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5,805 results for “Data model”
Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation
<p>Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using <em>Myxococcus xanthus</em> as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, <em>csgA</em> and <em>pilC</em>, that cannot aggregate unless mixed with wild type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.</p>
Data associated with the publication titled The impact of resolving sub-kilometer processes on aerosol-cloud interactions in global model simulations
<p>Datasets and scripts that are used in the journal article The impact of resolving sub-kilometer processes on aerosol-cloud interactions in global model simulations</p>
Supplementary material 2 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299
Multimodel inference table
Supplementary material 3 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299
R codes
Data of the ER 2020 Publication: Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis
<p>Related Publication:</p> <p>Härer, Felix, Fill, Hans-Georg (2020): Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis. Accepted for: 39th International Conference on Conceptual Modeling, ER 2020.</p> <p>Contents:</p> <ul> <li>The directory <em>Descriptive Analysis</em> contains all publications of the analysis database used for the descriptive analysis.</li> <li>The directory <em>Bibliometric Analysis</em> contains the NLP and analysis processes for RapidMiner with stopwords and synonyms.</li> </ul>
ENTSO-E Hydropower modelling data (PECD) in CSV format
<p>PECD Hydro modelling</p> <p>This repository contains a more user-friendly version of the <code>Hydro modelling data</code> released by ENTSO-E with their <a href="https://www.entsoe.eu/outlooks/seasonal/">latest Seasonal Outlook</a>.</p> <p>The original URLs:</p> <ul> <li>The zipped file: <a href="https://eepublicdownloads.blob.core.windows.net/public-cdn-container/clean-documents/sdc-documents/seasonal/SOR2020/data/Hydro.zip">https://eepublicdownloads.blob.core.windows.net/public-cdn-container/clean-documents/sdc-documents/seasonal/SOR2020/data/Hydro.zip</a></li> <li>The documentation file (v 1.0): <a href="https://eepublicdownloads.blob.core.windows.net/public-cdn-container/clean-documents/sdc-documents/MAF/2019/Hydropower_Modelling_New_database_and_methodology.pdf">https://eepublicdownloads.blob.core.windows.net/public-cdn-container/clean-documents/sdc-documents/MAF/2019/Hydropower_Modelling_New_database_and_methodology.pdf</a></li> </ul> <p>The original ENTSO-E hydropower dataset integrates the PECD (Pan-European Climate Database) released for the <a href="https://www.entsoe.eu/outlooks/midterm/#download">MAF 2019</a></p> <p>As I did for the <a href="https://zenodo.org/record/3702418">wind & solar data</a>, the datasets released in this repository are <strong>only</strong> a more user- and machine-readable version of the original Excel files. As avid user of ENTSO-E data, with this repository I want to share my data wrangling efforts to make this dataset more accessible.</p> <p><strong>Data description</strong></p> <p>The <a href="https://eepublicdownloads.blob.core.windows.net/public-cdn-container/clean-documents/sdc-documents/seasonal/SOR2020/data/Hydro.zip">zipped file</a> contains 86 Excel files, two different files for each ENTSO-E zone.</p> <p>In this repository you can find 6 CSV files:</p> <ul> <li><code>PECD-hydro-capacities.csv</code>: installed capacities</li> <li><code>PECD-hydro-weekly-inflows.csv</code>: weekly inflows for reservoir and open-loop pumping</li> <li><code>PECD-hydro-daily-ror-generation.csv</code>: daily run-of-river generation</li> <li><code>PECD-hydro-weekly-reservoir-min-max-generation.csv</code>: minimum and maximum weekly reservoir generation</li> <li><code>PECD-hydro-weekly-reservoir-levels.csv</code>: weekly reservoir levels</li> <li><code>PECD-hydro-weekly-reservoir-min-max-uniform-levels.csv</code>: weekly minimum and maximum reservoir levels to use outside the climate years</li> </ul> <p><strong>Capacities</strong></p> <p>The file <code>PECD-hydro-capacities.csv</code> contains: run of river capacity (MW) and storage capacity (GWh), reservoir plants capacity (MW) and storage capacity (GWh), closed-loop pumping/turbining (MW) and storage capacity and open-loop pumping/turbining (MW) and storage capacity. The data is extracted from the Excel files with the name starting with <code>PEMM</code> from the following sections:</p> <ul> <li>sheet <code>Run-of-River and pondage</code>, rows from 5 to 7, columns from 2 to 5</li> <li>sheet <code>Reservoir</code>, rows from 5 to 7, columns from 1 to 3</li> <li>sheet <code>Pump storage - Open Loop</code>, rows from 5 to 7, columns from 1 to 3</li> <li>sheet <code>Pump storage - Closed Loop</code>, rows from 5 to 7, columns from 1 to 3</li> </ul> <p><strong>Inflows</strong></p> <p>The file <code>PECD-hydro-weekly-inflows.csv</code> contains the weekly inflow (GWh) for the climatic years 1982-2017 for reservoir plants and open-loop pumping. The data is extracted from the Excel files with the name starting with <code>PEMM</code> from the following sections:</p> <ul> <li>sheet <code>Reservoir</code>, rows from 13 to 66, columns from 16 to 51</li> <li>sheet <code>Pump storage - Open Loop</code>, rows from 13 to 66, columns from 16 to 51</li> </ul> <p><strong>Daily run-of-river</strong></p> <p>The file <code>PECD-hydro-daily-ror-generation.csv</code> contains the daily run-of-river generation (GWh). The data is extracted from the Excel files with the name starting with <code>PEMM</code> from the following sections:</p> <ul> <li>sheet <code>Run-of-River and pondage</code>, rows from 13 to 378, columns from 15 to 51</li> </ul> <p><strong>Miminum and maximum reservoir generation</strong></p> <p>The file <code>PECD-hydro-weekly-reservoir-min-max-generation.csv</code> contains the minimum and maximum generation (MW, weekly) for reservoir-based plants for the climatic years 1982-2017. The data is extracted from the Excel files with the name starting with <code>PEMM</code> from the following sections:</p> <ul> <li>sheet <code>Reservoir</code>, rows from 13 to 66, columns from 196 to 231</li> <li>sheet <code>Reservoir</code>, rows from 13 to 66, columns from 232 to 267</li> </ul> <p><strong>Reservoir levels</strong></p> <p>The file <code>PECD-hydro-weekly-reservoir-levels.csv</code> contains the minimum, maximum and the exact reservoir levels at beginning of each week (scaled coefficient from 0 to 1) for each climate year. The data is extracted from the Excel files with the name starting with <code>PEMM</code> from the following sections:</p> <ul> <li>sheet <code>Reservoir</code>, rows from 13 to 66, column 340 to 375</li> <li>sheet <code>Reservoir</code>, rows from 13 to 66, column 376 to 411</li> <li>sheet <code>Reservoir</code>, rows from 13 to 66, column 412 to 447</li> </ul> <p><strong>Reservoir levels</strong></p> <p>The file <code>PECD-hydro-weekly-reservoir-min-max-uniform-levels.csv</code> contains the minimum, maximum and the exact reservoir levels at beginning of each week (scaled coefficient from 0 to 1). The number are supposed to be used when climate years cannot be used (e.g. outside the range 1982-2017). The data is extracted from the Excel files with the name starting with <code>PEMM</code> from the following sections:</p> <ul> <li>sheet <code>Reservoir</code>, rows from 14 to 66, column 12</li> <li>sheet <code>Reservoir</code>, rows from 14 to 66, column 13</li> </ul> <p><strong>CHANGELOG</strong></p> <p>[2020/08/14] Added missing inflows for some countries (including Norway)<br> [2020/07/20] The old reservoir levels have been renamed 'uniform' consisting with the PECD source data. Added min, max and exact levels<br> [2020/07/17] Added maximum generation for the reservoir</p>
3D Models from Morales, J. I., et al. (2015). "Measuring Retouch Intensity in Lithic Tools: A New Proposal Using 3D Scan Data." Journal of Archaeological Method and Theory 22(2): 543-558.
<p>This document compiles the complete set of 3D models and the measurements used for the experimental work of the paper:</p> <p>Morales, J. I., et al. (2015). "Measuring Retouch Intensity in Lithic Tools: A New Proposal Using 3D Scan Data." Journal of Archaeological Method and Theory 22(2): 543-558.<br> <br> It includes 3D scans from both unmodified and modified flakes (X & Xb). All the flakes produced in this experiment were produced by freehand hard hammer percussion and no specific flaking method was followed. Diferents types of tertiary evaporitic flint described in Soto, M., et al. (2017). "The chert abundance ratio (CAR): a new parameter for interpreting Palaeolithic raw material procurement." J. Archaeol Anthropol Sci. (https://doi.org/10.1007/s12520-017-0516-3) were used.</p> <p> </p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
SIM4NEXUS target scenario data from IMAGE 3.0 model
<p>Target scenario dataset aiming for improvement in different nexus sectors developed for the H2020 project SIM4NEXUS using the IMAGE 3.0 integrated assessment model framework.</p>
Data from: Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models
The location and modular structure of eukaryotic protein-coding genes in genomic sequences can be automatically predicted by gene annotation algorithms. These predictions are often used for comparative studies on gene structure, gene repertoires, and genome evolution. However, automatic annotation algorithms do not yet correctly identify all genes within a genome, and manual annotation is often necessary to obtain accurate gene models and gene sets. As manual annotation is time-consuming, only a fraction of the gene models in a genome is typically manually annotated, and this fraction often differs between species. To assess the impact of manual annotation efforts on genome-wide analyses of gene structural properties, we compared the structural properties of protein-coding genes in seven diverse insect species sequenced by the i5k initiative. Our results show that the subset of genes chosen for manual annotation by a research community (3.5-7% of gene models) may have structural properties (e.g., lengths and exon counts) that are not necessarily representative for a species' gene set as a whole. Nonetheless, the structural properties of automatically generated gene models are only altered marginally (if at all) through manual annotation. Major correlative trends, for example a negative correlation between genome size and exonic proportion, can be inferred from either the automatically predicted or manually annotated gene models alike. Vice versa, some previously reported trends did not appear in either the automatic or manually annotated gene sets, pointing towards insect-specific gene structural peculiarities. In our analysis of gene structural properties, automatically predicted gene models proved to be sufficiently reliable to recover the same gene-repertoire-wide correlative trends that we found when focusing on manually annotated gene models only. We acknowledge that analyses on the individual gene level clearly benefit from manual curation. However, as genome sequencing and annotation projects often differ in the extent of their manual annotation and curation efforts, our results indicate that comparative studies analyzing gene structural properties in these genomes can nonetheless be justifiable and informative.
Straight Tidal Channel Model data
<p>This repository contains the data analyzed in the paper "Effects of vegetation, sediment supply and sea level rise<br> on the morphodynamic evolution of tidal channels" (submitted to Water Research Resources)</p> <ul> <li>Directory 'Field data' contains the data from real tidal channels (Venice Lagoon, Western Scheldt).</li> <li>Directory 'Hydrodyanmics' contains the data computed by the fully fledged 2D model 2DEF and the simplified 1D model developed by the authors over three test bathymetry.</li> <li>Directory 'Straight_Channel_long_term_configuration' contains the data about the evolution and the final configuration of the channel in all the runs.</li> </ul>
Mobilisation of data to stakeholder communities: Bridging the research-practice gap using a commercial shellfish species model
<p>Knowledge mobilisation is required to "bridge the gap" between research, policy and practice. This activity is dependent on the amount, richness and quality of the data published. To understand the impact of a changing climate on commercial species, stakeholder communities require better knowledge of their past and current situations. The common cockle (<i>Cerastoderma edule</i>) is an excellent model species for this type of analysis, as it is well-studied due to its cultural, commercial and ecological significance in west Europe. Recently, <i>C. edule</i> harvests have decreased, coinciding with frequent mass mortalities, due to factors such as a changing climate and diseases. In this study, macro and micro level marine historical ecology techniques were used to create datasets on topics including: cockle abundance, spawning duration and harvest levels, as well as the ecological factors impacting those cockle populations. These data were correlated with changing climate and the Atlantic Multidecadal Oscillation (AMO) index to assess if they are drivers of cockle abundance and harvesting. The analyses identified the key stakeholder communities involved in cockle research and data acquisition. It highlighted that data collection was sporadic and lacking in cross-national/stakeholder community coordination. A major finding was that local variability in cockle populations is influenced by biotic (parasites) and abiotic (temperature, legislation and harvesting) factors, and at a global scale by climate (AMO Index). This comprehensive study provided an insight into the European cockle fishery but also highlights the need to identify the type of data required, the importance of standardised monitoring, and dissemination efforts, taking into account the knowledge, source, and audience. These factors are key elements that will be highly beneficial not only to the cockle stakeholder communities but to other commercial species.</p>
On the use of real-time mortality data in modelling and analysis during an epidemic outbreak - underlying data
<p>This project contains the primary data set analyzed in the paper "On the use of real-time mortality data in modelling and analysis during an epidemic outbreak ". The data was generated by downloading the <em>D<sup>T</sup></em>-series daily from the Public Health Agency of Sweden between 2020-04-02 and 2020-07-09. </p> <p>This project contains the following files:</p> <ul> <li>FHM_Covid_Download.zip. (Zip-archive of raw downloaded files with Swedish deaths data.)</li> <li>swedish_covid_deaths_data.csv. (Swedish deaths data collated from the raw data files in a .csv format.)</li> <li>swedish_covid_deaths_data.xlsx. (Swedish deaths data collated from the raw data files in a .xlsx format.)</li> </ul>
Depressurization of CO2 in a pipe: High-resolution pressure and temperature data and comparison with model predictions – dataset
<p>This dataset contains data from depressurization of pure CO<sub>2</sub> and nitrogen in a tube from a gaseous and a dense-liquid state. The data are described in the accompanying paper (DOI: <a href="https://doi.org/10.1016/j.energy.2020.118560">10.1016/j.energy.2020.118560</a>).</p> <p>Test number; fluid; pressure (MPa); temperature (deg C):<br> 3; CO2; 4.04; 10.2<br> 4; CO2; 12.54; 21.1<br> 6; CO2; 10.40; 40.0<br> 8; CO2; 12.22; 24.6<br> 11; N2; 5.13; 10.0</p> <p><br> </p>
Data from: Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics
<p>1. Assessing the source-sink status of populations and habitats is of major importance for understanding population dynamics and for the management of natural populations. Sources produce a net surplus of individuals (per capita contribution to the metapopulation >1) and will be the main contributors for self-sustaining populations, whereas sinks produce a deficit (contribution < 1). However, making these types of assessments is generally hindered by the problem of separating mortality from permanent emigration, especially when survival probabilities as well as moved distances are habitat-specific.<br> 2. To address this long-standing issue, we propose a spatial multi-event Integrated Population Model (IPM) that incorporates habitat-specific dispersal distances of individuals. Using information about local movements, this IPM adjusts survival estimates for emigration outside the study area.<br> 3. Analyzing 24 years of data on a farmland passerine (the northern wheatear Oenanthe oenanthe) we assessed habitat-specific contributions, and hence the source-sink status and temporal variation of two key breeding habitats, while accounting for habitat- and sex-specific local dispersal distances of juveniles and adults. We then examined the sensitivity of the source-sink analysis by comparing results with and without accounting for these local movements.<br> 4. Estimates of first-year survival, and consequently habitat-specific contributions, were higher when local movement data were included. The consequences from including movement data were sex specific, with contribution shifting from sink to likely source in one habitat for males, and previously noted habitat differences for females disappearing.<br> 5. Assessing the source-sink status of habitats is extremely challenging. We show that our spatial IPM accounting for local movements can reduce biases in estimates of the contribution by different habitats, and thus reduce the overestimation of the occurrence of sink habitats. This approach allows combining all available data on demographic rates and movements, which will allow better assessment of source-sink dynamics and better informed conservation interventions.</p>
Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships
<p>Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships which has been submitted to the Journal of Advances in Modelling Earth Systems: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002144</p> <p>The experiment id h3hh corresponds to simulations with the ECMWF IFS with a single layer snow model. h3eg corresponds to the experimental 5-layer snow model.</p> <p>The timeseries are made by concatenating hourly data from day2 of forecasts initialised at 00UTC each day between Dec 1st 2013 and 1 June 2014.</p>
Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>*Data* (comets_condBoot_data.zip)</p> <p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p> <p>*Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6)</p> <p>6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results</p> <p>In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res</p> <p> </p>
Data from: Habitat suitability and connectivity modeling reveal priority areas for Indiana bat (Myotis sodalis) conservation in a complex habitat mosaic
Context <p>Conservation for the Indiana bat (<i>Myotis sodalis), </i>a federally endangered species in the United States of America, is typically focused on local maternity sites; however, the species is a regional migrant, interacting with the environment at multiple spatial scales. Hierarchical levels of management may be necessary, but we have limited knowledge of landscape-level ecology, distribution, and connectivity of suitable areas in complex landscapes.</p> Objectives <p>We sought to 1) identify factors influencing <i>M. sodalis </i>maternity colony distribution in a mosaic landscape, 2) map suitable maternity habitat, and 3) quantify connectivity importance of patches.</p> Methods <p>Using 3 decades of occurrence data, we tested <i>a priori</i>,<i> </i>hypothesis-driven<i> </i>habitat suitability models. We mapped suitable areas and quantified connectivity importance of habitat patches with probabilistic habitat availability metrics.</p> Results <p>Factors improving landscape-scale suitability included limited agriculture, more forest cover, forest edge, proximity to medium-sized water bodies, lower elevations, and limited urban development. Areas closer to hibernacula and rivers were suitable. Binary maps showed that thirty percent of the study area was suitable for <i>M. sodalis</i> and 29% was important for connectivity. Most suitable patches were important for intra-patch connectivity and far fewer contributed to inter-patch connectivity.</p> Conclusions <p>While simple models may be effective for small, homogenous landscapes, complex models are needed to explain habitat suitability in large, mixed landscapes. Suitability modeling identified factors that made sites attractive as maternity areas. Connectivity analysis improved our understanding of important areas for bats, identified suitable patches that may be isolated from the habitat network, and prioritized areas to target restoration.</p>
Raw in vitro screening data and R scripts for: A Bayesian method for population-wide cardiotoxicity hazard and risk characterization using an in vitro human model
<p>Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Inter-individual variability in toxicodynamic sensitivity has also been demonstrated <i>in vitro</i>; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 44 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 non-diseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for five phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors (TDVF). Toxicokinetic modeling was used for probabilistic <i>in vitro</i>-to-<i>in vivo </i>extrapolation in order to derive population-wide margins of safety (MOS) for pharmaceuticals and margins of exposure (MOE) for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. TDVF estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally-assumed default of ~3. Population variability-based MOS for pharmaceuticals were correctly predicted to be relatively narrow, between 10-100; however, MOE for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at general population exposures even to sensitive sub populations. This study represents a first of its kind human <i>in vitro</i> model that can be used to characterize toxicodynamic population variability in cardiotoxic risk.</p>
Data from: In silico study of the role of cell growth factors in photosynthesis using a virtual leaf tissue generator coupled to a microscale photosynthesis gas exchange model
Computational tools that allow in silico analysis of the role of cell growth and division on photosynthesis are scarce. We present a freely available tool that combines a virtual leaf tissue generator and a two-dimensional microscale model of gas transport during C3 photosynthesis. A total of 270 mesophyll geometries were generated with varying degree of growth anisotropy, growth extent and extent of schizogenous airspace formation in the palisade mesophyll. The anatomical properties of the virtual leaf tissue and microscopic cross sections of actual leaf tissue of tomato (Solanum lycopersicum L.) were statistically compared. Model equations for transport of CO2 in the liquid phase of the leaf tissue were discretized over the geometries. The virtual leaf tissue generator produced a leaf anatomy of tomato that was statistically similar to real tomato leaf tissue. The response of photosynthesis to intercellular CO2 predicted by a model that used the virtual leaf tissue geometry compared well with measured values. The results indicate that the light-saturated rate of photosynthesis was influenced by interactive effects of extent and directionality of cell growth and degree of airspace formation through the exposed surface of mesophyll per leaf area. The tool could be used further in investigations of improving photosynthesis and gas exchange in relation to cell growth and leaf anatomy.
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.