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3,480 results for “a value”
Data from: Bird diversity and endemism along a land-use gradient in Madagascar: the conservation value of vanilla agroforests
<div> <div> <p>Land-use change is the most important driver of biodiversity loss worldwide and particularly so in the tropics, where natural habitats are transformed into large-scale monocultures or heterogeneous landscape mosaics of largely unknown conservation value. Using birds as an indicator taxon, we evaluated the conservation value of a landscape mosaic in north-eastern Madagascar, a biodiversity hotspot and the center of global vanilla production. We assessed bird species richness and composition by conducting point counts across seven prevalent land-use types (forest- and fallow-derived vanilla agroforests, woody and herbaceous fallow that are part of a shifting cultivation system, rice paddy, forest fragment and contiguous old-growth forest). We find that old-growth forest had the highest species richness, driven by a high share of endemics. Species richness and community composition in forest-derived vanilla agroforest was similar to forest fragment, whereas fallow-derived vanilla agroforest was most comparable to woody fallow. The open land-use types herbaceous fallow and rice paddy had fewest species. Across forest fragment, vanilla agroforests and woody fallow, endemic bird species richness was positively correlated to landscape-scale forest cover. We conclude that both fallow- and forest-derived vanilla agroforests play an important but contrasting role for bird conservation in the landscape: Fallow-derived agroforests are less valuable but take fallow land out of the shifting cultivation cycle, possibly preventing further degradation. Conversely, forest-derived agroforests contribute to forest degradation but may avoid total loss of forest fragments. Considering the land-use history of agroforests may thus be a promising avenue for future research beyond the case of vanilla.</p> </div> </div>
POES N15 spacecraft L* values 1998-2014 determined by the TS05 external magnetic field model
<p>L* values for the POES N15 spacecraft from 1998-2014 determined by the TS05 external magnetic field model. L* calculations were performed by the SpacePy package in Python (<a href="https://spacepy.github.io/">https://spacepy.github.io/</a>).</p> <p>Each year has been broken up into multiple files in chronological number order (beginning at 1) . The number of files for each year are as follows:</p> <p>1998 - 5</p> <p>1999 - 10</p> <p>2000-2014 - 15</p> <p>Each file is a gzipped csv file named via the following: poes_n15_YEAR_FILENUM.csv.gz.</p> <p> </p> <p>References:</p> <p>Tsyganenko, N. A., and Sitnov, M. I. (2005), Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, <em>J. Geophys. Res.</em>, 110, A03208, doi:<a href="https://doi.org/10.1029/2004JA010798">10.1029/2004JA010798</a>.</p>
Stable and dynamic representations of value in the prefrontal cortex
<p>This data set was compiled for the experiments detailed in <em>Enel, P., Wallis, J., & Rich, E. (2019). Stable and dynamic representations of value in the prefrontal cortex. </em><i>Elife</i> <em>9 (2020): e54313 </em><span class="doi"><a class="doi__link" href="https://doi.org/10.7554/eLife.54313">10.7554/eLife.54313</a></span></p> <p>Optimal decision-making requires that stimulus-value associations are kept up to date by constantly comparing the expected value of a stimulus with its experienced outcome. To do this, value information must be held in mind when a stimulus and outcome are separated in time. However, little is known about the neural mechanisms of working memory (WM) for value. Contradicting theories have suggested WM requires either persistent or transient neuronal activity, with stable or dynamic representations respectively. To test these hypotheses, we recorded neuronal activity in the orbitofrontal and anterior cingulate cortex of two monkeys performing a valuation task. We found that features of all hypotheses were simultaneously present in prefrontal activity, and no single hypothesis was exclusively supported. Instead, mixed dynamics supported robust, time invariant value representations while also encoding the information in a temporally specific manner. We suggest that this hybrid coding is a critical mechanism supporting flexible cognitive abilities.</p>
Human tissue gene expression TPM values for the advanced forensic biology course
<p><strong>Gene expression per tissue</strong></p> <p>This dataset comes from the <a href="https://www.gtexportal.org/home/">Genotype-Tissue Expression (GTEx)</a> database that gathers gene expression data from various human tissues. Specifically, the <strong>GTEx Analysis v7</strong> version was used.</p> <p>The file is called "<a href="https://zenodo.org/api/files/9f872792-6c96-4226-b659-ce0807da66e8/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_median_tpm.tsv">GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_tpm.tsv</a>" and contains tabulated-separated values of median TPM gene expression by tissue (TPM: transcript per million).</p> <p> </p> <p><strong>Genes with a favored sucutaneous-adipose expression profile</strong></p> <p>This file contains 195 genes that have a statistically (p < 0.01) favored expression profile in subcutaneous adipose tissue compared to other tissues. </p> <p>https://zenodo.org/api/files/7342bbfa-0e4d-49e7-b916-5eff5b638c33/genes_with_a_subcutaneous_adipose_favored_expression.tsv</p>
Data from: Minimum size limits and the reproductive value of numerous, young, mature female fish
<p>Fisheries management relies on various catch and effort controls to preserve spawning stock biomass and maximise sustainable yields while limiting fishery impacts on marine ecosystems. These include species-specific minimum or maximum size limits to protect either small non-reproductive sub-adults, a portion of reproductively mature adults, or large highly fecund individuals. Protecting size classes of mature fish is expected to yield a viable source of larvae for replenishing populations and reduce the risk of recruitment overfishing, yet size-specific recruitment contributions have rarely been assessed empirically. Here we apply genetic parentage analysis to measure the reproductive success of a size-structured population of a commercially important species of coral grouper (<i>Plectropomus</i> <i>maculatus </i>- Serranidae) in no-take marine reserves in the Great Barrier Reef Marine Park, Australia. Although the per-capita reproductive success of individual fish increases rapidly with body-length, the numerous young mature female fish (NYMFFs), below the minimum size limit (38 cm total length), were responsible for generating disproportionately large contributions (36%) towards larval replenishment of both fished and reserve reefs. Our findings indicate that minimum size limits are an effective harvest control measure to safeguard a portion of the spawning stock biomass for coral grouper and supplement recruitment subsidies assured from no-take marine reserves.</p>
Going digital: Added value of electronic data collection in 2018 Afghanistan Health Survey
<p>These data were collected and analyzed to compare measures of cost efficiency, data quality and user acceptability between paper-based and digitally collected data for a nationally representative household survey in Afghanistan. </p>
Age predicts risky investment better than residual reproductive value
Life-history theory predicts that investment into reproduction should increase as future reproductive opportunities (i.e., residual reproductive value, RRV) decrease. Researchers have thus intuitively used age as a proxy for RRV and assume RRV decreases with age when interpreting age-specific investment. Yet, age is an imperfect proxy for RRV and may even be a poor correlate in some systems. We used a 30-year study of the nesting ecology of painted turtles ( Chrysemys picta ) to assess how age and RRV compare in explaining variation in a risky investment behavior. We predicted that RRV would be a better predictor of risky investment than age because RRV accounts for variation in future reproductive potential across life. We found that RRV was high in early life, slowly decreased until midlife, and then steadily decreased to terminal reproduction. However, age predicted risky behavior better than RRV. This finding suggests stronger correlates of age (e.g., size) may be more responsible for this behavior in turtles. This study highlights that researchers should not assume that age-specific investment is driven by RRV and that future work should quantify RRV to more directly test this key element of life-history theory.
Quantitative dissection of color patterning in the foliar ornamental Coleus reveals underlying features driving aesthetic value
<p>readMe.txt (this file) /Coleus_scans.zip: This directory contains 3 sub-directories:</p> <p>1. raw_scans: Raw scans of coleus leaves. Data collection described below.</p> <p>2. binary_leaves: A zip file of individual binary leaves isolated from the raw scans. Data processing described below.</p> <p>3. colored_leaves: A zip file of individual colored leaves isolated from the raw scans using the binary leaf silhouettes. Data processing described below.</p> <p>Data collection</p> <p>Coleus leaf scans were collected from a starting population of 50,000 seedlings that were originally harvested from 133 open-pollinated mother plants in early January in Gainesville, FL. We organized the seedlings into families based on their maternal parents, grew the plants for five weeks and then selected ~2,000 individuals as potential new cultivars based on their foliar color patterning and branching architecture in mid-February. This data represents the youngest fully expanded leaf from each plant between 5-6 weeks of age. Leaves were imaged on Epson Perfection V550 Scanners with Kodak KOCSGS color separation guides included for color calibration. Analysis App Color analysis can be performed using an open-access software program called ColourQuant (Li et al., 2019); available on github: github.com/maoli0923/ColourQuant).</p> <p>Explanation of isolated binary and colored leaf files</p> <p>To isolate individual leaves from the raw data scans - we adjusted the RGB color balance on each scan by a white balance method so that the white swatch in the Kodak KOCSGS color separation guide is pure white, to ensure that scanners were not biasing the color data. Next, we segmented the leaves from the background by converting the RGB matrix into hue-saturation-value (HSV) format. Since most background pixels become grey in HSV, this was used to set a threshold that separates grey values from true leaf values. We then used the binary leaf silhouettes to extract the individual colored leaves by setting the background to pure white, and the foreground to pure black. We manually adjusted the thresholding for leaves that could not be automatically extracted due to shadows in the scan. The binary and colored leaf folder contain outliers, including leaves that were overlapping on the scanner, very small, or broken. These can be manually removed before analysis. </p> <p>File ID Key</p> <p>Files are named with the following code: Year_Family_Scan#. Files containing selected leaves for cultivar development are prepended with an “S” and files containing maternal leaf scans are prepended with an “M.”</p> <p>For questions regarding released dataset contact: Margaret Frank mhf47@cornell.edu</p> <p><br> Li, M., Frank, M.H., and Migicovsky, Z.. 2019a. ColourQuant: A high throughput technique to extract and quantify colour phenotypes from plant images. arXiv 190301652. http://arxiv.org/abs/1903.01652</p>
Exploratory Landscape Analysis Feature Values for the 24 Noiseless BBOB Functions
<p>The dataset contains feature values for the so-called "cheap" features available in the R package <em>flacco</em> [https://github.com/kerschke/flacco] on the 24 noiseless BBOB functions of the COCO benchmark environment [https://github.com/numbbo/coco].</p> <p>Precisely, the data set covers <strong>raw data</strong> and <strong>median</strong> feature values for the following</p> <ul> <li><strong>Feature Sets: </strong><em>dispersion, <span class="math-tex">\(y\)</span>-distribution, meta model, information content, nearest better clustering</em> and <em>principal component analysis.</em></li> <li><strong>Functions: </strong>24 noiseless BBOB functions of the COCO benchmark environment [https://github.com/numbbo/coco].</li> <li><strong>Instances: </strong>First five instances of each function.</li> <li><strong>Dimension <span class="math-tex">\(d\)</span>: </strong>6 different dimensions, <span class="math-tex">\(d \in \{5,10,15,20,25,30\}\)</span>.</li> <li><strong>Sample size ("budget") <span class="math-tex">\(b\)</span> for Feature Extraction: </strong>7 different options per dimension, <span class="math-tex">\(b \in \{ 30d, 50d, 100d, 250d, 650d, 800d, 1000d\}\)</span>.</li> <li><strong>Sampling Distribution:</strong> Features are computed using Sobol' low-discrepancy sequence as sampling strategy.</li> <li><strong>Repetitions: </strong>100 independent repetitions are available.</li> </ul> <p>Caused by the heavy computation times, <strong> some data records are missing:</strong></p> <ul> <li>The <em>dispersion</em> feature values are not available for <span class="math-tex">\((d=25,b=1000d), (d=30,b=800d) \)</span> and <span class="math-tex">\((d=30,b=1000d)\)</span>;</li> <li>Instances 2 to 5 are not available for: <ul> <li><span class="math-tex">\(1000 d\)</span> budgets with <span class="math-tex">\(d \geq 10\)</span></li> <li><span class="math-tex">\(800d\)</span> budgets with <span class="math-tex">\(d \geq 15\)</span></li> <li><span class="math-tex">\(650d\)</span> budgets with <span class="math-tex">\(d \geq 20\)</span></li> <li><span class="math-tex">\(250d\)</span> budgets with <span class="math-tex">\(d = 30\)</span></li> </ul> </li> </ul>
Recycling Carbon Dioxide In The Cement Industry To Produce Added-Value Additives
<p>Prof. Simelys Hernández of the Italian Institute of Technology presented the RECODE project (<a href="http://www.recodeh2020.eu">recodeh2020.eu</a>) at the EU Green Week Webinar 'Towards Zero Pollution in the Production of Green Fuels and Chemicals' on 4th June 2021.</p>
Data from: Low-productivity boreal forests have high conservation value for lichens
1. Land set aside for preservation of biodiversity often has low productivity. As biodiversity generally increases with productivity, due to higher or more diverse availability of resources, this implies that some of the biodiversity may be left unprotected. Due to a lack of knowledge on the species diversity and conservation value of low-productivity habitats, the consequences of the biased allocation of low-productivity land for set-asides are unknown. 2. We examined the conservation value of boreal low-productivity forests (potential tree growth < 1 m3 ha-1 year-1) by comparing assemblages of tree- and deadwood-dwelling lichens and forest stand structure between productive and low-productivity forest stands. We surveyed 84 Scots pine-dominated stands in three regions in Sweden, each including four stand types: two productive (managed and unmanaged) and two low-productivity stands (on mires and on thin, rocky soils). 3. Lichen species richness was highest in low-productivity stands on thin soil, which had similar amounts and diversity of resources (living trees and dead wood) to productive unmanaged stands. Stands in low-productivity mires, which had low abundance of living trees and dead wood, hosted the lowest lichen richness. Lichen species composition differed among stand types, but none of them hosted unique species. The differences in both species richness and composition were more pronounced in northern than in southern Sweden, likely due to shorter history of intensive forestry. 4. Synthesis and applications: Boreal low-productivity forests can have as high conservation value as productive forests, which should be reflected in conservation strategies. However, their value is far from uniform, and conservation planning should acknowledge this variation and not treat all low-productivity forests as a uniform group. Some types of low-productivity forest (e.g. on thin soil) are more valuable than others (e.g. on mires), and should thus be prioritized in conservation. It is also important to consider the landscape context: low-productivity forests may have higher value in landscapes where high-productivity forests are highly influenced by forestry. Finally, although low-productivity forests can be valuable for some taxa, productive forests may still be important for other taxa.
Artificial dataset for "Automatic Determination of Parameters Values for Heuristics Miner++"
<p>This set of processes, built for test purposes [1], is composed of 125 process models. These processes were created using PLG [2, 3]. The generation of the random processes is based on some basic “process patterns”, like the AND-split/join, XORsplit/join, the sequence of two activities, and so on.</p> <p>For each of the 125 process models, two logs were generated: one with 250 traces and one with 500 traces. In these logs, the 75% of the activities are expressed as time intervals (the other ones are instantaneous) and 5% of the traces are noise. In this context “noise” is considered either a swap between two activities or removal of an activity.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>A. Burattin, A. Sperduti. "<em>Automatic Determination of Parameters Values' for Heuristics Miner+</em>+". In <em>Proceedings of IEEE Congress on Evolutionary Computation</em> (IEEE WCCI CEC 2010); 10.1109/CEC.2010.5586208</li> <li>A. Burattin. “<em>PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings</em>”. In <em>CoRR</em> abs/1506.08415, Jun. 2015.</li> <li>A. Burattin and A. Sperduti. “<em>PLG: a Framework for the Generation of Business Process Models and their Execution Logs</em>”. In <em>Proc. of the 6th Int. Workshop on Business Process Intelligence</em> (BPI 2010); 2010.10.1007/978-3-642-20511-8_20.</li> </ol>
Prostate specific antigen density values among patients with symptomatic prostatic enlargement in Nigeria
<p><strong>Background</strong></p> <p>This study aims to estimate the prostate specific antigen density(PSAD) cut off level for detecting prostate cancer( CAP) in Nigerian men with “grey zone PSA’’(4-10ng/ml) and normal digital rectal examination findings . We addressed this research question: Is the international PSAD cut off of 0.15 ideal for detecting CAP in our symptomatic patients with “grey zone PSA’’?</p> <p><strong> Methods</strong></p> <p>Aim: To estimate the prostate specific antigen density(PSAD) cut off level for detecting prostate cancer( CAP) in Nigerian men with “grey zone PSA’’(4-10ng/ml) and normal digital rectal examination findings .</p> <p>Design: prospective</p> <p>Setting: A tertiary medical center in Enugu Nigeria</p> <p> Participants: Two hundred and fifty four men with either benign prostatic hyperplasia (BPH) or prostate cancer (CAP) were recruited. </p> <p> Intervention: Patients with PSA above 4ng/ml or abnormal digital rectal examination or hypoecoic lesion in the prostate were biopsied.</p> <p>Outcome measures: PSAD and histology report of BPH or CAP</p> <p><strong>Results</strong></p> <p>Ninety seven patients had cancer of the prostate (CAP) while 157 has benign prostatic hyperplasia (BPH). Seventy two patients had their serum PSA value within the range of 4.0 and 10ng/ml . PSAD cut off level to detect CAP was 0.04(sensitivity 95.88 %; specificity 28.7 %).</p> <p> </p> <p><strong>CONCLUSION</strong> </p> <p>The PSAD cut off level generated for Nigerian men in this study is 0.04 which is relatively different from international consensus. This PSAD cut off level has a positive correlation with histology and could detect patients with CAP who have “grey zone PSA’’.</p>
Oscillation test results for different Vdd values at positions X10Y90 and X44Y108
<p>16 files (X10Y90_X_XXV.bin and X44Y108_X_XXV.bin), where X_XX refers to the value of Vdd = oscillation counts generated by a TERO TRNG at placements X10Y90 and X44Y108 on a Xilinx Spartan 6 FPGA, with different voltage levels. Used in above mentioned paper in Figure 3.</p>
Aeolian saltation fieldwork 30-minute wind and saltation values
<p>Excel spreadsheet (.XLSX) containing 30-minute values for shear velocity, shear stress, wind direction, stability parameter, saltation layer height, total saltation flux, saltation detection frequency, and associated uncertainties for most of these values for field deployments at Jericoacoara (Brazil), Rancho Guadalupe (California), and Oceano (California).</p>
Aeolian saltation fieldwork binned 30-minute wind and saltation values
<p>Excel spreadsheet (.XLSX) containing binned 30-minute values for shear velocity, shear stress, excess shear stress, saltation layer height, total saltation flux, normalized saltation flux, saltation detection frequency, and associated uncertainties for all of these values. The spreadsheet also lists the numbers of 30-minute values in each bin. Data are from field deployments at Jericoacoara (Brazil), Rancho Guadalupe (California), and Oceano (California).</p>
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 4. - Intralineage and interlineage uncorrected genetic distance values for the "ivonicus/yuna" and "carteri" lineages.
Figure 4. - Intralineage and interlineage uncorrected genetic distance values for the "ivonicus/yuna" and "carteri" lineages.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
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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.