The dataset contains 9 citations graphs obtained from the Web of Science. 3 graphs were generated for each of 3 research areas, including SWF (Switched-capacitor Filters), NLP (Natural Language Processing), and RTT (Real-Time Tracking).The study analyzes two facets: the contribution ofdifferent types of groups on the impact research ideas make on other groups, and the importance groups have in linking sets of groups with each other.

Data supporting the paper "Forest structure and understory functional diversity at multiple scales: the importance of median tree height", De Benedictis et al. and allow reproducibility. The R scripts used can be found in the linked GitHub repository.

MAPE and PB statistics for IBFI compared with other imputation methods (mean, median, mode, PMM, and Hotdeck) for 20% missingness of type MAR and all parameters tested (RN, TH, TC, RH, and PR).

The overall view of retail investors surveyed in the United Kingdom saw the action of viewing and managing app notifications to be important, receiving an average score of **** from a maximum possible value of ****. The ability to trade cryptocurrency received an average score of ***, showing the ability to trade shares was ranked more highly among retail investors, receiving an average score of ***.

Summary of Significance analysis and median values of the topological features for the positive genes and negative genes.

Variable importance and median concentrations (pg/ml) of 7 selected biomarkers (N = 262).

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

The largest share of respondents, or ** percent of the polled in Russia in 2020, reported that the need for big data usage in the company, was closely related to its size and type. Every tenth respondent stated that investment worth in big data solutions exceeded profits volume yielded by thereof for the mid-size IT companies.

In a November 2021 survey conducted in the United States, 85 percent of respondents stated that the overall average star rating of a business was one of the most important considerations when judging a local business based on reviews. The business having a higher than average star rating than other businesses was also a very important factor, with 76 percent of respondents stating that this was one of the most important aspects.

The consistent importance of geography (as ranked by applicants on a scale from 1 [not important] to 5 [extremely important]) and the growing number of median submitted applications by matched dermatology applicants over time. (Based on data from the National Residency Matching Program Applicant Surveys: 2008-2019)

Novice divers’ <100 logged dives, experienced divers’ ≥100 logged dives.Values measured on a 1–5 point Likert scale: 1 = not important at all, 2 = not important, 3 = average, 4 = important, 5 = very important.

Median values and interquartile range of continuous variables and absolute and relative frequency values of categorical variables for the total sample and separated by gender.

Standard deviation is shown in brackets.

Characteristics of the participants included in the study per each round (n = 21 for round 1; n = 8 for round 2), ranked by frequency in round 1.

Tabular Summaries - Communities at Risk As part of Montana DNRC’s Montana Wildfire Risk Assessment (MWRA), wildfire risk to homes, commercial buildings, and other structures was assessed across the state. The purpose of this assessment is to identify the counties and communities whose structures are most threatened by wildfire—both on average and in total. The risk-to-structures methods used for this assessment are identical to the methods used for structures within the overall MWRA project. See earlier section 3.4.1 of the report (page 20) for details. This portion of the report addresses only the tabular summaries. The summary methods used in this section were customized to the MWRA results from similar methods previously developed for the Pacific Northwest Risk Assessment (PNRA) and for the national Wildfire Risk to Communities (WRC) project.  Mean Risk to Structures We calculated the Mean Risk to Structures as the product of Mean Conditional Risk to Structures and Mean Burn Probability (multiplied by 1000 to remove decimal places). This is the primary variable by which the summary polygons are ranked. Like the components used to calculate it, Mean Risk to Structures is not a cumulative measure for a summary polygon, so it does not necessarily increase as the number or importance of structures increases. It represents the average of the structures in the polygon regardless of the total number or importance of structures. Total Structure Risk We calculated Total Structure Risk as the product of Mean Risk to Structures and Total Structure Importance. This is the secondary variable by which the summary polygons are ranked. Unlike the previous measures, the total importance of structures (their number and mean importance) strongly influences Total Structure Risk. The risk-to-structures results were summarized for two primary sets of summary polygons: MT Counties MT Communities Expanded Area Each set of summary polygons captures nearly all structures in Montana, without overlap. In the MT Counties set, a summary polygon is an individual county (e.g. Ravalli County). In the MT Communities (core plus zone combined) set called MT Communities Expanded Area, a summary polygon is the community core plus the zone surrounding the core (as defined below). Expanded Areas include populated areas outside of official community boundaries that are closer to the selected community than to any other community. Long definition: Populated areas not within the boundaries of a community were associated with the community to which they were closest, as measured by travel time. Travel time is influenced by road networks, associated travel speeds, and physical barriers such as water. Populated areas greater than 45 minutes travel time from any community are not included within the Expanded Area for any community. For this assessment, a community core was defined as a Populated Place Area (PPA) as identified by the U.S. Census Bureau. PPAs include incorporated cities and towns as well as Census Designated Places (CDPs). A CDP is an unincorporated concentration of population—a statistical counterpart to incorporated cities and towns. There are 364 PPAs across Montana. Of those, 127 (35 percent) are incorporated cities or towns, and 235 (65 percent) are CDPs. Two PPAs—Butte-Silver Bow and Anaconda-Deer Lodge—are unique in that they represent the balance of a county that is not otherwise incorporated; they are much larger in size than most PPAs. In the PPA dataset, the CDPs represent the location of highest concentration of population for a community; they do not include the less-densely populated areas surrounding the PPA. We refer to the U.S. Census PPA delineation as the community “core.” Approximately 66 percent of Montana’s total structure importance can be found within these PPA core areas (Figure A.1 of the Montana Wildfire Risk Assessment report). To include the populated area and structures surrounding the PPAs, Ager and others (2019) used a travel-time analysis to delineate the land areas closest by drive-time to each PPA core, up to a maximum of 45 minutes travel time. Approximately 33 percent of Montana’s total structure importance can be found within 45 minutes travel time of the cores. Only 1 percent of the total structure importance is not within 45-minutes travel time of any community core.

A spatial dataset and tool to simultaneously assess hydropower potential and ecological potential of the Swiss river network (Version 2016) Introduction The steadily growing demand for energy and the simultaneous pursuit of decarbonisation are increasing interest in the expansion of renewable energies worldwide. In Switzerland, various funding projects have been launched to promote technologies in the field of renewable energies and their application as quickly as possible. With the introduction of a funding instrument in 2009, the number of projects submitted to produce renewable energies increased rapidly. The applications for small hydropower plants (≤ 10 MW) were correspondingly numerous. However, the assessment of the environmental impact and its comparison with hydropower importance is still not standardized. To provide a basis for decision-making, a methodology was developed to determine the overall hydropower potential of a region. A detailed assessment of each river reach, and the systematic and holistic assessment of small hydropower projects at a regional scale are combined here. The assessment of a river reach is conducted at the river space (i.e., the river and adjacent areas) and at the surrounding landscape level. The HYDROpot_integral methodology was developed as part of Carol Hemund's dissertation (2012) at the University of Bern. It allows the evaluation of river reaches holistically, regarding ecological, social, economic and cultural criteria. As a second part of the overall project, the theoretical hydropower (or hydraulic) potential was calculated for the entire river network, which complemnets the spatial assessment. In particular, it is possible to classify river reaches into those that are more suitable for hydropower production (=”use”) and those that are more suitable for protection. Material and method The HYDROpot_Integral method was developed and tested on the basis of cantonal and national data (Hirschi et al. 2013). The method relies on 73 geodata sets. This holistic assessment is the key element of the entire assessment procedure. Its aim is to quantify the importance of the ecosystem functions in terms of services. The river network (GWN07) is divided into reaches of about 450m and for each reach two study units are defined. The river space (RS) records the ecosystem functions of the water body and the nearby riparian area. The length of the RS is 315 m on average in Switzerland and a maximum of 450 m, whereas the width is based on the FOEN definition (BWG 2001: 18f) and varies between 7-107 m. The surrounding landscape (SLS) is the second survey unit that records the ecosystem functions of the surrounding area over a range of 21 m to 321 m. The SLS is calculated over three times the RS width. The length of the SLS is identical to the length of the RS. The ecosystem functions are divided into three types: regulating (service A), cultural (service B) and provisioning (service C) functions. Accordingly, the assessment of the functions is divided into three parts and three values are assigned to each river reach. The more functions there are and the greater their performance, the higher these values are and the more important the corresponding functions are. Hence, these values quantify the importance of the ecosystem functions and the ecological, cultural and economic ecosystem services of each river reach. The concatenation of ecosystem services results in a value (ABC) that can occur in 125 different versions due to the chosen five-level value scale; i.e. each digit of the three-digit number sequence can be assigned a value between 1 and 5. Each of the 125 combinations, and thus each river reach, has its own characteristics determined by the assessments of the three function types. To record the suitability, the combinations are ranked according to their ecological, cultural and economic ecosystem services. These rules mean that the combination that is most suitable for hydropower production at minimum cost in terms of ecological and cultural ecosystem services and has a high economic potential is ranked first; rank 125 indicates the highest ecological and cultural ecosystem services and the lowest economic services and is therefore most suitable for protection. A river reach that is excluded from hydropower use due to legislation, a so-called priority reach, is given rank 126 from the outset and specially marked. A more detailed description of the methods can be found in Hirschi et al. 2013 [Link]. The dataset presented here presents the latest state of the HYDROpot_integral methodology applied at the national level. Only national data that is easily accessible was used in the preparation of the dataset. The cantonal data, such as renaturation and revitalization, would have to be requested by each canton individually and was excluded here. The nationwide value synthesis was made with R. A list of data sources can be found here [link to text file] A list of all parameters can be downloaded here [link to PDF and text files] Dataset description Data is presented as a single shapefile. It contains the river network and all assessment results obtained with HYDROpot_Integral. Changes in the methodology compared to the original method (Hirschi et. al 2013) * RS_A11 Ecomorphology: recorded for the whole of Switzerland and zero values equated with NA; individual cantons such as Zug and St. Gallen have no mapped values according to the modular concept of the federal government, Valais and Graubünden only the main valleys, Ticino and Fribourg not completely (BAFU 2009). * RS_A14 Renaturation and revitalization data: not centrally available at the time of data collection. centrally available, therefore values in GR were equated with NA. * RS_A15 Dilution ratio at wastewater treatment plants (WWTPs) for discharges: Zero values equal to NA. * RS_A20 Water flow: use WASTA (2013) with hydroelectric power plants (- 300 kW) under Federal control and dams serving hydroelectricity (Dam, as of 2013). * RS_C05 Synoptic hazard maps: are cantonally managed at the time of data collection, Values in GR are marked with a 5 so that the systematics in the decision tree is not affected. is affected. * Water quality (RS_A15, RS_A16, RS_A17, RS_A18, RS_A19): for the evaluation of the function type. A Nature, it is important whether the median of the five values is less than or equal to 3 in total. This evaluation is based on the decision tree for evaluating GR (Hirschi et al. 2013:22). Therefore, an evaluation of the station data is made where critical and possible river segments with poor quality (median less than 3) exist. Only two longer and one short sections in Switzerland receive a lower median than 3 for water quality. * SLS_B06 Visibility: For 99 percent of the river segments (30,733 of 31,062) in the canton of Bern (2015 reduced version), the landscape area is considered to be visible. Due to this high number of sections, a large number of viewpoints in the layer of Swisstopo and the computation time and computability in ArcGIS, the landscape area is classified as generally viewable (equal to 1). 16 Method Additional indicators were added (see Appendix B.2): * SLS_A21 Dissection * SLS_A22 Forest areas * SLS_B03 Hiking trails * SLS_B10 Residential and vacation homes * SLS_B11 Tourist infrastructure * SLS_C01 Landfill * SLS_C03 Infrastructure * SLS_C05 Industry * SLS_C06 Agricultural land Not to be added, although present to some extent: * SLS_B06 Cultural assets of national importance: here, too, the calculability of the visibility analysis is for the whole of Switzerland is limited * SLS_A15 Legally binding protection and land use planning: the individual river sections are not clearly designated, i.e. no geodata exist The following data are also not supplemented, as they are cantonal data: * SLS_A10 Cantonal nature reserves * SLS_A16 Forest reserves * SLS_A17 Cantonal inventories and contractually protected areas

A paradigm shift away from null hypothesis significance testing seems in progress. Based on simulations, we illustrate some of the underlying motivations. First, p-values vary strongly from study to study, hence dichotomous inference using significance thresholds is usually unjustified. Second, 'statistically significant' results have overestimated effect sizes, a bias declining with increasing statistical power. Third, 'statistically non-significant' results have underestimated effect sizes, and this bias gets stronger with higher statistical power. Fourth, the tested statistical hypotheses usually lack biological justification and are often uninformative. Despite these problems, a screen of 48 papers from the 2020 volume of the Journal of Evolutionary Biology exemplifies that significance testing is still used almost universally in evolutionary biology. All screened studies tested default null hypotheses of zero effect with the default significance threshold of p = 0.05, none presented a pre-specified alternative hypothesis, pre-study power calculation and the probability of 'false negatives' (beta error rate). The results sections of the papers presented 49 significance tests on average (median 23, range 0–390). Of 41 studies that contained verbal descriptions of a 'statistically non-significant' result, 26 (63%) falsely claimed the absence of an effect. We conclude that studies in ecology and evolutionary biology are mostly exploratory and descriptive. We should thus shift from claiming to 'test' specific hypotheses statistically to describing and discussing many hypotheses (possible true effect sizes) that are most compatible with our data, given our statistical model. We already have the means for doing so, because we routinely present compatibility ('confidence') intervals covering these hypotheses.

Relative importance (RI) and modal rank (rank) of predictors in BRT models at a 1 km resolution (median, mean and s.d. of values across 100 model runs).

02029.px. National. Average expenditure by household and person by consid. current moment suitable for purchasing.