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8,028 results for “Influence”
Detection of Real-World Influence through Social Media
<p><strong>Description. </strong>This dataset corresponds to the resources produced for the following conference paper and its extended version:</p> <ol> <li>J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in <em>2nd European Network Intelligence Conference (ENIC)</em>, 2015, pp. 83–90. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01164453">hal-01164453</a>⟩ DOI: <a href="http://doi.org/10.1109/ENIC.2015.20">10.1109/ENIC.2015.20</a></li> <li>J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” <em>Social Network Analysis and Mining </em>6:25, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01203171">hal-01203171</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-016-0329-x">10.1007/s13278-016-0329-x</a></li> </ol> <p>Raw data are available through the official RepLab page: <a href="http://nlp.uned.es/replab2014/">http://nlp.uned.es/replab2014/</a> (follow <a href="http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz">http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz</a>)</p> <p><strong>Source code. </strong>The source code used to generate these output is available on GitHub: <a href="https://github.com/CompNet/Influence">https://github.com/CompNet/Influence</a></p> <p><strong>Funding. </strong>This work was partly funded by the French National Research Agency (ANR), through the project <a href="https://anr.fr/Project-ANR-12-CORD-0002">ImagiWeb ANR-12-CORD-0002</a>.</p> <p><strong>Contact. </strong>Jean-Valère Cossu <<a href="mailto:jean-valere.cossu@alumni.univ-avignon.fr">jean-valere.cossu@alumni.univ-avignon.fr</a>></p> <p><strong>Citation. </strong>If you use these data, please cite paper [1] above.</p> <p><br><code>@InProceedings{Cossu2015,</code><br><code> author = {Cossu, Jean-Valère and Dugué, Nicolas and Labatut, Vincent},</code><br><code> title = {Detecting Real-World Influence Through {Twitter}},</code><br><code> booktitle = {2\textsuperscript{nd} European Network Intelligence Conference},</code><br><code> year = {2015},</code><br><code> pages = {83-90},</code><br><code> address = {Karlskrona, SE},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ENIC.2015.20},</code><br><code>}</code></p> <p><strong>Details. </strong>This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.</p> <p>File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).</p> <p>RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.</p> <p>The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all. </p> <p>Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.</p>
Dataset: Does vendor breeding colony influence sign- and goal-tracking in Pavlovian conditioned approach?
<p>Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.</p>
Marine plastics alter the organic matter composition of the air-sea boundary layer, with influences on CO2 exchange: a large-scale analysis method to explore future ocean scenarios
<p>Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications in the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used six large scale mesocosms to simulate future ocean scenarios of high plastic concentration. Each mesocosm was filled with 3 m3 of seawater from the oligotrophic Sea of Crete, in the Eastern Mediterranean Sea. A known amount of standard polystyrene microbeads of 30 μm diameter was added to three replicate mesocosms, while maintaining the remaining three as plastic-free controls. Over the course of a 12-day experiment, we explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of carbohydrate-like and proteinaceous marine gel compounds in the sea-surface microlayer. Importantly, this resulted in a 3 % reduction in the concentration of dissolved CO2 in the underlying water. This reduction was associated to both direct and indirect impacts of microplastic pollution on the uptake of CO2 within the marine carbon cycle, by modifying the biogenic composition of the sea's boundary layer with the atmosphere.</p>
Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing
<p><span>Plasma electrolytic polishing (PeP) is mainly used to improve the surface quality and thus </span><span>the performance of electrically conductive parts. It is usually used as an anodic process, i.e., the </span><span>workpiece is positively charged. However, the process is susceptible to high current peaks during</span> <span>the formation of the vapour–gaseous envelope, especially when polishing workpieces with a large </span><span>surface area. In this study, the influence of the anode immersion speed on the current peaks and the</span> <span>average power during the initialisation of the PeP process is investigated for an anode the size of a </span><span>microreactor mould insert. Through systematic experimentation and analysis, this work provides</span> <span>insights into the control of the initialisation process by modulating the anode immersion speed. The </span><span>results clarify the relationship between immersion speed, peak current, and average power and</span> <span>provide a novel approach to improve process efficiency in PeP. The highest peak current and average </span><span>power occur when the electrolyte splashes over the top of the anode and not, as expected, when the </span><span>anode touches the electrolyte. By immersion of the anode while the voltage is applied to the anode</span> <span>and counterelectrode, the reduction of both parameters is over 80 %.</span></p>
Proline and β-alanine influence bumblebee nectar consumption without affecting survival
<p>These files (.txt) contain the dataset used for analyses of bumblebee aminoacid consumption and survival in the article "Proline and β-alanine influence bumblebee nectar consumption without affecting survival" by Bogo G. et al., accepted for publication in Apidologie (2024, xx:xxx-xxx, DOI: xxx).</p>
Dateset: Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix
<p>This dataset provides complementary material to the scientific research presented in the paper "<strong>Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix</strong>"<strong>,</strong> accepted for publication in PLOS ONE under number PONE-D-18-07662R1.</p> <p>A draft version of the paper is available at <a href="https://arxiv.org/abs/1803.05336">https://arxiv.org/abs/1803.05336</a></p> <p>This paper presents two studies targeting two groups of countries:</p> <ul> <li>[40] the set 40 worldwide countries set ;</li> <li>[EU] the set of 27 European Union countries as of February 2013.</li> </ul> <p>Data is derived using Reduced Google matrix analysis on the Wikipedia English, Arabic, Russian, German and French editions collected in February 2013. Networks representing each edition are available here:</p> <p><a href="http://www.quantware.ups-tlse.fr/QWLIB/topwikipeople/index.html">http://www.quantware.ups-tlse.fr/QWLIB/topwikipeople/index.html</a></p> <p>The following files are given:</p> <ul> <li>[GRedured_40.zip] GReduced matrix and its decomposition for [40] countries set</li> <li>[GRedured_EU.zip] GReduced matrix and its decomposition for [EU] countries set</li> <li>[PageRank_vs_CheiRank.zip] PageRank versus CheiRank figures for RuWiki and ArWiki</li> <li>[Sensitivity.xlsx] and [Sensitivity_html.xlsx] Sensitivity values for both [EU] and [RU] in either .xlsx or in .html format</li> </ul>
Dataset _ Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Bedrock radioactivity influences the rate and spectrum of mutation - Orthologous genes
<p>Alignments of the 2490 orthologous genes used in the article "Natural Bedrock radioactivity influences the rate and spectrum of mutation" to estimate the mutational spectrum and synonymous substitution rate.</p> <p>To compute accurate synonymous substitution rate, we removed genes with short sequences (<half of the alignment) and genes strongly supporting another phylogeny using ProfileNJ <a href="https://paperpile.com/c/Klqlpb/W5sS">(Noutahi et al. 2016)</a> with a bootstrap threshold of 90%, resulting in a subset of 769 genes listed in the file "List_769_1-to-1_orthologs_EvolutionRate.txt".</p> <p>Transcriptome paired-end reads used to define these orthologous genes have been deposited to the European Nucleotide Archive and are available under the study ID PRJEB14193.</p> <p>Sequences were aligned with Prank<a href="https://paperpile.com/c/Klqlpb/pilh"> (Löytynoja & Goldman 2008)</a> using a codon model and sites ambiguously aligned were removed with Gblocks <a href="https://paperpile.com/c/Klqlpb/c5kb">(Castresana 2000)</a>.</p>
Supplementary data for- Heat-evolved microalgae (Symbiodiniaceae) are stable symbionts and influence thermal tolerance of the sea anemone Exaiptasia diaphana
<p>Raw data and R codes for - Heat-evolved microalgae (Symbiodiniaceae) are stable symbionts and influence thermal tolerance of the sea anemone <em>Exaiptasia diaphana</em>. DOI: 10.1111/1462-2920.70011</p>
Dataset and R script for the analysis in the article "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", Journal of Cleaner Production
<p>We hereby publish the dataset (with metadata) and the R script (R Core team, 2018) used for implementing the analysis presented in the paper "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", <em>Journal of Cleaner Production </em>(Piras et al., 2023). The dataset is provided in csv format with semicolons as separators and "NA" for missing data. The dataset includes all the variables used in at least one of the models presented in the paper, either in the main text or in the Supplementary Material. Other variables gathered by means of the questionnaires included as Supplementary Material of the paper have been removed. The dataset includes inputted values for missing data on independent variables. These were inputted using two approaches: last observation carried forward (LOCF) - preferred when possible - and last observation carried backward (LOCB). The metadata are presented as a PDF file.</p>
Data from: Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication "<strong>Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide</strong>" published in Zoo Biology. It contains anonymised data from 540 captive flamingo populations, and includes the four species: <em>Phoeniconaias minor, Phoenicopterus chilensis, Phoenicopterus roseus</em> and<em> Phoenicopterus ruber</em>. Data were sourced from the Zoological Information Management System (ZIMS), operated by Species360 (https://www.species360.org/). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each flamingo species and both their current and historic population sizes. These data were used to investigate the relationship between reproductive success and both flock size, and structure, on a global scale.</p> <p>This dataset also contains climatic data provided by WorldClim, which were used to assess the influence of climatic variables on captive flamingo reproductive success globally. The WorldClim database averages 19 different climatic variables derived from monthly temperature and rainfall values at a 1 km spatial resolution for the period 1970-2000. Using geographic coordinates (latitude and longitude) we calculated several climatic metrics for each institution. </p> <p> </p> <p><strong>Description of the Dataset</strong></p> <p>One file is provided for each species (<em>P. minor, P. chilensis, P. roseus </em>and <em>P. ruber</em>) as a csv file. Each file contains the following 15 columns:</p> <ul> <li><strong>Institution Code: </strong>An anonymous code used to identify individual zoological institutions. </li> <li><strong>Country: </strong>The country where the institution is located.</li> <li><strong>Year: </strong>Current year (<em>t</em>).</li> <li><strong>Flock Size:</strong> Flock size in year <em>t.</em></li> <li><strong>Males: </strong>The number of males in the flock in year <em>t.</em> </li> <li><strong>Females:</strong> The number of females in the flock in year <em>t.</em></li> <li><strong>Unsexed:</strong> The number of unsexed individuals in the flock in year <em>t.</em></li> <li><strong>Proportion of Females: </strong>The proportion of the flock made up of female individuals in year <em>t</em>. </li> <li><strong>Proportion of Unsexed:</strong> The proportion of the flock made up of unsexed individuals in year <em>t.</em></li> <li><strong>Hatches:</strong> Number of birds hatched in year <em>t.</em></li> <li><strong>Proportion of Additions:</strong> The proportion of the flock in year <em>t</em> made up of additions from year <em>t-1</em> (not including new birds hatched into the flock).</li> <li><strong>MAP: </strong>Mean annual precipitation (mm).</li> <li><strong>MAT: </strong>Mean annual temperature (°C).</li> <li><strong>MAP Var: </strong>Mean annual variation in precipitation (MAP coefficient of variation).</li> <li><strong>MAT Var: </strong>Mean annual variation in temperature (MAT standard deviation).</li> </ul> <p>Note: Mean Annual Temperature (MAT) is provided by WorldClim as °C multiplied by 10, and similarly mean annual variation in temperature as MAT standard deviation multiplied by 100. In the corresponding publication, both were divided (by 10 and 100 respectively) prior to modelling to avoid confusion in the units used.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge and thank all Species360 member institutions for their continued support and data input. The research which data refers to was funded by the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. Additionally, S.Q.S. received funding from the International Max Planck Research School for Organismal Biology. The Species360 Conservation Science Alliance would like to thank their sponsors: the World Association of Zoos and Aquariums, Wildlife Reserves of Singapore, and Copenhagen Zoo. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data contained within ZIMS are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA). </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., Teare, J. A., Staerk, J.,Smeele, S. Q., Rose, P., Edell, R. H., King, C. E., Conrad, L., & Buckley, Y. M. (2023). Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide.<em> Zoo Biology</em>, 1–14. <a href="https://doi.org/10.1002/zoo.21753">https://doi.org/10.1002/zoo.21753</a></p> <p> </p> <p> </p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Influence of Framework n(Si)/n(Al) Ratio on the Nature of Cu Species in Cu-ZSM-5 for NH3-SCR-DeNOx
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP3_20220705_01_CW_Experimental</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP3_20220705_02_CW_Simulations</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>exp </strong>– experimental data, <strong>hyd </strong>– cw-EPR spectra related to hydrated state, <strong>dehyd </strong>– cw-EPR spectra related to the dehydrated state, <strong>sim </strong>– simulation data. <strong>Sys </strong>– copper species used for constructing the spin-Hamiltonian in EPR simulations. <strong>Cu-ZSM-5-com </strong>– commercial Cu-ZSM-5. <strong>Cu-ZSM-5-100</strong> – Cu-ZSM-5 synthesized at 100 °C. <strong>Cu-ZSM-5-120</strong> – Cu-ZSM-5 synthesized at 120 °C. <strong>Cu-ZSM-5-150</strong> – Cu-ZSM-5 synthesized at 150 °C.</li> </ul> </li> <li>– Cu-ZSM-5 synthesized at 170 °C. <ul> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers
<p>Dataset for the publication "Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers"</p>
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
Factors Influencing Aboveground Carbon Storage in Mixed Oak-Pine Forests: USDA FIA Data from Southeastern U.S. (2009-2019)
This study explores factors affecting aboveground carbon (AGC) storage in mixed oak-pine forests across the Southeastern United States. Utilizing USDA Forest Inventory and Analysis (FIA) data from 2009 to 2019, the research spans nine states: Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, Louisiana, and Virginia. Data processing in R included converting units to the metric system and calculating structural diversity using Shannon diversity indices. Climate data from the PRISM Climate Group were integrated with FIA data using longitude and latitude. The research aims to uncover how various factors influence AGC storage and contribute to informed forest management practices.
Influence of cockle bioturbation on microphytobenthic primary producers: habitat and density-dependent effect
The purpose of this study was to better understand the non-trophic interactions of benthic macrofauna, especially through their bioturbation activity, on microphytobenthos (MPB), which remain poorly studied and understood. For this purpose, a mesocosm experiment was performed, using the common cockles Cerastoderma edule. This species plays a key role in coastal ecosystem, especially impacting sediment characteristics and biogeochemistry through their bioturbation, including sediment reworking and bioirrigation. For the first time, bioturbation rates, biogeochemical fluxes at the sediment-water interface and MPB biomass and photosynthetic variables were measured at the same time. The effect of cockles density and sediment type were also investigated. This mesocosm experiment took place at the marine station of Arcachon. Experimental units consisted of PVC tubes filled with 2 types of sediment (medium sand or fine sand; samples in Arachon Bay and Baie des Veys in France). Then, 4 density of cockles were added in triplicate (0, 288, 720 and 1,297 ind. m-2), for each sediment type. The whole design was repeated twice, because in one of them luminophores were added at the top to measure sediment reworking, and could interfere with fluorescence measurement of MPB variables. All experimental units were incubated 6 days into a big tank, with artificial tide and light. Dissolved tracers were added into the natural seawater (close system) to measure bioirrigation rates of cockles. After 6 days, regarding experimental units with luminophores, they were slices and porewater was extracted to quantify bioturbation rates. Regarding units without luminophores, surface biomass and photosynthetic parameters of MPB were first measured in all unit with an IMAGING PAM. Then, oxygen and nutrient fluxes at the sediment water interface were measured using incubations. And finally, the first centimeter was sliced to measure total MPB biomass. This study demonstrated that bioturbation intens
Influences on charitable giving for conservation: Online survey data of 1,331 respondents across the US, August 2017
This dataset records survey data collected from an online panel of 1,331 anonymous, nationwide U.S respondents. Data collection was both initiated and completed in August 2017. Survey data is of two major types. The first type, information about respondents, includes (1) select background and demographic information; (2) a brief version of a social desirability scale, measured to test and control for potential bias related to socially desirable responding; and (3) a scale developed to measure moral inclusivity, conceptualized as the breadth of an individual’s moral community (i.e., to what extent do different types of entities “count,” in a moral sense). The second type of data records information about an experimental message manipulation featured in the survey. The dataset includes one variable indicating which of seven manipulated textual messages each respondent viewed, along with several variables used as metrics of response to the messages, including (1) attitudes toward the message; (2) hypothetical willingness to donate for the cause promoted in the message; (3) perceived moral salience of the message (i.e., the extent to which it was perceived as a matter of moral concern); (4) manipulation checks, to test whether the manipulated elements of the messages were perceived as intended, and (5) a donation set-up, in which individuals were given the option to donate between $0 and $5 for a conservation organization, from an incentive fee provided by the researchers.
RCS01 Recovery and relative influence of root, microbial, and structural properties of soil on physically sequestered carbon stocks in restored grassland at Konza Prairie
Managing soil to sequester C can help mitigate increasing CO2 in the atmosphere. To maximize this ecosystem service, more knowledge of factors influencing C sequestration is needed. The objectives of this study were to (i) quantify recovery of the roots, microbial biomass and composition, and soil structure across a chronosequence of grassland restorations and (ii) use a structural equation model to develop a data-based hypothesis on the relative influence of physical and biological soil properties on the soil C aggregate fraction diagnostic of sequestered C. We hypothesized measured variables would recover with restoration age. Belowground plant biomass and tissue quality (C/N ratio), soil microbial biomass C, phospholipid fatty acid (PLFA) concentrations, soil structure, and soil C stocks in the bulk soil and each aggregate fraction were quantified from a cultivated field, prairies restored for 1 to 35-yr (n = 6), and a never-cultivated (native) prairie. Root biomass, microbial biomass C, arbuscular mycorrhizal fungi (AMF) PLFA biomass across the chronosequence increase to resemble native prairie following 35 yr of restoration. Many aspects of soil structure (i.e., bulk density, proportional mass of aggre- gate fractions, and aggregate mean weighted diameter) and the distribution C among soil fractions, including C in the micro-within-macro aggregate fraction (sequestered C), also became representative of native prairie within 35 yr of restoration. Total soil C stock and physically protected C increased at a similar rate (23 and 27 g C m-2 yr-1) respectively, across the chronosequence. After 35 yr of restoration, 50% of the total C pool was physically protected. The structural equation modeling developed by these data hypothesizes that microbial biomass C and AMF biomass (microbial composition) have the strongest causal influence on physically protected C. This model needs to be tested using independent sites to achieve greater inference.
Factors influencing decomposition of leaves for five plant species at El Verde
We evaluated the influences of leaf quality, climate and microsite on the decomposition of leaves of five tropical tree species. Single-species litterbags were used to determine weight loss during the first three months of decomposition in the Luquillo Experimental Forest, Puerto Rico. Significant differences were found in decomposition rates among leaf species (Inga fagifolia < I. vera < Manilkara bidentata < C-roton poecilanthus << Sapium laurocerasus), but only S. laurocerasus differed significantly from the other species. Lignin had a suggestive negative correlation with leaf decomposition while carbon content and the lignin:N ratio were significantly correlated with mass loss. Content of N, P, Ca, and polyphenol were not significantly correlated with mass loss, but several of the litter quality variables were correlated with each other. Leaf species decomposed faster under canopies of their source trees than in a common plot where the source species were absent. Decomposition in two species in the Euphorbiaceae, S. laurocerasus and C. poecilanthus, was significantly affected by microsite. Leaching losses during the first three weeks were greater under source trees than in the common plot, and may have been associated with differences in canopy structure and throughfall. Differences in detrital communities, however, could have contributed to the differences in decomposition between microsites. Leaves of all species decomposed significantly faster in the wet than in the dry period (P = 0.001) despite little climatic variation in this subtropical wet forest type. This suggests that decomposition of tropical leaf litter might be sensitive to microclimatic changes on the forest floor resulting from either global climate change, or from natural or anthropogenic disturbances that open the canopy. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB
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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.