasticity in WUE, PNUE and CUE in response to water limitation was associated, in both species, with low carbon acquisition during drought. Our results exhibited a convergence in resource use efficiency across piñon and juniper which contradicts the paradigm that resource use efficiencies are indicators of species drought tolerance and ecological strategy.
Data from: Rehabilitating the cyanobacteria – niche partitioning, resource use efficiency, and phytoplankton community structure during diazotrophic cyanobacterial blooms
1. Blooms of nitrogen-fixing cyanobacteria are recurrent phenomena in marine and freshwater habitats, and their supplying role in aquatic biogeochemical cycles is generally considered vital. The objective of this study is to analyze if an increasing proportion of nitrogen-fixing cyanobacteria affects (i) the composition of the non-diazotrophic component of ambient phytoplankton communities, and (ii) resource use efficiency (RUE; ratio of chl a to total nutrients) – an important ecosystem function. We hypothesize that diazotrophs increase community P use, and decrease N use efficiencies, as new N is brought into the system, relaxing N, and concomitantly aggravating P limitation. We test this by analyzing an extensive dataset from the Baltic Sea (> 3700 quantitative phytoplankton samples), known to harbor conspicuous and recurrent blooms of Nodularia spumigena and Aphanizomenon sp. 2. System-level phosphorus use efficiency (RUEP) was positively related with high proportion of diazotrophic cyanobacteria, suggesting aggravation of phosphorus limitation. However, concomitant decrease of nitrogen use efficiency (RUEN) was not observed. Nodularia spumigena, a dominant diazotroph and a notorious toxin producer, had a significantly stronger relationship with RUEP, compared to the competing non-toxic Aphanizomenon sp., confirming niche differentiation in P acquisition strategies between the major bloom-forming cyanobacterial species in the Baltic Sea. Nodularia occurrences were associated with stronger temperature stratification in more offshore environments, indicating higher reliance on in situ P regeneration. 3. By using constrained and unconstrained ordination, permutational multivariate analysis of variance, and local similarity analysis, we show that diazotrophic cyanobacteria explained no more than a few percent of the ambient phytoplankton community variation. The analyses furthermore yielded rather evenly distributed negative and positive effects on individual co-occurring phytoplankton taxa, with no obvious phylogenetic or functional trait-based patterns. 4. Synthesis. Our study reveals that despite the widely acknowledged noxious impacts of cyanobacterial blooms, the overall effect on phytoplankton community structure is minor. There are no predominantly positive or negative associations with ambient phytoplankton species. Species-specific niche differences in cyanobacterial resource acquisition affect important ecosystem functions, like biomass production per unit limiting resource.
Data from: Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation
Global syntheses of leaf trait scaling relationships report an increase in light interception costs or 'diminishing returns' with increase in leaf area. However, variation in light interception costs across ecological gradients and plant strategies to cope up with these costs are not adequately understood. We analyzed leaf area (A) - leaf dry mass (M), leaf water mass (W) - M and W - A scaling relationships in plants occurring in a high altitude region of western Himalaya across environmental gradients to understand changes in light interception cost and metabolic mass component. M represents light interception cost, whereas, W is considered as a proxy of metabolic mass component for liquid phase being the ultimate source of metabolic activity. Trait values were measured from 9278 leaves belonging to 136 dominant species occurring at different sites, slope aspects, elevations and habitat types. Overall, light interception cost increased with increasing A (scaling exponent (α) <1 in A-M relationship) and metabolic mass component increased disproportionately high with increasing M and A. We found significant differences in scaling exponents of leaf trait relationship between sites, elevations, slope aspects and habitat types, indicating that increase in light interception cost was more evident at higher elevations, southern slopes and open habitats. Further, with increase in light interception cost, metabolic mass component also increased (α>1 in W-M and W-A relationships). The changes in scaling exponents of various leaf trait relationships across ecological gradients indicated that vegetation of different regions have differences in light interception cost and metabolic mass component. Moreover, increasing light interception cost (increase in mechanical and hydraulic tissues) with increasing A and increasing metabolic mass (leaf thickness) with increasing A and M are favored in high altitude vegetation. This could be a key strategy of high altitude plants for efficient resource capture and use in harsh environments.
ScienceDex guides
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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.