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652 results for “allocation”
Phenology and Carbon Allocation of Roots at Harvard Forest 2011-2013
The objective of this study is to estimate the phenology and partitioning of C allocated belowground across the growing season at Harvard Forest in two hardwood stands dominated by Quercus rubra and Fraxinus americana, respectively, and one conifer stand dominated by Tsuga canadensis. The phenology of fine root production was characterized by multiple flushes of growth and mortality, especially in the red oak (Q. rubra) stand. Root exudation rate did not have a clear seasonal signal. The deciduous hardwood stands allocated C belowground earlier in the season compared to the conifer-dominated stand. Deciduous stands also allocated a greater proportion of total belowground C flux (TBCF) to root growth compared to the conifer-dominated hemlock (T. canadensis) stand. Of the three stands, red oak partitioned the greatest proportion of TBCF (~50%) to root growth, while hemlock partitioned the least.
Soil nitrogen availability and acidity: effects on aboveground production and belowground carbon allocation in mid- and late-successional mixed temperate forests (2009-2021)
In 2011, an experimental nitrogen x pH manipulation study was initiated in mid- and late-successional mixed temperate forests in central New York, USA to disentangle the often-confounded roles of nitrogen (N) and soil pH in driving various ecosystem processes. This data package contains forest productivity (wood, litterfall, and aboveground net primary production), total belowground carbon flux (TBCF), and leaf litterfall and fine root chemistry (C and N concentration) data collected from all experimental plots. It also includes plot-level, species-weighted estimates of measured and modeled photosynthesis (Anet) for the late-successional stands. Wood production, litterfall production, and litterfall chemistry data were collected between 2009 and 2019. Aboveground net primary production data are reported for a pre-treatment interval (2009-2011) and the interval including years 6-9 of experimental treatment (2016-2019). All other properties were measured between years 9 and 11 of the experiment (2019-2021).
Block summaries of biomass, carbon, nitrogen, and phosphorus allocation among tissue types, species, and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment harvests: 2000 and 2015, Toolik Lake Field Station, Alaska.
A complete accounting of biomass, C, N, and P allocation both among tissue types (leaves, stems, rhizomes, roots) and among species and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment’s untreated control plots and plots that were fertilized annually, harvested after 20 and 35 years, near Toolik Lake Field Station, Alaska. Data are gram per meter squared summarized by block.
POLCAND_ELEC (Allocation of Seats on Electoral Lists: A Dynamic Analysis of Candidate Positioning in Parliamentary Elections from 1991 to 2023)
<p>The aim of the study was to supplement and expand the data contained in the EAST PaC database (Joshua Kjerulf Dubrow: East European Parliamentarian and Candidate Data (EAST PaC), 1985 - 2015 [dane]. Institute of Philosophy and Sociology, Polish Academy of Sciences [producent], Warsaw, 2016. PADS21320. Polish Social Data Archive [dystrybutor], Repozytorium Danych Społecznych [wydawca], 2021. <a href="https://doi.org/10.18150/LSBNLO" target="_blank" rel="noopener">https://doi.org/10.18150/LSBNLO</a>, V1).</p> <p>Currently, an open-access database called EAST PaC (with a data structure similar to panel data, which allows for identifying candidates each time they participate in subsequent elections) contains information on all candidates who have ever run in elections to the Sejm in Poland, from the last elections in the People's Republic of Poland in 1985 to the elections in 2015.</p> <p>This study allowed for the expansion of previous analyses regarding the ways of forming political representation and the associated quality of political elites. This is essential for conducting a dynamic analysis of candidate positioning on electoral lists, which enables tracing the electoral activity of all candidates in elections from 1991 to 2023. It allows for illustrating the occurrence of events over time by tracking candidates' electoral activities. Many previous empirical studies on the course and consequences of parliamentary elections have significant gaps, as they are limited to analyzing the situation of parliamentarians (often focusing on only selected categories). They lack the history of candidacies in elections, even though voters' final decisions are based on the assessment of previous results of candidates and the political entities they represent.These analyses are significant for explaining the dynamics of the political system and assessing the quality of democracy. They also enable empirical verification of hypotheses concerning the periodization of the institutionalization of the electoral system in Poland from 1991 to 2023.</p>
The tpm metabarcoding DNA sequence database for taxonomic allocations using RDP classifier implemented in DADA2.
<p><strong>The </strong><em>tpm</em><strong> metabarcoding DNA sequence database for taxonomic allocations using the Mothur and DADA2 bio-informatic tools</strong></p> <p>A.C.M. Pozzi<sup>1</sup>, R. Bouchali<sup>1</sup>, L. Marjolet<sup>1</sup>, B. Cournoyer<sup>1</sup></p> <p><sup>1 </sup><em>University of Lyon, UMR Ecologie Microbienne Lyon (LEM), CNRS 5557, INRAE 1418, Université Claude Bernard Lyon 1, VetAgro Sup, Research Team “Bacterial Opportunistic Pathogens and Environment” (BPOE), 69280 Marcy L’Etoile, France.</em></p> <p><strong>Corresponding authors: </strong></p> <ul> <li>A.C.M. Pozzi, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 39 47. Fax. (+33) 472 43 12 23. Email: <a href="mailto:adrien.meynier_pozzi@vetagro-sup.fr">adrien.meynier_pozzi@vetagro-sup.fr</a></li> <li>B. Cournoyer, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 56 47. Fax. (+33) 472 43 12 23. Email: and <a href="mailto:benoit.cournoyer@vetagro-sup.fr">benoit.cournoyer@vetagro-sup.fr</a></li> </ul> <p><strong>Keywords:</strong></p> <p>BACtpm, Bacteria, <em>tpm</em>, thiopurine-<em>S</em>-methyltransferase EC:2.1.1.67, Nucleotide sequences, PCR products, Next-Generation-Sequencing, OTHU</p> <p><strong>Description:</strong></p> <ul> <li>The <em>tpm</em> gene codes for the thiopurine-<em>S</em>-methyltransferase (TPMT), an enzyme that can detoxify metalloid-containing oxyanions and xenobiotics (Cournoyer et al., 1998). Bacterial TPMTs radiated apart from human and animal TPMTs, and showed a vertical evolution in line with the 16S rRNA gene molecular phylogeny (Favre‐Bonté et al., 2005).</li> <li>The <em>tpm</em> database, named BACtpm, was designed to apply the <em>tpm</em>-metabarcoding analytical scheme published in Aigle et al. (2021). It includes the full <em>tpm</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 215 nucleotide-long <em>tpm</em> sequences of 840 unique taxa belonging to 139 genera.</li> <li>Nucleotide sequences of <em>tpm</em> (range: 190-233 nucleotides) were either retrieved from public repositories (GenBank) or made available by B. Cournoyer’s research group. Colin et al. (2020) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>tpm</em> sequences.</li> <li>BACtpm v.2.0.1 (June 2021 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>tpm </em>sequences down to the species and strain levels. Data is stored in the csv format enabling future user to reformat it to fit their specific needs.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>We thank the worldwide community of microbiologists who made contributions to public databases in the past decades, and made possible the elaboration of the BACtpm database. We also thank the Field Observatory in Urban Hydrology (OTHU, <a href="http://www.graie.org/othu/">www.graie.org/othu/</a>), Labex IMU (Intelligence des Mondes Urbains), the Greater Lyon Urban Community, the School of Integrated Watershed Sciences H2O'LYON, and the Lyon Urban School for their support in the development of this database. This work was funded by the French national research program for environmental and occupational health of ANSES under the terms of project “Iouqmer” EST 2016/1/120, l'Agence Nationale de la Recherche through ANR-16-CE32-0006, ANR-17-CE04-0010, ANR-17-EURE-0018 and ANR-17-CONV-0004, by the MITI CNRS project named Urbamic, and the French water agency for the Rhône, Mediterranean and Corsica areas through the Desir and DOmic projects. We thank former BPOE lab members who contributed to start and expand the BACtpm database: Céline COLINON, Romain MARTI, Emilie BOURGEOIS, Sébastien RIBUN and Yannick COLIN.</p> <p><strong>References:</strong></p> <p>Aigle, A., Colin, Y., Bouchali, R., Bourgeois, E., Marti, R., Ribun, S., Marjolet, L., Pozzi, A.C.M., Misery, B., Colinon, C., Bernardin-Souibgui, C., Wiest, L., Blaha, D., Galia, W., Cournoyer, B., 2021. Spatio-temporal variations in chemical pollutants found among urban deposits match changes in thiopurine S-methyltransferase-harboring bacteria tracked by the tpm metabarcoding approach. Sci. Total Environ. 767, 145425. https://doi.org/10.1016/j.scitotenv.2021.145425</p> <p>Colin, Y., Bouchali, R., Marjolet, L., Marti, R., Vautrin, F., Voisin, J., Bourgeois, E., Rodriguez-Nava, V., Blaha, D., Winiarski, T., Mermillod-Blondin, F., Cournoyer, B., 2020. Coalescence of bacterial groups originating from urban runoffs and artificial infiltration systems among aquifer microbiomes. Hydrol. Earth Syst. Sci. 24, 4257–4273. https://doi.org/10.5194/hess-24-4257-2020</p> <p>Cournoyer, B., Watanabe, S., Vivian, A., 1998. A tellurite-resistance genetic determinant from phytopathogenic pseudomonads encodes a thiopurine methyltransferase: evidence of a widely-conserved family of methyltransferases1The International Collaboration (IC) accession number of the DNA sequence is L49178.1. Biochim. Biophys. Acta BBA - Gene Struct. Expr. 1397, 161–168. https://doi.org/10.1016/S0167-4781(98)00020-7</p> <p>Favre‐Bonté, S., Ranjard, L., Colinon, C., Prigent‐Combaret, C., Nazaret, S., Cournoyer, B., 2005. Freshwater selenium-methylating bacterial thiopurine methyltransferases: diversity and molecular phylogeny. Environ. Microbiol. 7, 153–164. https://doi.org/10.1111/j.1462-2920.2004.00670.x</p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
Fair emissions allocations under various global conditions
<h1>Introduction</h1> <p>This dataset contains information on how to fairly distribute the mitigation efforts that countries need to undertake to together achieve certain climate goals. There is no single answer to this question, but we explore this topic by looking at various global emissions pathways, and subsequently allocate these emissions to countries using different effort-sharing rules. This data is applied in a preprint of a <a href="https://www.researchsquare.com/article/rs-5023350/v1">scientific article</a> where we explore implications of justice on NDCs and international mitigation finance.</p> <p>The research behind this dataset is still under development and therefore this dataset is not final. Our scientific work is still under revision so the data is subject to potential changes upon peer review of this publication. Nevertheless, because (a version of) this data is already used in the Carbon Budget Explorer and in scientific projects, we feel it should be available and versioned. Hence these releases of a preliminary version.</p> <h1>Carbon Budget Explorer</h1> <p>We also published this work on a website called the <em>Carbon Budget Explorer</em>: an online interactive tool that allows users to navigate through these results, without having to download and plot the data themselves. It is free and publicly available at <a href="https://www.carbonbudgetexplorer.eu">www.carbonbudgetexplorer.eu</a>. Currently, the Carbon Budget Explorer relies on a previous version of this dataset (version 0.1, unpublished, but available upon request). The Explorer will be updated with new data early 2025 (i.e., with the version presented in this data repository).</p> <h1>Data description</h1> <h3>Default (DefaultAllocations.zip and DefaultReductions.zip)</h3> <p>For many users, these are the main datafiles. Per country and region, allocations and reduction targets are shown for two trajectories, which are associated with 1.5 (with slight overshoot: peak temperature 1.6) and 2.0 degree pathways, and default settings across all other dimensions. The exact parameters used in these precooked pathways are shown in Table 1 (see "Dimensions"). The <em>reductions_default_*.csv</em> files show data along the same structure, also using the default pathways, but contain the emission reductions with respect to 2015 rather than absolute allocations.</p> <h3>Global pathways (GlobalPathways.zip)</h3> <p>Allocating emissions to countries starts with determining global emissions pathways. The files in <em>GlobalPathways.zip</em> contain projected global emissions on GHG, CO2 and non-CO2 levels, constrained by various global settings (see below) such as temperature targets and derived CO2 budgets. The pathway shapes are informed by mitigation scenarios from the IPCC AR6 database. The starting values are all harmonized with 2021 historical datapoints. For convenience, the <em>emissionspathways_default.csv</em> datafile provides the pathways with default settings (see Table 1, column 'Default'). The complete dataset can be found in <em>emissionspathways_all.csv</em>.</p> <h3>Emission allocations (Allocations.zip -> allocations_*.nc)</h3> <p>The emissions from the global pathways can be divided among countries according to different allocation rules (see 'Allocation rules' for more information). Files of the format <em>allocations_region.nc </em>indicate allocations according to all allocation rules, parameters and global choices, for a single region. Because of the high number of parameters and dimensions, these files are shared in NetCDF (.nc) format. NetCDF files are commonly used for storing multidimensional scientific data and can be displayed, analyzed and read/written using GIS systems (such as ArcGIS, QGIS), MATLAB funcions (such as <em>nccreate</em>, <em>ncread</em>), R (e.g. using the <em>ncdf4</em> package) and Python (e.g. using the <em>xarray</em> package).</p> <h3>Input data (Inputdata.zip)</h3> <p>Additional input data coming from third parties, such as population and GDP data, is stored in <em>Inputdata.zip</em>. We prepared these input data sources in the exact same format as the rest for convenience of the user, but we would like to emphasize that the appropriate references should be cited. For further information, please check 'Input data sources'.</p> <h3>CO2 budgets</h3> <p>A file has been added in the version 0.3.1, including cumulative CO2 budgets. How they are calculated, is slightly different for each rule (only PC, AP and ECPC are included here), because of the varying nature of these allocation rules. The PC budget is simply the fraction of the remaining carbon budget determined by a country's 2021 population share. The AP budget is computed by adding all positive CO2 allocations according to the AP rule. The ECPC budget is the full-century budget: that is, historical leftover (or debt) plus a country's fair per capita share between 2021-2100. Note that there is not necessarily a one-to-one relation between these budgets and the CO2 part of the allocation files (<em>Allocations.zip</em>). For example, the PC budget uses 2021 population, while the allocation files use year-to-year population numbers (also if they change in the future). We have the ambition to, in next versions, expand this dataset to account for and vary the choices one can make in this regard.</p> <h1>Allocation rules</h1> <p>Below you can find a summarized description of all allocation rules. More detailed information can be found in <a href="https://link.springer.com/article/10.1007/s10584-019-02368-y" target="_blank" rel="noopener noreferrer">Van den Berg et al. (2020)</a>, as well as in a scientific paper (preprint) expected in summer 2024. The rules have a variety of parameters, each included as dimensions in the data. See Table 1, in "Dimensions", for details.</p> <ul> <li>The (immediate) 'Per Capita' method (PC) uses a country's population share in the global population and allocates future emissions accordingly. Naturally, socio-economic conditions affect this method. Therefore, all five SSPs are used in our analysis. </li> <li>'Grandfathering' (GF) is a method that preserves current emission fractions. In other words, all countries reduce their emissions proportional to their current share. Note that this rule is controversial and is commonly not regarded as fair (see <a href="https://www.tandfonline.com/doi/full/10.1080/14693062.2021.1970504">Rajamani et al. 2021</a>). It is include here for reference only.</li> <li>The 'Per Capita Convergence' (PCC) method starts as 'Grandfathering', but converges over time to a 'Per Capita' basis. An additional important parameter here is the year at which this convergence completes.</li> <li>The 'Per Capita via Budget' (PCB_lin) method is a specific implementation of distributing the total CO2 budget on a per capita basis, and then drawing a linear line from current emissions down to net-zero CO2. A median non-CO2 path is added to end up with a total greenhouse gas emissions line. This is similar to, for example, <a href="https://newclimate.org/resources/publications/what-is-a-fair-emissions-budget-for-the-netherlands">Fekete et al. (2022)</a>.</li> <li>The 'Ability to Pay' (AP) method allocates emissions inversely related to the GDP per capita of countries. Also this method is dependent on the socio-economic scenario.</li> <li>The 'Equal Cumulative Per Capita' (ECPC) method builds on the per-capita convergence method, also accounts for historical responsibility: throughout the convergence period, countries resolve historical 'debt' or 'leftover' from what countries would have emitted if it had emissions according to a per capita share in the past. <em>Note</em>: this method has been significantly revised in version 0.4. In earlier versions, resolving of historical responsibility was only achieved by 2100, postponing most debt.</li> <li>The 'Greenhouse Development Rights' (GDR) method is, in the short run, based on a <a href="https://calculator.climateequityreference.org/" target="_blank" rel="noopener noreferrer">Responsibility-Capability Index</a>, and in the long run based on GDP per capita (similar to 'Ability to Pay').</li> </ul> <h1>Dimensions</h1> <p><em>Table 1 - Data dimensions</em></p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Range</strong></td> <td><strong>Default</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>General</strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>Time</td> <td>Year</td> <td> <p>Past: 1850-2021</p> <p>Future: 2021-2100 (yearly or 5-year increments)</p> </td> <td>All</td> <td>The historic data reported here ends in 2021, and we start our analysis in 2021. Intentionally, to be able to exactly match historic and future data. The year 2021 is chosen because of limited availability of more recent data sources.</td> </tr> <tr> <td>Region</td> <td>ISO3 code</td> <td> <p>Country-level (ISO3)</p> <p>Country groups (e.g., G20 and Umbrella)</p> <p>World ('EARTH')</p> </td> <td>All</td> <td> </td> </tr> <tr> <td><strong>Global</strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> <td><strong> </strong></td> </tr> <tr> <td>Temperature</td> <td>Degrees temperature rise with respect to pre-industrial times</td> <td> <p>1.5 - 2.0 degrees</p> </td> <td>1.6 and 2.0</td> <td>Peak temperature without overshoot</td> </tr> <tr> <td>Climate sensitivity ('Risk' in the data)</td> <td>Risk of exceeding a certain climate target, based on climate sensitivity percentiles.</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td> <p>50% (for 1.6 degrees) and 33% (for 2.0 degrees)</p> </td> <td> <p>This governs the uncertainty in climate sensitivity. Because there is still uncertainty about the exact numerical response of temperature to CO2, we have to include this. Low-risk (e.g., 0.17) indicates that we assume a high climate sensitivity: for a given amount of greenhouse gas emissions, temperature rises higher. This means that carbon budgets at a given temperature level have to be lower. Vice-versa for high-risk (e.g., 0.83).</p> </td> </tr> <tr> <td>NegEmis</td> <td>Quantiles of 2100 GHG emissions among AR6 scenarios with a similar temperature target</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td> <p>50%</p> </td> <td> <p>Even though negative emissions (predominantly in the second-half of the century) are not very relevant for achieving a certain peak temperature, they do alter the second half of global emissions pathways.</p> </td> </tr> <tr> <td>NonCO2red</td> <td>Quantiles of non-CO2 reductions in 2040 with respect to 2020 among AR6 scenarios with a similar temperature target</td> <td> <p>17%, 33%, 50%, 67%, 83%</p> </td> <td>50%</td> <td>Non-CO2 reduction varies greatly among mitigation scenarios, but at the same time has a large effect on the remaining carbon budget. Hence, we vary this factor.</td> </tr> <tr> <td>Timing</td> <td>-</td> <td> <p>Immediate or Delayed</p> </td> <td>Immediate</td> <td>The timing of mitigation action up to 2030. Either this starts immediately (2020) or only after 2030. This factor distinguishes mitigation scenarios from which the functional form of the global emissions pathways are constructed.</td> </tr> <tr> <td><strong>Parameters in allocation rules</strong></td> <td> </td> <td> <p> </p> </td> <td> </td> <td> </td> </tr> <tr> <td>Scenario</td> <td>SSP</td> <td> <p>SSP1-5</p> </td> <td>SSP2</td> <td>Shared-Socioeconomic pathway, defining population and GDP data based on a scenario of how to perceive the future world.</td> </tr> <tr> <td>Convergence_year</td> <td>Year</td> <td> <p>2040, 2050, 2080, 2100</p> </td> <td>2050</td> <td>Year of convergence for the per capita convergence and equal-cumulative per capita rules.</td> </tr> <tr> <td>Discount_factor</td> <td>% per year</td> <td> <p>0%, 1.6%, 2%, 2.8%</p> </td> <td>0%</td> <td>Discount factor of historical emissions, counting from the startyear 2021.</td> </tr> <tr> <td>Historical_startyear</td> <td>Year</td> <td> <p>1850, 1950, 1990</p> </td> <td>1990</td> <td>Year from which and on historical emissions are accounted for in the computation of the responsibility of countries.</td> </tr> <tr> <td>Capability_threshold</td> <td>-</td> <td> <p>No, PrTh, Th</p> </td> <td>Th</td> <td>Implicates whether an additional development threshold should be implemented for the computation of the capability of a country to contribute to mitigation. This is used in the calculations of the Greenhouse Development Rights rule. Entries are (1) no development threshold (No), (2) a threshold of \$7500 (Th) or (3) the \$7500 threshold plus additional progressivity factors. For more information, see <a href="https://joss.theoj.org/papers/10.21105/joss.01273">Holz et al. (2019)</a>.</td> </tr> <tr> <td>RCI_weight</td> <td>-</td> <td> <p>Cap, Half, Resp</p> </td> <td>Half</td> <td>Distinguishes how the Responsibility-Capability Index in the Greenhouse Development Rights rule should weight capability (fully = Cap) or responsibility (fully = Resp). 'Half' indicates that both factors should weigh equally.</td> </tr> </tbody> </table> <h1>Input data sources</h1> <p>For most important data sources, aggregated regions (e.g., G20 and the Umbrella group) are not reported in the original data sources below. We did that aggregation ourselves.</p> <ul> <li>Historic population: UN population data</li> <li>Future population: <a href="https://data.ece.iiasa.ac.at/ssp/#/login">SSP database</a></li> <li>Future GDP: <a href="https://data.ece.iiasa.ac.at/ssp/#/login">SSP database</a></li> <li>Historical emissions: <a href="https://www.nature.com/articles/s41597-023-02041-1">Jones et al. (2023)</a></li> <li>Emissions pathways (shapes): <a href="../records/7197970">Byers, E. et al. AR6 Scenarios Database. (2022)</a></li> <li>NDC data: <a href="https://themasites.pbl.nl/o/climate-ndc-policies-tool/">PBL NDC tool</a></li> <li>Carbon budgets: <a href="https://essd.copernicus.org/articles/15/2295/2023/">Forster et al. (2023)</a></li> <li>Impact of non-CO2 on carbon budgets: <a href="https://www.nature.com/articles/s43247-023-01168-8">Rogelj et al. (2024)</a></li> </ul> <h1>Changelog</h1> <ul> <li>Version 0.4.2: <ul> <li>Fixed export error that resulted in incomplete PCB_lin data.</li> </ul> </li> <li>Version 0.4.1: <ul> <li>Fixed export error that mixed up the columns in DefaultReductions and DefaultAllocation files.</li> </ul> </li> <li>Version 0.4: <ul> <li>Equal-cumulative per capita is significantly revised in terms of temporal allocation. This has large consequences for short-term allocations in most countries, depending on the convergence year. See description above under 'Allocation rules'.</li> <li>Data is now also available for different analysis starting years, gases and including or excluding LULUCF. This is upon request because this would make the repository too large.</li> <li>In the same spirit, a selection has been made on what to include in these datafiles for completeness and clarity, and what to omit to limit file size and computation problems. If you need any specific parameter combination that you cannot find here, feel free to contact us.</li> <li>Improved data on historical population data and baseline emissions</li> <li>Added units in CSV datafiles</li> </ul> </li> <li>Version 0.3.1: <ul> <li>Added CO2 budgets for additional combinations of global targets (no changes in allocation values)</li> </ul> </li> <li>Version 0.3: <ul> <li>Fixed small error in regional aggregation</li> <li>Cumulative CO2 budgets for PC, AP and ECPC are added in a new file</li> <li>Updated global baseline emissions, which affects AP and ECPC</li> </ul> </li> <li>Version 0.2: <ul> <li>Significant update on LULUCF emissions data and historical emissions data by changing to a more up-to-date data source</li> <li>NDC data update (now from the PBL NDC tool)</li> <li>Added the per-capita via budget rule</li> <li>All the above affect emissions allocations, which are therefore also updated</li> </ul> </li> <li>Version 0.1: <ul> <li>First version of the data</li> <li>Published on the Carbon Budget Explorer</li> </ul> </li> </ul> <h1>Contact</h1> <p>We are very open to suggestions of all kinds. Feel free to contact Mark Dekker at <a href="mailto:mark.dekker@pbl.nl?subject=Effort%20Sharing%20Data">this email address </a>or at the contact form on <a href="https://www.pbl.nl/en/about-pbl/employees/mark-dekker">this website</a>.</p>
Disentangling the effects of jasmonate and tissue loss on the sex allocation of an annual plant
<p>In this study, we explored norms of reaction in sex expression and sex allocation to herbivory in an experiment designed to uncouple its direct (through tissue loss) and indirect effects (due to defensive jasmonate signalling) in hermaphroditic XX females of the wind-pollinated Mercurialis annua. To uncouple the direct and indirect effects of herbivory on the sex expression and to test the role of jasmonate on conditional sex allocation, we conducted a two-factorial experiment manipulating tissue loss (25% chronic defoliation) and plant anti-herbivore defences via the jasmonate pathway (external application of jasmonate), and measured sexual expression in plants with both a male and a female function. The herbivory treatment applied were:</p> <p>For the control treatment (C), leaves were sprayed with a sham solution containing only water and polysorbate until all leaves were wet (see Supplementary Materials for detailed solution formulae). The herbivory treatment (H) consisted of cutting off half of every second leaf on the plant with scissors and spraying plants with a sham solution until all leaves were wet (defoliation resulted in a 25% reduction of total leaf area over the course of the whole plant’s lifetime). In the jasmonate treatment (JA) plants were sprayed with a solution of methyl-jasmonate and polysorbate until all leaves were wet (polysorbate 20 was used to fix the methyl-jasmonate on the sprayed leaves). Finally, the jasmonate and herbivory treatment (JAH) consisted of cutting off half of every other leaf on the plant with scissors and spraying plants with the methyl-jasmonate solution until all leaves were wet. These treatments were applied repeatedly as plants continued to grow, i.e., they represent chronic stress or manipulation. The first round of treatment was applied one week after repotting the plants (25th of November 2019) and then every two weeks over the next 12 weeks (the last treatment was applied on the 2<sup>nd</sup> of February 2020). On the first round of treatment, when most plants had fewer than six leaves each, we cut off only half a leaf (~10% of the leaf area removed) for plants under the herbivory treatments to avoid seedlings death.</p> <p>Plant sampling consisted of cutting all above-ground plant material of 34 plants per enclosure (<em>N</em> = 272) and recording total height. Plants were then cut in half, lengthwise, creating two distinct segments: top and bottom. The top segment was carefully examined and we counted the number of fruits (immature and mature) and harvested all male flowers using tweezers. Male flowers were stored in paper envelopes, dried and weighed. After phenotyping, plant segments were dried and weighed to obtain plant dry biomass (top + bottom). To estimate seed production, the seeds were isolated from the dried plant materials, stored in paper envelopes and weighed. All materials were dried in an oven at 50°C for at least 14 days and weighed using a digital scale.</p> <p>Variables names and meaning:</p> <p>PlantID: Individual identifier for each plant<br> nb_seeds_estimate.TOP: Number of seeds form the top section of the plant <br> Biomass.BOTTOM: Dry biomass of the bottom plant section (grams) <br> Total_biomass: Dry biomass of the whole aboveground plant materials, except for the male flowers <br> Biomass.TOP: Dry biomass of the bottom plant section (grams) <br> seed_mass_total: Dry biomass of the seeds of the whole plant (top+bottom sections) (grams)<br> seed_mass.BOTTOM: Dry biomass of the seeds from the bottom section (grams)<br> seed_nb_total: Number of seeds from the whole plant (top+bottom sections) <br> Lenght_section.TOP: Length of the top section (cm) <br> Fruit_number.TOP: Number of fruits present on the top sectioon at the time of harvest<br> nb_seeds_estimate.BOTTOM: Number of seeds from the bottom section<br> Height: Plant height (top+bottom sections) (cm) at the time of harvest<br> Fruit_number.BOTTOM: Number of fruits present on the bottom section at the time of harvest <br> DPT: Days-post-treatment = the period elapsed between the last treatment application and the plant sampling date. For logistical reasons, our sampling was spread over 14 days by a team of six assistants.<br> Lenght_section.BOTTOM: Length of the bottom section (cm)<br> Treatment: Herbivory treatments: C=Control; H= 25% chronic tissue loss, JA=exogenous jasmonate application; JAH=tissue loss + jasmonate.<br> Box: Enclosure in which plants were kept. This was a blocking factor with 2 boxes per treatment, each one with 30-32 plants. <br> Date: sampling date <br> seed_mass.TOP: Dry biomass of the seeds on the bottom plant sections (grams)<br> Observer: Identifier for each of the six researchers who sampled plants. We recorder observer identity and included it in our statistical analyses to account for possible biases among assistants.<br> male_fl_mass.TOP: Dry biomass of the male flowers sampled from the top plant section (grams). <br> nb_fl_estimate.TOP: Number of male flowers present on the top plant section at the time of harvest <br> male_fl_mass.BOTTOM: Dry biomass of the male flowers sampled from the bottom plant section (grams). <br> nb_fl_estimate.BOTTOM: Number of male flowers present on the bottom plant section at the time of harvest </p> <p> </p>
Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model
<p>This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.</p>
Environment's Share in Total Government Budget Allocations for R&D
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget.</p> <p>This dataset calculates the share of the Environment objective compared to the total allocations.</p> <p><br> The source (raw) dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Government Budget Allocations for R&D in Environment
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget. This dataset uses the Environment objective.<br> <br> The missing data are approximated, forecasted and backcasted by country. In our version of the dataset, 40% more countries, and a 23% larger dataset can be used for supervised and unsupervised learning models, such as machine learning, that require complete datasets compared to the source dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Supporting Dataset for the Analysis on TSO-DSOs Cooperation and Stable Cost Allocation for the Joint Procurement of Flexibility (Network and Bid List)
<p>The data provides supporting material for the two case studies in Chapter 5 of CoordiNet D6.2 (the deliverable is available at <a href="https://coordinet-project.eu/publications/deliverables">https://coordinet-project.eu/publications/deliverables</a>) and the two case studies in paper on TSO-DSO cooperation (available at <a href="https://arxiv.org/abs/2111.12830">https://arxiv.org/abs/2111.12830</a>).</p> <p>The dataset is cooresponding to two case studies. In the first case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). The interface flow limit is TPmax. In the second case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three Matpower systems 18-bus distribution networks, who are named as DN_1, DN_2, DN_3. </p> <p>All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line. The interconnected system is fully represented in "Network_XXX.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_XXX);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected.. The generated orderbook is presented in "OrderbookTN_XXX.xlsx" (transmission system) and "OrderbookDN_XXX.xlsx" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_XXX) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Data from: Radial stem growth of the clonal shrub Alnus alnobetula at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring Pinus cembra
<p><strong>Data are documented in the following article:</strong></p> <p>Oberhuber W., G Wieser, F. Bernich, A. Gruber (2022) Radial stem growth of the clonal shrub <em>Alnus alnobetula</em> at treeline is constrained by summer temperature and winter desiccation and differs in carbon allocation strategy compared to co-occurring <em>Pinus cembra</em>. Forests 2022, 13, 440. doi: 10.3390/f13030440.</p> <p> </p> <p><strong>Summary:</strong></p> <p>Global change is affecting species areal distribution in many regions. A better understanding of how land-use change and climate warming affects shrub growth is essential for improved predictions of forest dynamics at the alpine treeline. Evaluation of radial stem growth of the clonal shrub <em>Alnus alnobetula</em> (= <em>Alnus viridis</em>) and the co-occurring tree species Swiss stone pine (<em>Pinus cembra</em>) within an alpine treeline ecotone revealed that mean ring width of nitrogen fixing <em>A. alnobetula</em> was about four times lower compared to <em>P. cembra</em>. Our findings are based on ring width data from <em>A. alnobetula</em> and <em>P. cembra</em> stems sampled at the alpine treeline ecotone on Mt. Patscherkofel (47°12’N, 11°27’E, Central European Alps, Austria, elevation range 2050 to 2190 m asl). Ring width time series include 86 radii from 51 stems of <em>A. alnobetula</em> (stems had mean age of 18±7 yrs) and 24 radii from 16 stems of <em>P. cembra </em>(18±4 yrs). We explain our findings by different carbon allocation strategies, i.e., preference of “vertical” stem growth in late successional <em>P. cembra</em> vs. favoring “horizontal” spread in the pioneer shrub<em> A. alnobetula.</em> By favouring clonal propagation over individual stem growth <em>A. alnobetula</em> is able to quickly spread at the alpine treeline ecotone.</p>
The allocation of Chinese and Indian development finance in Nepal and its influence on local election results
<p>This realease contains the used datasets and calculations for my Bachelorthesis about Indian and Chinese allocation of Overall Development Assistance in Nepal.</p>
Data and code for: Insect herbivores drive sex allocation in angiosperm flowers
<p><strong>Code and Data for the paper:</strong></p> <p>Insect herbivores drive sex allocation in angiosperm flowers</p> <p><em>Carlos Roberto Fonseca, Martin M. Gossner, Johannes Kollmann, Martin Brändle, Gustavo Brant Paterno</em></p> <p> </p> <p>Content of the repository</p> <ol> <li> <p><strong>Data</strong>: the folder <code>data</code> contains all data required to reproduce analyses, figures and tables.</p> </li> <li> <p><strong>Outputs</strong>: the folder <code>output</code> contains the figures, tables and temporary files generated.</p> </li> <li> <p><strong>Code</strong>: the folder <code>scripts</code> contains all scripts (.R) that generated results, figures and tables used in the manuscript and in the supporting information.</p> </li> <li> <p><strong>Supplementary information</strong>: the folder <code>doc</code> contains the supplementary information associated to the paper.</p> </li> </ol>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Global variation in the fraction of leaf nitrogen allocated to photosynthesis
<p>ReadMe</p> <p>1. The datasets were produced based on the method described in Luo X. et al. Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nature Communications. doi: 10.1038/s41467-021-25163-9.</p> <p>2. Vcmax25_RF and fLNR_RF are the key output. Vcmax25_RF was estimated using random forest trained by ground observations, remote sensing leaf chlorophyll content and some ancillary environment variables. fLNR_RF was further calculated from Vcmax25_RF.</p> <p>3. Vcmax25_un and fLNR_un are the uncertainties of Vcmax25_RF and fLNR_RF. </p> <p>4. Note there are several gridded leaf nitrogen content maps (LNC; area-based) available for our derivation of fLNR from Vcmax25. In our study, we mainly use EB17, but also provide the results based on AMM18 and CB20 (see reference).</p> <p>5. Other Vcmax25 and fLNR datasets are provided for comparison. They are all driven by CRU TS4.01 climate data, soil grids soil data and EB17 leaf nitrogen/phosphorus datasets.</p> <p>If you have any questions about the dataset, please contact Xiangzhong (Remi) Luo at xzluo.remi@nus.edu.sg</p>
Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species at the Coweeta Hydrologic Laboratory in 1997
Allometric equations were developed for mature trees of 10 deciduous species at the Coweeta Hydrologic Laboratory in western North Carolina, U.S.A. These equations included the following dependent variables: stem wood mass, stem bark mass, branch mass, total wood mass, foliage mass, total biomass, foliage area, stem surface area, sapwood volume, and total tree volume. High correlation coefficients (R2) were observed for all variables versus stem diameter, with the highest being for total tree biomass, which ranged from 0.981 for Oxdendrum arboreum to 0.999 for Quercus coccinea. Foliage area had the lowest R2 values, ranging from 0.555 for Quercus alba to 0.962 for Betula lenta. When all species were combined, correlation coefficients ranged from 0.822 for foliage area to 0.986 for total wood mass, total tree biomass, and total tree volume. Species with ring versus diffuse/semiring porous wood anatomy exhibited higher leaf area with a given cross-sectional sapwood area as well as lower total sapwood volume. Liriodendron tulipifera contained one of the highest foliar nitrogen concentrations and had consistently low branch, bark, sapwood, and heartwood nitrogen contents. For a tree diameter of 50 cm, Carya spp. exhibited the highest total nitrogen content whereas Liriodendron tulipifera exhibited the lowest.
Drought increases microbial allocation to stress tolerance but with few tradeoffs among community-level traits
Climate change will increase soil drying, altering microbial communities via increasing water stress and decreasing resource availability. The responses of these microbial communities to changing environments is likely governed by physiological tradeoffs between high yield, resource acquisition, and stress tolerance (Y-A-S framework). We leveraged a unique field experiment that manipulates both drought and carbon availability across two years and three land uses, and we used both metagenomic and bioassay indicators of the three microbial community traits to test the following hypotheses: 1. Drought increases microbial allocation to stress tolerance functions, at the expense of growth and resource acquisition. 2. Because microbes are resource-limited under drought, increased carbon will enable greater expression of stress tolerance. 3. All three key life history traits described in the YAS framework will trade off, especially when resources are limited. Drought did increase microbial physiological investment in stress tolerance (measured via trehalose production), but we saw few other changes in microbial communities under drought. Carbon addition increased resource acquisition (measured via enzyme activity and resource acquisition gene abundance) and stress tolerance (trehalose assay), but did so in both drought and average rainfall environments. We found no evidence of trait tradeoffs, as we found no significant negative correlations between traits (measured via bioassay and metagenomics). In summary, we found C addition, and to a lesser extent, drought, both altered microbial community function and functional genes. However, resources did not alter drought response in a way that was consistent with theory of life history tradeoffs.
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.