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887 results for “Retention”
Level and spatial pattern of overstory retention impose tradeoffs for regenerating and retained trees
<p>Variable retention (VR) has been adopted globally as an alternative to more intensive forms of regeneration harvest. By retaining live trees within harvest units, VR seeks balance among the commodity, ecological, and aesthetic values of managed forests. Achieving these multiple, often competing objectives requires an understanding of how level and spatial pattern of retention shape the abundance, growth, and mortality of regenerating and retained trees. Using long-term (18-19 yr) data from a regional-scale VR experiment, we explore the individual and interactive effects of retention level (15% vs. 40% of initial basal area) and pattern (dispersed vs. aggregated) on the post-harvest dynamics of forests of differing structure and seral composition.</p> <p>Level and pattern of retention imposed tradeoffs for the density and growth of regenerating trees (>0.1 m tall, <5 cm dbh) and ingrowth (trees attaining 5 cm during the study). Greater retention led to greater density of late-seral regeneration, but lower density of early-seral ingrowth, and slower growth of late-seral ingrowth. Dispersed retention enhanced the density of early- and late-seral regeneration (compared to aggregated treatments), but reduced the growth of early-seral ingrowth. We also observed tradeoffs for retained trees. Lower retention enhanced the growth of smaller trees (<25 cm dbh)—particularly in dispersed settings—but reduced the survival of larger trees, which were more susceptible to windthrow. Greater retention reduced the growth, but enhanced the survival of smaller trees. Pattern imposed similar tradeoffs, with dispersed retention enhancing growth, but reducing survival of small trees. Finally, level and pattern resulted in tradeoffs for productivity of regenerating vs. retained-tree cohorts. Ingrowth productivity was greater at lower retention and in aggregated treatments; retained-tree productivity was greater at higher retention and in dispersed treatments.</p> <p>Our results provide a unique, long-term perspective on the sensitivity of tree regeneration, growth, and mortality to key structural elements of VR systems. Strong responses to level and pattern of retention produce tradeoffs for different ecological or resource objectives. Balancing these objectives may require the combined use of aggregates, dispersed retention, and clearings, to mimic the spatial heterogeneity of habitats, physical structures, and resource conditions that are produced by natural disturbances.</p>
Dataset: "Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification"
<p>The SQLite database contains the pre-computed tandem mass spectra (MS2) and retention order scores used for the experiments in the publication: "<a href="https://doi.org/10.1093/bioinformatics/btaa998">Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification</a>" by Bach et al. (2020).</p> <p>A detailed description of the database structure is given in the 'README.md' and can also be found in the <a href="https://github.com/aalto-ics-kepaco/msms_rt_score_integration/tree/master/data">code-repository associated with the publication</a>. The database layout is illustrated in the 'db_layout.png' file.</p> <p>The SQLite file 'ms_and_rt_score_DB_bach_etal_2020.db.gz' is compressed using <a href="https://en.wikipedia.org/wiki/Gzip">gzip</a>.</p>
Research data on pavement particle retention for D6.5
<p>We tested the hypothesis that porous asphalt would be able to retain more dust (including microplastics) in its pore network than conventional dense asphalt. Hence, the collection of pore dust was done on the four sections of the carousel to compare among pavements and according to the number of cycles. This is because the different sections will experience the same weather conditions, such as precipitation, wind speeds or dust deposits.</p><p>Dust was collected each time the carousel stopped (at 0, 20,000, 50,000, 100,000, 200,000, 500,000, 750,000 and 1,000,000 cycles) using a specially designed vacuum cleaner. This equipment was used thanks to a collaboration with CSIC. Based on the laboratory results, this equipment is able to recover the particles not only on the surface but also those inside accessible pores. The area vacuumed per section was initially 30 x 5 cm2, then 30 x 15 cm2 to increase the sample size. Samples were collected both in the wheel track area and in an outer ring separated from the circulated zone. The samples are weighted and referenced to the sampling area.</p><p>Collected dust samples were stored in individual containers and sent to the laboratory of the UC to process them by thermogravimetric analysis (TGA) and SEM for their microplastics contents. </p>
Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
Structurally well-defined anti-π-allyliridium complexes catalyze Z-retentive asymmetric allylic alkylation of oxindoles
<p>Uploaded herein are all the output files of the computational studies on Ir-catalyzed <em>Z</em>-retentive asymmetric allylic alkylation of oxindoles.</p> <p>Some Gaussian checkpoint files, summary of Mayer bond order calculations (as plain txt files), and the output and checkpoint files of the calculations on a known Ir-complex (JACS, 2017, 3606), are included in this update.</p>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
<p>1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m<sup>2</sup>) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.</p>
Fig. 1 in Comparison of parasitoid retention on yellow sticky card traps
Fig. 1. Percentage of parasitoid escape afer 72 h on Alpha Scents folding yellow card traps and Pherocon AM no-bait traps.
Fig. 2 in Comparison of parasitoid retention on yellow sticky card traps
Fig. 2. Yellow sticky card containing pushpins marking the location of captured parasitoid wasps. Pushpins were placed approximately 2 to 3 mm from each captured wasp and their movement on the sticky card can be observed with the wasp's varying distance from pushpins.
TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING
<p>In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.</p>
Dataset: Wood retention at inclined bar screens: effect of wood characteristics on backwater rise and bedload transport
<p>This dataset includes flow and bedload transport measurements and wood accumulation characteristics of flume experiments conducted at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
Figure 1 in Embryo retention, character optimization, and the origin of the extra-embryonic membranes of the amniotic egg
Figure 1. Sarcopterygian phylogeny showing an optimization of embryo retention (character 1), as previously advocated by Laurin and Girondot (1999). The only modification is that all terminal taxa are in the present tree, instead of collapsing Monotremata and Theria into Mammalia, to better match the character distribution shown in Table I, and that Actinistia is coded as unknown (as shown by the absence of a data box below that taxon).
Figure 3 in Embryo retention, character optimization, and the origin of the extra-embryonic membranes of the amniotic egg
Figure 3. Sarcopterygian phylogeny showing an optimization of the developmental stage at oviposition (character 2, with ordered states). This optimization suggests that the ancestral amniote laid its eggs at the gastrula developmental stage (equivalent to absence of extended embryo retention). If the character is left unordered, the ancestral condition for amniotes is to lay eggs in the post-neurula embryonic stage (equivalent to presence of extended embryo retention).
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50). in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50).
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.
Data from: Assessing the reproductive consequences of mate retention and pair bond duration in Thorn-tailed Rayadito (Aphrastura spinicauda), a short-lived, socially monogamous Neotropical bird
<p><strong>Description for "PairingData_Aspinicauda.xlsx" file.</strong></p> <p>Data from: Assessing the reproductive consequences of mate retention and pair bond duration in Thorn-tailed Rayadito (Aphrastura spinicauda), a short-lived, socially monogamous Neotropical bird<br> MS Reference Number: IBIS-2022-OA-113.R2<br> Article DOI: 10.1111/ibi.13183</p> <p>Please address questions to:</p> <p>Esteban Botero D.<br> Guest Scientist<br> Max Planck Institute for Ornithology<br> Dep. Behavioural Ecology and Evolutionary Genetics<br> Eberhard-Gwinner-Str. 8<br> 82319 Seewiesen, Germany<br> Telephone: +49 8157 932453<br> http://www.orn.mpg.de/en<br> e-mail: eboterod@gmail.com; ebotero@orn.mpg.de</p> <p>=====================================================================================<br> =====================================================================================</p> <p><br> General information:</p> <p>The whole dataset contains breeding data collected from a population of the furnariid Thorn-tailed rayadito (Aphrastura spinicauda) in north-central Chile (Fray Jorge National Park; 30º38’S, 71º40’W). These data were collected during 2009–2017 as part of a long-term study on the breeding biology of rayaditos. In this study, data were used to evaluate the consequences of mate replacement versus mate retention using 243 breeding attempts made by 159 different breeding pairs. This, in the end, allowed to test whether successive remating conferred reproductive benefits to reunited pairs.</p> <p>The data set is comprised by an Excel file (three spreadsheets) that are explained below.</p> <p>*************************************************************************************</p> <p>Excel file "PairingData_Aspinicauda.xlsx" (created 11-01-2023)</p> <p><br> ********** Spreadsheet "1. AllPairs" **********<br> This spreadsheet contains information from all breeding attempts monitored during the study (n = 243). Each row correspond to a unique breeding attempt. The ring number is used as an ID for each individual. The matrix includes information regarding individual and pair identification, age, previous breeding status (whether an individual is a widow or a divorcee), current pairing status (whether is a newly formed pair or a reunited pair), number of seasons breeding together for each pair, confidence on pairing information for each pair (high: there was absolute confidence on the previous breeding status of both members of a breeding pair; low: when information on previous breeding status was missing for at least one of the members of a pair), and measures of reproductive success (laying day, clutch size, umber of fledglings produced). This dataset can be saved as a *.txt file so that it can be imported into R (R Core Team 2020).</p> <p>The matrix contains the following variables:</p> <p>VARIABLE DESCRIPTION</p> <p>Year Sampling year.<br> Box Nestbox code.<br> FID ID for the breeding female.<br> FMAge Age for each breeding female (yearling: 1; adult: 2).<br> SocMID ID for the breeding male (social father of the clutch).<br> SocMaAge Age for each breeding male.<br> PairID ID for the breeding pair. This is for indexing purposes.<br> FPaSta Previous breeding status of the female (Wid: widow; Div: divorcee; Reu: reunited).<br> MPaSta Previous breeding status of the male (Wid: widow; Div: divorcee; Reu: reunited).<br> PairSta Pairing status for the focal breeding pair (New: newly formed; Reunited: reunited).<br> PairSea No. of seasons breeding together for each pair.<br> Certainty Certainty on previous breeding status (High or Low; see explanation above).<br> LayingD Laying date (number of days in relation to date of first egg in the population).<br> ClutchS Clutch size.<br> NoFle Number of fledging produced.</p> <p><br> ********** Spreadsheet "2. WidowFBre" **********<br> This spreadsheet contains breeding information for females that were monitored in the years before and after mate loss.</p> <p>The matrix contains the following variables:</p> <p>VARIABLE DESCRIPTION</p> <p>Year Sampling year.<br> Box Nestbox code.<br> FID ID for the breeding female.<br> LayingD Laying date during year after mate loss.<br> ClutchS Clutch size during year after mate loss.<br> NoFle Number of fledging produced during year after mate loss.<br> FPaSta Previous breeding status of the female (Wid: widow; Div: divorcee; Reu: reunited).<br> LayingD_x.1 Laying date during year before mate loss (year x-1).<br> ClutchS_x.1 Clutch size during year before mate loss (year x-1).<br> NoFle_x.1 Number of fledging produced during year before mate loss (year x-1).</p> <p><br> ********** Spreadsheet "3. WidowMBre" **********<br> This spreadsheet contains breeding information for males that were monitored in the years before and after mate loss.</p> <p>The matrix contains the following variables:</p> <p>VARIABLE DESCRIPTION</p> <p>Year Sampling year.<br> Box Nestbox code.<br> SocMID ID for the breeding male.<br> LayingD Laying date during year after mate loss.<br> NoFle Number of fledging produced during year after mate loss.<br> MPaSta Previous breeding status of the female (Wid: widow; Div: divorcee; Reu: reunited).<br> LayingD_x.1 Laying date during year before mate loss (year x-1).<br> NoFle_x.1 Number of fledging produced during year before mate loss (year x-1).</p> <p>*************************************************************************************</p> <p><br> =====================================================================================</p> <p><br> Methodological information (for more details, please see the related manuscript):</p> <p>A total of 101–157 nestboxes were installed in Fray Jorge since 2007, and are monitored annually during September–December. We gathered data on reproductive phenology and productivity during 2008–2017 for all nestbox occupants. Nestboxes were initially visited every 3–5 days to detect nest building. Once nestboxes were occupied, we increased the frequency of visits to record data on laying date, clutch size, and the number of hatchlings and fledglings produced (see more details in Botero-Delgadillo et al. 2017). We captured and marked breeding adults and nestlings with numbered aluminium rings when nestlings were 12–14 days old. Additionally, we used mist nets to capture adult birds breeding in natural cavities in our study site. A total of 248 adults (132 females, 116 males) and 730 nestlings were marked. For all nests that were monitored, we marked ~90% of all breeding adults every year.</p> <p>We used data from a total of 243 breeding attempts made by 159 breeding pairs captured during 2009–2017 to describe mating patterns in the study population, including: (i) the duration of social bonds for all breeding pairs formed during the study; (ii) the proportion of newly formed and remated pairs found during the entire study period and during each year; and (iii) the proportion of divorce versus mate loss causing pair dissolution.</p> <p>The consequences of mate retention and successive remating were evaluated by performing mixed-effects models in the lme4 package (Bates et al. 2015) in the free software R 4.0.2 (R Core Team 2020). To assess whether reproductive success was higher for remated pairs than for newly formed pairs, we tested for the effects of pairing status (newly formed vs. remated) on measures of breeding productivity. Linear models were fit for laying date, clutch size, and number of fledglings produced. To control for between-season variation in reproductive output, we calculated Z-scores for all numeric response variables using the mean and standard deviation for each year. All models included age class of both members of a breeding pair as covariates (yearling vs. adult), and female, male and pair ID as random intercepts. First, we performed analyses on the complete set of 243 breeding attempts, and subsequently repeated the analyses on a reduced subset of data that only contained pairs whose previous pairing status was known with certainty (n = 159). This allowed to evaluate potential bias in our results, given that the complete dataset included pairs misclassified as “newly formed”, because the previous pairing status of older individuals that we captured for the first time is unknown.</p> <p>We also investigated whether individuals experienced reduced reproductive success after mate replacement. To test this, we compared breeding productivity of individuals in the years before and after mate loss. We focused the analysis on widowed birds, as the frequency of divorced individuals was low in the study population. We used linear mixed-effects models that included data on laying date, clutch size, and number of fledglings produced as response variables. Each sex was tested separately, with clutch size being evaluated only for females. We included the breeding season as predictor (year x vs. x-1), and entered individual ID as a random intercept.</p> <p>Lastly, to evaluate whether successive remating influenced reproductive success, we used data on pairs that bred more than once together during the study (n = 132). Linear mixed-effects models were fitted to assess the effect of the number of seasons breeding together on laying date, clutch size, and number of fledglings produced. Between-season effects were controlled as described above, while the number of seasons breeding together (range: 1–6) was introduced as predictor. Given the skewed distribution of the number of seasons breeding together in this dataset (one = 36%; two = 36%; three = 17%; four = 8%; five = 2%; six = 1%), and the possibility that its effect on reproductive success might not be linear, a dummy variable indicating whether an observation belonged to the first breeding attempt (first attempt vs. after-first attempt) was also entered as predictor. Models included female and male age class as covariates, and pair ID as a random intercept.</p> <p><strong>References:</strong></p> <p>Botero-Delgadillo, E., Quirici, V., Poblete, Y., Cuevas, E., Kuhn, S., Girg, A., Teltscher, K., Poulin, E., Kempenaers, B., & Vásquez, R. A. (2017). Variation in fine-scale genetic structure and local dispersal patterns between peripheral populations of a South American passerine bird. Ecology and Evolution, 7(20), 8363–8378. https://doi.org/10.1002/ece3.3342</p> <p>Bates, D., Maechler, M., Bolker, B., & Walker, S. 2015. Fitting linear mixed-effects models using lme4. J. Stat. Soft. 67: 1–48.</p> <p>R Core Team. (2020). R: a language and environment for statistical computing, version 4.0.2. R Foundation for Statistical Computing, Vienna, Austria, http://www.R.project.org</p> <p><br> =====================================================================================<br> =====================================================================================</p>
Dataset accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes"
<p>The dataset in this repository is accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes" (in Microplastics and Nanoplastics, 2023, submitted 09.06.2023)</p> <p>The repository contains the raw image files of all sample filters which were scanned using the fluorescence imaging system ChemiDoc and used to analyse the infiltration behaviour of microplastic polystyrene in the manuscript. In addition, we provide the resulting data from the particle identification and geometric analysis which were derived from the raw data using ImageJ in tabular excel format. The data is structured in folders following the naming of the columns from the manuscript.</p>
Dataset for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"
<p>Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"</p> <p><strong>CONTEXT: </strong>Soybean (<em>Glycine max</em> (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. </p> <p><strong>OBJECTIVE</strong>: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. </p> <p><strong>METHODS</strong>: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. </p> <p><strong>RESULTS: </strong>Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (V<sub>cmax25</sub>) by 42%.</p> <p><strong>CONCLUSIONS:</strong> The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity.</p> <p><strong>IMPLICATIONS:</strong> These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy.</p> <p><strong>Code:</strong></p> <p><strong>1_phenocam.rmd: </strong>Code to download Phenocam data and identify phenophase transition dates.</p> <p><strong>2_Daily_CO2_Water_Fluxes.Rmd: </strong>Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2).</p> <p><strong>3_Inferring_LAI_and_Height.Rmd: </strong>Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework.</p> <p><strong>4_Big_Leaf.Rmd: </strong>Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4).</p> <p><strong>4_Data_Dictionary_Vairables:</strong> Code to identify the data dictionary variables. </p> <p> </p>
Data for "Gender and retention patterns among U.S. faculty"
<p>This repository contains the data for the paper "Gender and retention patterns among U.S. faculty".</p>
The neural correlates of embodied L2 learning Does embodied L2 verb learning affect representation and retention?
<p>We investigated how naturalistic actions in a highly immersive, multimodal, interactive 3D virtual reality (VR) environment may enhance word encoding by recording EEG in a pre/post-test learning paradigm. While behavior data has shown that coupling word encoding with gestures congruent with word meaning enhances learning, the neural underpinnings of this effect have yet to be elucidated. We coupled EEG recording with VR to examine whether “embodied learning” improves learning and creates linguistic representations that produce greater motor resonance. Participants learned action verbs in an L2 in two different conditions: Specific action (observing and performing congruent actions on virtual objects) and Pointing (observing actions and pointing to virtual objects). Pre and post-training participants performed a Match-mismatch task as we measured EEG (variation in the N400 response as a function of match between observed actions and auditory verbs) and a Passive listening task while we measured motor activation (mu (8-13 Hz) and beta band (13-30Hz) desynchronization during auditory verb processing) during verb processing. Contrary to our expectations, post-training results revealed neither semantic nor motor effects in either group when considered independently of learning success. Behavioral results showed both groups learned the verbs, but also a great deal of variability in learning success. When considering performance, Low performance learners showed no semantic effect and High performance learners exhibited an N400 effect for Mismatch vs Match trails post-training, independent of the type of learning. Taken as a whole, our results suggest that embodied processes can play an important role in L2 learning.</p>
Level and spatial pattern of overstory retention impose tradeoffs for regenerating and retained trees
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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.