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3,202 results for “Maintenance”
Maintenance of phenotypic plasticity is linked to oxidative stress in spadefoot toad larvae
<p class="MsoNormal">Phenotypic plasticity allows organisms to improve the match between their phenotype and heterogeneous environments. Theoretical models have argued that costs of maintaining the sensory and response machinery necessary for adaptive phenotypic plasticity are important determinants to the evolution of plasticity. Despite recurrent arguments invoking putative metabolic costs associated with maintenance of cellular machinery, no studies have yet attempted to quantify it from a molecular standpoint. Here we experimentally examine physiological differences across genotypes (sibships) of spadefoot toad larvae with different degrees of plasticity in response to predator cues. We observed marked differences across sibships in developmental, growth and morphological responses to predators, and tested whether increased plasticity was associated with oxidative stress or immune suppression. We observed that more plastic sibships experienced higher antioxidant enzymatic activity when reared in the absence of predator cues, i.e. not expressing their plastic responses. The degree of plasticity was also associated with higher lipid peroxidation and slightly greater granulocyte-to-lymphocyte ratio. Higher antioxidant activity in highly plastic sibships suggests that maintenance of phenotypic plasticity may be linked to energy demanding metabolic processes. Our findings suggest that having the potential to produce plastic responses may incur oxidative and immunological costs. In the long term, such maintenance costs may erode individual fitness and even constrain the evolution of plasticity. To our knowledge, this is the first empirical evidence indicating the existence of a physiological cost to the maintenance of phenotypic plasticity.</p>
GPR56/ADGRG1 regulates development and maintenance of peripheral myelin
<p>These files are the raw data files for RNAseq experiments reported in "GPR56/ADGRG1 regulates development and maintenance of peripheral myelin" by Ackerman et al., Journal of Experimental Medicine, 2018.</p>
Realistic and idealized model output of surface currents and tracers used in frontal maintenance analysis.
<h1>Realistic model</h1> <p><em>./surface_child.zarr</em> directory is a zarr dataset containing a realistic model model based on the Regional Ocean Modeling System \citep[ROMS][]{haidvogel.ea:08} configured with a high-resolution ($\sim$300~m), two-way nested child domain within a coarser ($\sim$1.5~km in the region of nesting) model of the Texas-Louisiana continental shelf, described by \citet{schlichting.ea:23}. The coarser parent model includes the entire coastlines of Louisiana and Texas, and has been run over multiple years of simulated time to examine, for example, interannual variability in harmful algal blooms \citep{thyng.ea:13} along the Texas coast, and the extent of the Mississippi-Atchafalaya plume \citep{zhang.ea:12}. Both models have 30 vertical layers, with resolution focused near the surface. The mean water depth in the nested region is about 50~m. </p> <h1>Idealized model</h1> <p><em>./reduced_channel_files</em> contain six resolution cases spanning 200 m to 10 km horizontal resolution. The idealized ROMS model domain for all of them is 100~km by 300~km, with no-slip walls along the short (north/south) boundaries, and re-entrant along the long (east/west) boundaries. Horizontal resolution is varied from 200~m to 10~km. The initial condition is designed to roughly mirror conditions in the Mississippi-Atchafalaya river plume in the northern Gulf of Mexico, which is a location of strong buoyancy forcing, energetic eddy activity, and intense frontal processes \citep[see][]{hetland:17}. The channel has a constant depth of 50~m, with a constant rotation throughout the domain, $f=10^{-4}$~s$^{-1}$. Initial tracer distributions of temperature, $T$, and salinity, $S$, are defined by constant values specifying the vertical and horizontal density gradients, $N^2 = -g \rho_z \rho_0^{-1} = 10^{-4}~\mathrm{s}^{-2}$ and $M^2 = g \rho_y \rho_0^{-1} = 10^{-6}~\mathrm{s}^{-2}$:<br>\begin{align}<br> T &= T_0 + \frac{N^2}{g \rho_0 \alpha} z\\<br> S &= S_0 + \frac{M^2}{g \rho_0 \beta} y \quad ,<br>\end{align}<br>where $\alpha$=0.17~$^\circ$C$^{-1}$ and $\beta$=0.76~g$^{-1}$~kg are the linear thermal and haline expansion coefficients in a linearized equation of state, <br>\begin{equation}<br> \rho = \rho_0 (1 - \alpha T + \beta S) \quad ,<br>\end{equation}<br>where $\rho_0$ is a reference density.<br>A small perturbation to the initial $u$ velocity (in the periodic direction) with a period of 100~km in both the $x$- and $y$-directions and an amplitude of 0.01~m~s$^{-1}$ is applied to supply perturbations on which instabilities can form and grow. The simulations include a weak near inertial (0.9$f$) wind stress in the $x$-direction,<br>\begin{equation}<br> \label{eqn:roms_wind}<br> \tau^x = \tau^x_0 \mathrm{sin}(0.9 f\,t) \quad ,<br>\end{equation}<br>to mimic the diurnal winds observed in the northern Gulf of Mexico.</p>
Impact of Methodological Choices on the Analysis of Code Metrics and Maintenance
<p>The repo-data folder contains 53 <code>.json</code> files, each corresponding to one of the 53 Java open-source projects. Each file contains various metrics for methods in the project.</p> <p> </p> <pre><code> { "hawtio-3976.json": { "Age": 794, "sloc": [11,11,11], "slocAsItIs": [11,14,14], "slocNoCommentPretty": [11,11,11], "diffSizes": [0,7,0 ], "bodychanges": [0,1,0], "newAdditions": [0,5,0], "isGetter": [false,false,false], "isSetter": [false,false,false], "changeDates": [0,3,794], "isEssentialChange": [false,true,false], "isBuggy": [false,false,false], "changeTypes": ["Yintroduced","Ybodychange","Yfilerename"], "filename": "hawtio-3976.json", "authors": ["X","Y","Z"], "editDistance": [0, 68, 0], "repo": "hawtio" }, "method_id": {...}, "method_id": {...} } </code></pre> <p> </p> <p>The above method with id <code>hawtio-3976.json</code> has total 3 revisions which is why the array of values for a particular metric (e.g., sloc: <code>[11,11,11]</code>) are of length 3. <code>Index 0</code> of the array represents the introduction value of a particular metric for the above method.</p> <div> <h3>Description of the metrics</h3> </div> <ul> <li><code>Age</code>: Age of the method in days</li> <li><code>sloc</code>: Source line of code of a method without comment and blank lines</li> <li><code>slocAsItIs</code>: Source line of code of a method with comment and blank lines</li> <li><code>slocNoCommentPretty</code>: Source line of code pretty printed without comment and blank lines</li> <li><code>diffSizes</code>: Total number of lines added + removed in git <code>diff</code></li> <li><code>bodychanges</code>: Contains value 0 or 1; where 1 implies occurrence of body change</li> <li><code>newAdditions</code>: Total number of lines added in git <code>diff</code></li> <li><code>isGetter</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates it is a <code>get</code> method</li> <li><code>isSetter</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates it is a <code>set</code> method</li> <li><code>changeDates</code>: Contains the date difference in days from when the method was introduced. Index <code>0</code> is always 0 which indicates the introduction date</li> <li><code>isEssentialChange</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates it is an essential change. Essential change includes: <code>Ybodychange</code>, <code>Ymodifierchange</code>, <code>Yexceptionschange</code>, <code>Yrename</code>, <code>Yparameterchange</code>, <code>Yreturntypechange</code> and <code>Yparametermetachange</code> detected by <code>CodeShovel</code></li> <li><code>isBuggy</code>: Contains <code>true</code> or <code>false</code>; where <code>true</code> indicates the method bug was fixed at a particular revision</li> <li><code>changeTypes</code>: All transformations applied to the method at each revision. The full list of transformation that is detected by <code>CodeShovel</code> are: <code>Ybodychange</code>, <code>Ymodifierchange</code>, <code>Yexceptionschange</code>, <code>Yrename</code>, <code>Yparameterchange</code>, <code>Yreturntypechange</code>, <code>Yparametermetachange</code>, <code>Yannotationchange</code>, <code>Ydocchange</code>, <code>Yformatchange</code>, <code>Yfilerename</code> and <code>Ymovefromfile</code></li> <li><code>filename</code>: It is the method <code>id<br></code> <p><strong>bugData</strong> folder contains 53 .json files with bug information, each belonging to one of the 53 Java open-source projects. The sample JSON schema of a file is given below:</p> <pre><code>{ "hawtio-3976.json":{ "exactBug0Match": [false, false, false], "exactBug1Match": [false, false, false], "exactBug2Match": [false, false, false], "exactBug3Match": [false, false, false], "regExBug0": [false, false, false], "regExBug1": [false, false, false], "regExBug2": [false, false, false], "regExBug3": [false, false, false] }, "method_id": {...}, "method_id": {...}, }</code></pre> <p>The above method can be mapped to its metrics dataset using the method_id. For e.g., the above method with id hawtio-3976.json in bugData/hawtio.jsonthat has 3 revision can be found in the metric dataset using the same id hawtio-3976.json in the file repo-data/hawtio.json.<br>Description of bug dataset</p> <p>Each key in the above example contains value true or false indicating if a method was buggy or not at each revision. The "hawtio-3976.json method has 3 revisions (including method's introduction) which is why the array length is 3. The keys in the above json output represent bug-fix classification based on buggy keywords adopted from prior work.</p> <p>We identified bug-fix commit using two approaches:</p> <p> Exact case insensitive match of buggy keywords from the commit message (keys prefix wih exact represent this)<br> Partial case insensitive substring match (using regular expression) excluding words that ends with fix or bug. (keys prefix with regEx represent this)</p> <p> Bug0: This is the approach that we have used for classifying bug-fix commit. Buggy keyword list: <strong>["error", "bug", "fixes", "fixing", "fix", "fixed", "mistake", "incorrect", "fault", "defect", "flaw"]</strong><br> Bug1: Same keyword list as exactBug0Match with the addition of keyword issues<br> Bug2: Buggy keyword list from prior work: <strong>["bug", "fix", "error", "issue", "crash", "problem", "fail", "defect", "patch"]</strong><br> Bug3: Buggy keyword list from prior work: <strong>["error", "bug", "fix", "issue", "mistake", "incorrect", "fault", "defect", "flaw", "type"]</strong></p> </li> </ul>
Empirical Data from a Longitudinal Software Maintenance Experiment
<p>This dataset contains IDE interaction traces (logs) and screen videos obtained from a comprehensive study conducted to understand in detail how software professionals plan and execute maintenance activities on real-life software.</p>
ESEM18 Maintenance Communities Dataset
<p>This dataset contains data in the paper: The Birth, Growth, Death and Rejuvenation of Software Maintenance Communities (ESEM 2018). </p>
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
<h2>README</h2> <p>The dataset consists of the following components:<br> <br>- `<strong>prompts.txt</strong>`: This file contains the prompts used for large language models.<br> <br>- `<strong>Dataset</strong>` directory: includes general information about the dataset. Specifically, the `dataset.xlsx` file lists flaky and non-flaky tests, along with their root causes and fix types.<br> <br>- `<strong>Full</strong>` directory contains two subdirectories: `Flaky` and `Non-flaky`. Each of these directories is organized by individual GitHub organization projects, with each project having its list of repository subdirectories. These subdirectories are further divided into “issues” and “pull requests” (PRs).</p> <p><br>- `<strong>Method</strong>` level subdirectory has a similar structure but contains extracted code snippets at the method level instead of full code listings. The `code.diff` file is copied over and left unaltered. </p> <p><br>- <strong>Issue Directories (IRs):</strong> Named with an `issueID` template, each issue directory contains a `log.issue` file that includes the extracted description, comments, and metadata.<br> <br>- <strong>PR Directories (PRs)</strong>: Named using the `prID` template, each PR directory contains the text, comments, and metadata in the `pr.log` file. The text of the associated issue is stored in the `log.issue` file. Code listings are stored in a file with the `.bug` suffix, while the corresponding fixed version is in a `.fix` file. The `code.diff` file contains the patch that transforms the `.bug` version into the `.fix` version.</p> <p><br><strong>Additional notes:</strong><br>Issues with associated pull requests in `dataset.xlsx` are combined into the pull request directory template. If two pull requests are listed for a row, a PR directory is created for each. Due to updates in the extended dataset, some repositories have been renamed or archived, meaning the current repository directory names in `Dataset` will include both the previous and new names if it has been changed (e.g., a repository previously saved as Qiskit/qiskit-terra may now be saved as Qiskit/qiskit following the renaming from qiskit-terra to qiskit).</p> <h2>Directory Structure:</h2> <p><br>├── prompts.txt<br>├── Dataset/<br> └── dataset.xlsx<br>├── Full/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-Flaky/<br> └── <Organization>/<Repository>/...<br>├── Method/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-flaky/<br> └── <Organization>/<Repository>/...</p> <p> </p>
Figure 3 in Importance of riparian vegetation and wood-pastures in the maintenance of bat assemblages in a highly fragmented landscape in Veracruz, Mexico
Figure 3: Rank abundance curves of bats captured in wood-pastures (A) and riparian vegetation (B) in Jamapa, Veracruz, Mexico. Numbers indicate species, 1: Artibeus jamaicensis, 2: Sturnira parvidens, 3: A. lituratus, 4: S. hondurensis, 5: Glossophaga soricina, 6: G. commissarissi, 7: Rhogeesa tumida, 8: Desmodus rotundus, 9: Phyllostomus discolor, 10: Eptesicus furinalis, 11: Carollia sowelli, 12: A. phaeotis, 13: Uroderma bilobatum, 14: Molossus rufus, 15: C. perspicillata, 16: Chiroderma salvini, 17: A. watsoni, 18: C. villosum, 19: Pteronotus parnelli, 20: Platyrrhinus helleri, 21: Micronycteris microtis, 22: A. toltecus, 23: Centurio senex, 24: P. davyi, 25: Mormoops megallophylla, 26: Eumops bonariensis, 27: Promops centralis, 28: Myotis californicus, 29: M. keaysi.
Figure 2 in Importance of riparian vegetation and wood-pastures in the maintenance of bat assemblages in a highly fragmented landscape in Veracruz, Mexico
Figure 2: Species accumulation curves for wood-pastures and riparian vegetation in the locality of Jamapa, Veracruz, Mexico. Hill's numbers (effective numbers of species) are in brackets. According to non-overlapping confidence intervals only q1 ("typical" species) and q2 (dominant species) were significantly higher in riparian vegetation compared to pastures.
Data from: Ethnicity- and sex-based discrimination and the maintenance of self-esteem
The psychological underpinnings of labor market discrimination were investigated by having participants from Israel, the West Bank and Germany (N = 205) act as employers in a stylized employment task in which they ranked, set wages, and imposed a minimum effort level on applicants. State self-esteem was measured before and after the employment task, in which applicant ethnicity and sex were salient. The applicants were real people and all behavior was monetarily incentivized. Supporting the full self-esteem hypothesis of the social identity approach, low self-esteem in women was associated with assigning higher wages to women than to men, and such behavior was related to the maintenance of self-esteem. The narrower hypothesis that successful intergroup discrimination serves to protect self-esteem received broader support. Across all participants, both ethnicity- and sex-based discrimination of out-groups were associated with the maintenance of self-esteem, with the former showing a stronger association than the latter.
Data from: Maintenance of genetic and morphological identity in two sibling Syrrhopodon species (Calymperaceae, Bryopsida) despite extensive introgression
Bryophytes are a group of land plants wherein the role of hybridization has long been challenged. Using Genotyping by Sequencing to circumvent the lack of molecular variation at selected loci previously used for phylogeny and morphology, we determine the level of genetic and morphological divergence and reproductive isolation between the sibling Syrrhopodon annotinus and S. simmondsii (Calymperaceae, Bryopsida) that occur in sympatry but in different habitats in lowland Amazonian rainforests. A clear morphological differentiation and a low (0.06), but significant Fst derived from the analysis of 183 SNPs were observed between the two species. Conspecific pairs of individuals consistently exhibited higher average kinship coefficients along a gradient of geographic isolation than interspecific pairs. The weak, but significant genetic divergence observed is consistent with growing evidence that ecological specialization can lead to genetic differentiation among bryophyte species. Nevertheless, the spatial genetic structures of the two species were significantly correlated, as evidenced by the significant slope of the Mantel test based on kinship coefficients between pairs of interspecific individuals and the geographic distance separating them. Interspecific pairs of individuals are thus more closely related when they are geographically closer, suggesting that isolation-by-distance is stronger than the interspecific reproductive barrier and pointing to interspecific gene flow. We conclude that interspecific introgression, whose role has long been questioned in bryophytes, may take place even in species wherein sporophyte production is scarce due to dioicy, raising the question as to what mechanisms maintain differentiation despite weak reproductive isolation.
Data from: Mating system contributes only slightly to female maintenance in gynodioecious Geranium maculatum (Geraniaceae)
Gynodioecy, the co-occurrence of female and hermaphroditic individuals within a population, is an important intermediate in the evolution of separate sexes. The first step, female maintenance, requires females to have higher seed fitness compared with hermaphrodites. A common mechanism thought to increase relative female fitness is inbreeding depression avoidance, the magnitude of which depends on hermaphroditic selfing rates and the strength of inbreeding depression. Less well studied is the effect of biparental inbreeding on female fitness. Biparental inbreeding can affect relative female fitness only if its consequence or frequency differs between sexes, which could occur if sex structure and genetic structure both occur within populations. To determine whether inbreeding avoidance and/or biparental inbreeding can account for female persistence in Geranium maculatum, we measured selfing and biparental inbreeding rates in four populations and the spatial genetic structure in six populations. Selfing rates of hermaphrodites were low and did not differ significantly from zero in any population, leading to females gaining at most a 1–14% increase in seed fitness from inbreeding avoidance. Additionally, although significant spatial genetic structure was found in all populations, biparental inbreeding rates were low and only differed between sexes in one population, thereby having little influence on female fitness. A review of the literature revealed few sexual differences in biparental inbreeding among other gynodioecious species. Our results show that mating system differences may not fully account for female maintenance in this species, suggesting other mechanisms may be involved.
Data from: Exploring the role of Micronesian islands in the maintenance of coral genetic diversity in the Pacific Ocean
Understanding how genetic diversity is maintained across patchy marine environments remains a fundamental problem in marine biology. The Coral Triangle, located in the Indo-West Pacific, is the center of marine biodiversity and has been proposed as an important source of genetic diversity for remote Pacific reefs. Several studies highlight Micronesia, a scattering of hundreds of small islands situated within the North Equatorial Counter Current, as a potentially important migration corridor. To test this hypothesis, we characterized the population genetic structure of two ecologically important congeneric species of reef-building corals across greater Micronesia, from Palau to the Marshall Islands. Genetic divergences between islands followed an isolation-by-distance pattern, with Acropora hyacinthus exhibiting greater genetic divergences than A. digitifera, suggesting different migration capabilities or different effective population sizes for these closely related species. We inferred dispersal distance using a biophysical larval transport model, which helped explain an additional 15-21% of genetic variation compared to between-island geographic distance alone. For both species, genetic divergence accumulates and genetic diversity diminishes with distance from the Coral Triangle, supporting the hypothesis that Micronesian islands act as important stepping-stones connecting the central Pacific with the species rich Coral Triangle. However, for A. hyacinthus, the species with lower genetic connectivity, immigration from the sub-equatorial Pacific begins to play a larger role in shaping diversity than input from the Coral Triangle. This work highlights the enormous dispersal potential of broadcast-spawning corals and identifies the biological and physical drivers that influence coral genetic diversity on a regional scale.
Source–sink dynamics assists the maintenance of a pollinating wasp
<p>Dispersal that unites spatially subdivided populations into a metapopulation with source–sink dynamics is crucial for species persistence in fragmented landscapes. Understanding such dynamics for pollinators is particularly urgent owing to the ongoing global pollination crisis. Here, we investigated the population structure and source–sink dynamics of a pollinating wasp (Wiebesia sp. 3) of Ficus pumila in the Zhoushan Archipelago of China. We found significant asymmetry in the pairwise migrant numbers for 22 of 28 cases on the historical timescale, but only 2 on the contemporary timescale. Despite a small population size, the sole island not colonized by a superior competitor wasp (Wiebesia sp. 1) consistently behaved as a net exporter of migrants, supplying large sinks. Comparable levels of genetic diversity, with few private alleles and low genetic differentiation (total FST: 0.03; pairwise FST: 0.0005–0.0791), were revealed among all the islands. There was a significant isolation-by-distance pattern caused mainly by migration between the competition-free island and other islands, otherwise the pattern was negligible. The clustering analysis failed to detect multiple gene pools for the whole region. Thus, the sinks were most likely organized into a patchy population. Moreover, the estimates of effective population sizes were comparable between the two timescales. Thus the source–sink dynamics embedded within a well-connected population network may allow Wiebesia sp. 3 to persist at a competitive disadvantage. This study provides evidence that metapopulations in the real world may be complicated and changeable over time, highlighting the necessity to study such metapopulations in detail.</p>
dataset Master Thesis Optimising parity game solvers using dynamic SCC maintenance
<p>Complete dataset of my Master Thesis Optimising parity game solvers using dynamic SCC maintenance</p>
Parenting costs time: Changes in pair bond maintenance across pregnancy and infant rearing in a monogamous primate (Plecturocebus cupreus)
<p>Dataset for manuscript submitted to New Directions for Child and Adolescent Development. Abstract for article:</p> <p>Relationships support social animals’ health, but maintaining relationships is challenging. When transitioning to parenthood, new parents balance pair-bond maintenance with infant care. We studied pair-bond maintenance via affiliation in 22 adult titi monkey pairs (<em>Plecturocebus cupreus</em>) for 16 months centered around their first offspring’s birth. Pair affiliation peaked during pregnancy, decreased across the postpartum period, and rose after reaching minimum affiliation 32.6 weeks postpartum. Pairs in which fathers carry infants more than average had lower affiliation at the infant’s birth and return to an increase in affiliation sooner. Parents of infants who were slow to independence had higher rates of affiliation. Titi monkey infants actively prefer their fathers; mothers may avoid their infant-carrying mate, suggesting infants play an active role in parental affiliative decline. Our data supports previous findings that affiliation between partners declines following an infant’s birth, but demonstrates new knowledge about the extent and duration of affiliative decline.</p>
Maintenance of spatial gene expression by Polycomb-mediated repression after formation of a vertebrate body plan
<p>This dataset contains zebrafish (<em>Danio rerio</em>) raw RNA and ChIP sequencing data:</p> <ul> <li>RNA-seq: <ul> <li>RNAseq_Wildtype_rep[12].fastq.gz: 2 biological replicates of single-end RNA-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>RNAseq_Wildtype_rep[3-6].fastq.gz 4 biological replicates of paired-end RNA-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> </ul> </li> <li>ChIP-seq: <ul> <li>lane1_MPZezh2WT-24hpf-Ezh2__R[12].fastq.gz: 1 sample of paired-end Ezh2 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>lane1_MPZezh2WT-24hpf-Rnf2__R[12].fastq.gz: 1 sample of paired-end Rnf2 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>lane1_MPZezh2WT-24hpf-H3K27me3__R[12].fastq.gz: 1 sample of paired-end H3K27me3 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>*MPZezh2WT-24hpf-H3K4me3*: 2 biological replicates of paired-end H3K4me3 ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>MPZezh2WT-24hpf-Ezh2-spikein-13277_R[12].fastq.gz: 1 sample of paired-end Ezh2 ChIP-seq data (with Drosophila H2Ay spike in) from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>MPZezh2WT-24hpf-H3K27A[cC]*: 2 biological replicates of paired-end H3K27ac ChIP-seq data from 24hpf wild-type (TU/TL background) whole embryo lysates</li> <li>MPZezh2WT-24hpf-H3K27me3-spikein-13275_R[12].fastq.gz: 1 sample of paired-end H3K27me3 ChIP-seq data (with Drosophila H2Ay spike in) from 24hpf wild-type (TU/TL background) whole embryo lysates</li> </ul> </li> </ul>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Maintenance Data Set for Car Rental Company
<p>Maintenance records for a car rental company. The column details are provided as below:</p> <ul> <li><strong>CARID: </strong>Unique ID for each of the cars in the fleet</li> <li><strong>Brand:</strong> Brand of the car</li> <li><strong>Model :</strong> Model of the car </li> <li><strong>SubModel :</strong> Submodel for the brand and model of the car</li> <li><strong>Segment:</strong> there are segments for the cars ( for further information please refer to wikipedia : https://en.wikipedia.org/wiki/Car_classification ) </li> <li><strong>Year :</strong> Model year of the car</li> <li><strong>Fuel Type: </strong>Fuel consumption Type ( like Diesel, Petrol, Hybrid or Electric)</li> <li><strong>Gear : </strong>Gear type of the car</li> <li><strong>Milage:</strong> is the of the call of the maintenance (expected milage for the maintenance), the actual milage might be different than the scheduled one, this column indicates the scheduled milage for maintenance.</li> <li><strong>MaintenanceMile :</strong> is the actual mile during the call </li> <li>CallID: a unique id for each maintenance call. </li> <li>StockID: ID of the parts or services provided during the call</li> <li>Quantity: Quantity of the parts or duration of service or amount of items used during the maintenance</li> <li>UnitPrice : unit price for the parts, services or consumption</li> <li>SubTotal : Line bases subtotal for the invoice (the actual total value in US Dollars for the quantity and the unit price : unitprice x quantity)</li> <li>Discount: Line based discount for the stockid, quantity and unitprice</li> <li>Tax : Legal taxes in USD</li> <li>GrandTotal : invoice based total amount (total amount of each lines in the same invoice)</li> <li>InvoiceDate : Data of invoice in Year - Month - Day Hour - Minute - Seconds , you can skip the hour, minute and seconds </li> <li>VendorID: Unique ID of each vendor providing the maintenance service</li> <li>CityID: Location of the maintenance</li> <li>MaintenanceType : It can be a regular maintenance ( 0) , request based maintenance (1) or unknown (2) </li> </ul> <p> </p>
Female investment in terminal reproduction or somatic maintenance depends on infection dose - data
<p>This is the data used in an experiment investigating the effects of infection dose on fecundity compensation in the red flour beetle.</p>
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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.