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Neural correlates of expectations-induced effects of caffeine intake on executive functions
<p><strong>ABSTRACT</strong></p> <p>Placebo effects (PE) are defined as the beneficial psychophysiological outcomes of an intervention that are not attributable to its inherent properties; PE thus follow from individuals’ expectations about the effects of the intervention. The present study aims aimed at examining how expectations influence neurocognitive processes.</p> <p>We will addressed this question by contrasting three double-blinded within-subjects experimental conditions in which participants are were given decaffeinated coffee, while being told they have had received caffeinated (condition i) or decaffeinated coffee (ii), and given caffeinated coffee while being told they have had received decaffeinated coffee (iii).</p> <p>After each of these three interventions, performance and electroencephalogram will bewas recorded at rest as well as during sustained attention Rapid Visual Information Processing task (RVIP) and a Go/NoGo motor inhibitory control task.</p> <p> We first aimed to confirm previous findings for caffeine-induced enhancement on these executive components and on their associated electrophysiological indexes (attentional P3 component, response conflict N2 and inhibition P3 components (ii vs iii contrast); and then to test the hypotheses that expectations also induce these effects (i vs ii), although with a weaker amplitude (i vs iii).</p> <p>Related to the behavioral findings, wWe didn’t not confirm any of our hypotheses for behavioral improvement induced by caffeine intakeon either of the investigate tasks’ measures. Regarding the neurophysiological findingsAt the electrophysiological level, however, we confirmed that caffeine effects on increased the attentional P3 and inhibition P3 components amplitude, but not on the response conflict N2 component. Additionally, wWe dodid not confirm provide evidence that expectations do not influence any of the investigate electrophysiological indexeices. Finally, we confirm that that expectations effects are smaller compared to caffeine effects but only for the Global Field Power parameter related to the attentional P3 component.</p> <p>only for one of the investigated the attentional P3 component’s parameters, and that this effect was smaller than that of</p> <p>We conclude that Hence, previously identified caffeine effects at the behavioral level may have been overestimated and that if while expectations effects have any no influence on sustained attention and inhibitory control, they are small. XXCaffeine effects at the electrophysiological level indicate that it tends to modulate brain areas underlying attentional mechanisms in both RVIP and Go/NoGo tasks rather than being specific to inhibitory control processes.</p>
Dataset from: "Reward expectation facilitates context learning and attentional guidance in visual search"
<p>Dataset for Bergmann N, Koch D, Schubö A (2019). Reward expectation facilitates context learning and attentional guidance in visual search, <em>Journal of Vision</em>, 19(3). <a href="https://doi.org/10.1167/19.3.10">https://doi.org/10.1167/19.3.10</a></p>
Information Filtering in Electronic Networks of Practice: An fMRI Investigation of Expectation [Dis]confirmation
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Resilient farm demographics withstand, adapt, or transform in the face of competitive pressure, technological change, and the expected lifestyles of future generations
<p>Farm demographics has been recognized as an important driver of structural change in European agriculture. Focus groups and computer simulations on farm demographic change were used to better understand its role for the case study regions of the Altmark in the eastern part of Germany and Flanders in the northern part of Belgium. According to these analyses, many potential agricultural entrants are deterred by what they view as a poor quality of life that farming offers. This applies to farm successors as well as hired workers. For higher attractiveness of agriculture, policy objectives should address the social image of farming as well as revitalize rural areas. Increasingly critical is the demand for skilled hired labour. However, policies dealing with farm demographic change ignore these needs and focus almost exclusively on farm succession. Particularly, the direct payment system, including additional support for small farms and young farmers, must be re-evaluated for its effectiveness. The analyses provide evidence that this system constrains European agricultural development more than assists it; ultimately preventing farms from adapting and transforming.</p>
MINIATURA 6 Housing decisions, behavioral aspects of choices, price expectations and anchoring effect - Polsh case study
<p>The data was created as a result of a survey conducted in accordance with the guidelines: - the survey questionnaire consisted of approximately 30 questions and a form, - the surveyed population was defined as 1,000 households living in a large Polish city (over 450,000 inhabitants), quota selection based on the number of city inhabitants, - CAWI method (online), - completion date: 1 week. The survey was parameterized. Part of the sample is a control trial, part is an experimental trial.</p><p>Dane powstały w wyniku przeprowadzonej ankiety zgodnie z wytycznymi: - kwestionariusz badania składał się z ok. 30 pytań oraz metryczki, - badana zbiorowość określono na 1000 gospodarstw domowych zamieszkałych w dużym mieście Polski (powyżej 450 tys. ludności), dobór kwotowy na podstawie liczby mieszkańców miast, - badanie metodą CAWI (on-line), - termin realizacji 1 tydzień. Ankieta byłą sparametryzowana. Część próby stanowi próba kontrolna, część próba eksperymentalna. </p>
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</p>
End-user's survey results on needs and expectations for next- generation Energy Performance Certificates (H2020 X-tendo project)
<p>The SPSS data file consists of survey data from the X-tendo project on the end-user needs and expectations from next-generation energy performance certificates.</p>
Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>40-lifetables.csv</strong></p> <p>Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.</p> <p><strong>30-lt_input.csv</strong></p> <p>Life table input data.</p> <ul> <li>`id`: unique row identifier</li> <li>`region_iso`: iso3166-2 region codes</li> <li>`sex`: Male, Female, Total</li> <li>`year`: iso year</li> <li>`age_start`: start of age group</li> <li>`age_width`: width of age group, Inf for age_start 100, otherwise 1</li> <li>`nweeks_year`: number of weeks in that year, 52 or 53</li> <li>`death_total`: number of deaths by any cause</li> <li>`population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)</li> <li>`death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)</li> <li>`death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_source`: source of the all-cause death data</li> <li> <p>`death_total_prop_q1`: observed proportion of deaths in first quarter of year</p> </li> <li> <p>`death_total_prop_q2`: observed proportion of deaths in second quarter of year</p> </li> <li> <p>`death_total_prop_q3`: observed proportion of deaths in third quarter of year</p> </li> <li> <p>`death_total_prop_q4`: observed proportion of deaths in fourth quarter of year</p> </li> <li> <p>`death_expected_prop_q1`: expected proportion of deaths in first quarter of year</p> </li> <li> <p>`death_expected_prop_q2`: expected proportion of deaths in second quarter of year</p> </li> <li> <p>`death_expected_prop_q3`: expected proportion of deaths in third quarter of year</p> </li> <li> <p>`death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year</p> </li> <li>`population_midyear`: midyear population (July 1st)</li> <li>`population_source`: source of the population count/exposure data</li> <li>`death_covid`: number of deaths due to covid</li> <li>`death_covid_date`: number of deaths due to covid as of <date></li> <li>`death_covid_nageraw`: the number of age groups in the covid input data</li> <li>`ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year</li> <li>`ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database</li> <li>`nmx_hmd_estimate`: death rate estimates from the Human Mortality Database</li> <li>`nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019</li> </ul> <p><em>Deaths</em></p> <ul> <li>source: <ul> <li>STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)</li> <li>ONS for GB-EAW pre 2020</li> <li>CDC for US pre 2020</li> </ul> </li> <li>STMF: <ul> <li>harmonized to single ages via pclm</li> <li>pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110</li> <li>smoothing parameters estimated via BIC grid search seperately for every pclm iteration</li> <li>last age group set to [110,111)</li> <li>ages 100:110+ are then summed into 100+ to be consistent with mid-year population information</li> <li>deaths in unknown weeks are considered; deaths in unknown ages are not considered</li> </ul> </li> <li>ONS: <ul> <li>data already in single ages</li> <li>ages 100:105+ are summed into 100+ to be consistent with mid-year population information</li> <li>PCLM smoothing applied to for consistency reasons</li> </ul> </li> <li>CDC: <ul> <li>The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data</li> </ul> </li> </ul> <p><em>Population</em></p> <ul> <li>source: <ul> <li>for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019</li> <li>for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100</li> </ul> </li> <li>mid-year population <ul> <li>mid-year population translated into exposures: <ul> <li>if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates</li> <li>if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.</li> </ul> </li> </ul> </li> </ul> <p><em>COVID deaths</em></p> <ul> <li>source: COVerAGE-DB (https://osf.io/mpwjq/)</li> <li>the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total</li> </ul> <p><em>External life expectancy estimates</em></p> <ul> <li>source: <ul> <li>World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019</li> <li>Human Mortality Database (https://mortality.org/), single year and age tables</li> </ul> </li> </ul>
Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>50-e0diffT.csv</strong></p> <p>Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019.</p> <p><strong>51-arriagaT.csv</strong></p> <p>Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age.</p> <p><strong>52-sexdiff.csv</strong></p> <p>Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes.</p> <p><strong>53-e0diffcodT.csv</strong></p> <p>Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p> <p><strong>55-vaxe0.csv</strong></p> <p>Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE.</p> <p><strong>54-tab_arriaga.csv</strong></p> <p>Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p>
Historical Reconstruction Dataset of Hourly Expected On-Shore Wind Generation in Japan
<h2>Description</h2> <p>This is a historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing the spatio-temporal impact of on-shore wind in Japan.</p> <p>The dataset consists of a set of <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> files with yearly archives of reconstruction results from 1958 to 2012; hourly expected on-shore wind power potential in Japan with a spatial resolution of approximately 5 km mesh has been reconstructed from the numerical weather model reanalysis results. The expected per-unit output values at each location were calibrated using a nonparametric machine learning model that learns statistical relationships between spatial/meteorological features of target locations and actual wind farm outputs.</p> <p>A convenient way to handle this dataset would be to use a tool for manipulating netCDF files, such as <a href="https://code.mpimet.mpg.de/projects/cdo">CDO: Climate Data Operators</a>.</p> <h2>Associated Publication</h2> <ul> <li>Yu Fujimoto, Masamichi Ohba, Yujiro Tanno, Daisuke Nohara, Yuki Kanno, Akihisa Kaneko, Yasuhiro Hayashi, Yuki Itoda, and Wataru Wayama, "Historical Reconstruction Dataset of Hourly Expected Wind Generation Based on Dynamically Downscaled Atmospheric Reanalysis for Assessing Spatio-Temporal Impact of On-Shore Wind in Japan", <em>Big Earth Data</em>, doi: 10.1080/20964471.2024.2374044 </li> </ul> <h2>Version history</h2> <ul> <li>Ver. 1.0: Released.</li> <li>Ver. 1.1: The preprocessing of the source information used for dataset preparation has changed.</li> <li>Ver. 1.2: The hyperparameter tuning scheme for the post-processing model has changed.</li> </ul>
(Un)expected similarity of the temporary adhesive systems of marine, brackish, and freshwater flatworms
<p>This repository contains</p> <ul> <li>a container with 441 single *.tiff files from a serial-block-face-imaging experiment of a Macrostomum lignano tail plate. The images were aligned with Dragonfly v. 2021.1 (ORS). This dataset was used to reconstruct the 3D model of a Macrostomum lignano adhesive organ. [Macrostomum_lignano_tailplate_SBFI.tar.gz]</li> <li>The raw Illumina PE150 reads from Macrostomum tuba [Mtub_1_R2_HTY2GBGXC_1_107969_TGTTGATCCTATGTTA.fastq.gz, Mtub_1_R1_HTY2GBGXC_1_107969_TGTTGATCCTATGTTA.fastq.gz]</li> <li>The assembled (Trinity v. 2.11.0 ) transcriptome of Macrostomum tuba including annotated fasta headers using Trinotate (v. 3.2.0)</li> </ul>
AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea
<p>The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria. </p> <p> </p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980].
Using a Hybrid Kano-Importance Questionnaire in the Acquisition of Data Related to Students' Expectations from Online Educational Platforms
<p>This dataset contains the data collected for the assessment of the quality attributes of a new online educational platform. The questionnaire used for data collection the Kano methodology and was designed as a hybrid Kano-importance questionnaire. The purpose of this data collection consists of the analysis of the students’ expectations regarding the features proposed for a new online educational platform. This analysis facilitates the identification of student needs during times of COVID-19 pandemic and post-pandemic times, while a transition to an online educational system was used throughout the world. </p>
Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019).
Assessing the value of monitoring to biological inference and expected management performance for a European goose population
<p>1. Informed conservation and management of wildlife require sufficient monitoring to understand population dynamics and to direct conservation actions. Because resources available for monitoring are limited, conservation practitioners must strive to make monitoring as cost-effective as possible.</p> <p>2. Our focus was on assessing the value of monitoring to the adaptive harvest management (AHM) program for pink-footed geese (Anser brachyrhynchus). We conducted a retrospective analysis to assess the costs and benefits of a capture-mark-resight (CMR) program, a productivity survey, and biannual population censuses. Using all available data, we fit an integrated population model (IPM) and assumed that inference derived from it represented the benchmark against which reduced monitoring was to be judged. We then fit IPMs to reduced sets of monitoring data and compared their estimates of demographic parameters and expected management performance against the benchmark IPM.</p> <p>3. Costs and the precision and accuracy of key demographic parameters decreased with the elimination of monitoring data. Eliminating the CMR program, while maintaining other monitoring instruments, resulted in the greatest cost savings, usually with small effects on inferential reliability. Productivity surveys were also expensive and some reduction in survey effort may be warranted. The biannual censuses were inexpensive and generally increased inferential reliability.</p> <p>4. The expected performance of AHM strategies was surprisingly robust to a loss of monitoring data. We attribute this result to explicit consideration of parametric uncertainty in harvest-strategy optimization and the fact that a broad range of population sizes is acceptable to stakeholders.</p> <p>5. Synthesis and applications: Our study suggests that existing or potential monitoring instruments for wildlife populations should be scrutinized as to their cost-effectiveness for improving biological inference and management performance. Using Svalbard pink-footed geese as a case study, we show that the loss of some existing monitoring instruments may not be as adverse as commonly assumed if data are jointly analyzed in an integrated population model. Finally, regardless of the monitoring data available, we suggest that conservation strategies that explicitly account for uncertainty in demography are more likely to be successful than those that do not<span>.</span></p>
A questionnaire survey of expected characteristics of i-voting systems among students and graduates of agricultural colleges
<p>In order to find out the opinions on the transparency of remote electronic voting, a questionnaire survey was conducted among students and graduates of Czech universities with agricultural specialization. We assume that these are people with higher technical literacy who may be potential users of i-voting systems in farms in the near future. For the composition of the questions, the framework from Agbesi et al. (2023) was used to investigate the dimensions of transparency for Internet voting, supplemented with a few specific questions. Agbesi et al. (2023) identify five core dimensions, namely information accessibility, clarity, monitoring and verifiability, corrective action and testing, these dimensions influence the perception of transparency which in turn influences trust in the overall system. The anonymous questionnaire survey was conducted online via the Dotaznik.czu.cz platform operated by the Czech University of Life Sciences Prague. The invitation to participate in the survey was extended primarily to Czech students and graduates of agricultural universities. The questionnaire survey was conducted from 24 October 2023 to 12 May 2024. The questionnaire was freely accessible on the platform, therefore some respondents may be outside the target population within the limitations of the research. Participants were shown all information including consent to data processing on the homepage of the survey.</p> <p>Respondents answered on a seven-point Likert scale ranging from Strongly Disagree (0) to Strongly Agree (6) to statements within the five defined dimensions. A total of 177 individuals were recorded as completing the questionnaire. A total of 108 questionnaires were completed in full. Of these, 8 more questionnaires were removed because the control question "This question is not part of the survey and just helps us to detect bots and automated scripts. To confirm that you are a human, please choose 'Strongly agree' here" was answered differently than Strongly agree. Of the 100 responses examined, 64 were male, 35 were female, and 1 respondent did not indicate their gender. 72 respondents are aged 18-30, 18 aged 31-40, 5 aged 41-50, 2 aged 51-60 and 3 aged 61-70. 72 respondents have completed secondary education, 10 have a Bachelor's degree, 12 have a Master's degree and 6 have a PhD.</p>
PERCEIVE - What do you need and expect?
<p>This dataset include 5 questionnaire prepared within the PERCEIVE project to identify needs and requirements to be used in the development of Tools, Services and Prototypes (www.perceive-horizon.eu)</p>
Anonymized Responses to the Student Expectation of Learning Analytics Questionnaire (SELAQ) in 2022 and 2023
<p>Contains responses of 566 bachelor students to the Student Expectation of Learning Analytics Questionnaire (SELAQ) conducted at Bochum University of Applied Sciences in Germany in fall 2022 and summer 2023. The Questionnaire consists of 12 statements (in the dataset: enumerated from 1-12) which are evaluated by students regarding both their desire and their expectation (in the dataset: D and E). This results in 24 items (in the dataset: 1D, 1E, 2D, 2E, ..., 12D, 12E). Each item is evaluated by the students on a Likert-scale from 1 (strongly disagree) to 7 (strongly agree). Additionally, the students' study track (in the dataset: Study_Track) and their current semester (in the dataset: Semester) were also recorded. The questionnaires were carried out in paper form at the beginning of lectures. Statements 1, 2, 3, 5 and 6 deal with ethical and privacy expectations, while statements 4, 7, 8, 9, 10, 11 and 12 deal with service feature expectations regarding learning analytics. For a text version of the items, please refer to the paper related to this dataset (DOI 10.1145/3636555.3636923), the additional descriptions below or the attached description file.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.