Introduction

CEO Background Statistics: The CEO or Chief Executive Officer is the pillar of the company. CEO leads the company in terms of revenue growth, operational efficiency, strategic vision, and overall business. A CEO must possess qualities like leadership, vision, communication skills, optimism, decision-making ability, intelligence, and much more that drive the company and its employees toward a better future.

However, becoming a CEO is not easy in today’s competitive world. Money might lure people into these but it is difficult to manage all kinds of departments in the company in a progressive way. Let’s understand CEO background statistics to learn the basics.

The data set measures participants in a course and their experience of 5 important elements in a recovery process. The elements measured are Hope, Quality of life, Empowerment, Loneliness and Confidence. The scope is to examine the participants experience of the intervention. Article abstract: Recovery as a phenomenon has gained influence in the local community services within mental health and substance use. An essential element to enhance recovery is peer support. This article provides an overview of the implementation of a peer-led intervention –“Recovery is up to you” – developed in the Netherlands into a Norwegian context, as well as the characteristics and completion rate of the participants. Self-reported, validated measures were used to provide information about the participants and their evaluation of their situation in this new context. These measures have been used before to assess this intervention in the Netherlands. Five measures were conducted, assessing different elements important in a recovery process, such as hope, loneliness/support and empowerment. This study aims to provide a description of the implementation process and the participants involved to generate knowledge about the implementation process and the use of peer support. For the analysis, we conducted a paired t-test for baseline and follow-up 1 and baseline and follow-up 2 for each of the measures, along with descriptive analysis. The results provide a description of the participants in this study and their evaluation of their experience with the course. The discussion provides some comparisons with a Dutch RCTstudy on the effects of the intervention, to enhance its local relevance. In addition, we discuss the strengths and limitations of the study, such as relevance and biased selection. To conclude, we summarise by indicating the interventions’ relevance for future practice and the need for further research.

This "Predicted Background Conductivity" view consists of a shapefile derived from National Hydrography Dataset Plus Version 2.0 which displays modeled natural background conductivity for the continental United States. The displayed information is based on specific conductivity predictions for stream segments in the contiguous United States from the Natural Background Specific Conductivity (NBSC) model. The NBSC model was developed using a random forest modeling approach and enables comparison with measured in-stream conductivity. Geology, soil, vegetation, climate and other empirically measured data were used as inputs. The NBSC model was designed for streams with natural background SC < 2000 µS/cm. Above this level (typical for freshwater), NBSC model estimates may be less reliable. Data for some parameters that affect background SC were not readily available and were therefore not included in the model. These include freshwater and marine interfaces, natural mineral springs, salt deposits which may affect groundwater and streams, and other natural sources of salts. In such areas, the model is likely to underestimate SC. Local knowledge is often necessary to assess differences between predicted and measured background SC. More information about the model and datasets can be found at Freshwater Explorer Story Metadata. The calculated predicted background conductivity for individual stream segments in the contiguous U.S.A. and metadata are accessible from the ArcGIS platform on Predicted Background Conductivity Data. Data is available as table (from Data) or in by pointing and clicking on a stream segment (from Visualization) (https://arcg.is/9vnrv). This data set is used in the Freshwater Explorer Beta 0.1 which on Jan. 2020 is password protected but can be obtained by requesting access from cormier.susan@epa.gov and then using the link: https://arcg.is/KHb9S. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

• Traces variants (traces-normalized.zip, traces-adjusted.zip)
  • ./traces-normalized/ — normalized PCAP files and details in YAML format;
  • ./traces-adjusted/ — configuration files for traces combination in YAML format.
• Computed statistics (statistics.zip)
  • ./statistics-background/ — background traffic statistics computed by ID2T;
  • ./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);
  • ./statistics-difference/ — computed mean and median differences of background and combined traffic traces.
• Evaluation results
  • statistics-difference.ipynb — file containing visualization of statistics differences.

This dataset contains tabular files with information about the usage preferences of speakers of Maltese English with regard to 63 pairs of lexical expressions. These pairs (e.g. truck-lorry or realization-realisation) are known to differ in usage between BrE and AmE (cf. Algeo 2006). The data were elicited with a questionnaire that asks informants to indicate whether they always use one of the two variants, prefer one over the other, have no preference, or do not use either expression (see Krug and Sell 2013 for methodological details). Usage preferences were therefore measured on a symmetric 5-point ordinal scale. Data were collected between 2008 to 2018, as part of a larger research project on lexical and grammatical variation in settings where English is spoken as a native, second, or foreign language. The current dataset, which we use for our methodological study on ordinal data modeling strategies, consists of a subset of 500 speakers that is roughly balanced on year of birth. Abstract: Related publication In empirical work, ordinal variables are typically analyzed using means based on numeric scores assigned to categories. While this strategy has met with justified criticism in the methodological literature, it also generates simple and informative data summaries, a standard often not met by statistically more adequate procedures. Motivated by a survey of how ordered variables are dealt with in language research, we draw attention to an un(der)used latent-variable approach to ordinal data modeling, which constitutes an alternative perspective on the most widely used form of ordered regression, the cumulative model. Since the latent-variable approach does not feature in any of the studies in our survey, we believe it is worthwhile to promote its benefits. To this end, we draw on questionnaire-based preference ratings by speakers of Maltese English, who indicated on a 5-point scale which of two synonymous expressions (e.g. package-parcel) they (tend to) use. We demonstrate that a latent-variable formulation of the cumulative model affords nuanced and interpretable data summaries that can be visualized effectively, while at the same time avoiding limitations inherent in mean response models (e.g. distortions induced by floor and ceiling effects). The online supplementary materials include a tutorial for its implementation in R.

1. In order to understand and grasp the development of science and technology in our country, and to establish scientific and technological indicators for objective comparison with other countries as a reference for national science and technology development policies, the National Science and Technology Commission conducts the "National R&D Status Survey" regularly every year. This dataset is one of the statistical results of the "National R&D Status Survey." 2. "..." denotes no value. 3. "0" denotes less than one unit.

Group statistics.

The High Energy Astrophysics Science Archive Research Center (HEASARC) and the Legacy Archive for Microwave Background Data Analysis (LAMBDA) have merged into a single organization that will use the name HEASARC. The merged archive will continue to provide all of the services currently being offered by the two archives, while better serving users interested in studies requiring both high energy and microwave data. Users will notice few immediate changes, but we will gradually fully integrate all the services of the archives.

Image used for Open Checkbook BR

In this file there are statistics for a number of variables broken down by Malmö’s different areas over time. Source Unless otherwise stated, the statistics in this database are retrieved from Statistics Sweden’s (SCB) regional database, Skånedatabasen or from Statistics Sweden’s area statistics database (OSDB). The Skåne database and OSDB show data from several different sources that Statistics Sweden has compiled on a geographical level. The statistics only cover persons who are part of the population registered in the population. Therefore, persons without a residence permit, such as asylum seekers, and persons who simply have not registered in the municipality are not included. Statistics Sweden does not provide statistics on which language residents speak, which religion you belong to or what ethnicity or political views you have. Therefore, such data is not available here either. However, the Electoral Authority reports election results per constituency on its website val.se. There are statistics from the last election as well as several previous elections available. Please note, however, that the constituencies do not necessarily follow the division of the city made here. Update The data is updated every spring as Statistics Sweden releases the figures to the municipality. Most variables are available for the year before. However, income and employment data are released with another year’s backlog. Unless otherwise stated, the date of measurement is 31 December of each year. Geographical breakdown Unless otherwise stated, the data is available for Malmö as a whole and broken down into urban areas (5 pieces), districts (10 pieces) and subareas (136 pieces). In addition to these, there is a residual post that contains the people who are not written in a specific place in the municipality, have protected identity and more. These people are also part of the total. In several of the subareas there are no or only a few registered population registers. Therefore, no data are reported for these areas. Examples of such sub-areas are parks such as Pildammsparken and Kroksbäcksparken and industrial areas such as Fosieby Industriområde and Spillepengen. Privacy clearance In order to protect the identity of individuals, the data is confidentially audited. This means that small values are suppressed, i.e. replaced by empty cells. However, the values are included in summaries. In general, the following rules apply: * No statistics are reported for geographical areas with very few housing. * No cells with fewer than 5 individuals are reported. For data classified as sensitive (e.g. income and country of birth), larger values can also be suppressed. * In cases where a subcategory (e.g. a training category) is too small to be accounted for, all categories are often suppressed. Please use the numbers, but use “City Office, Malmö City” as the source.

1. To facilitate the understanding and mastery of the status of scientific and technological research and development in our country, and to establish scientific and technological indicators, and to make objective comparisons with other countries as a reference for the formulation of national science and technology development policies, the National Science and Technology Commission holds the "National R&D Status Survey" regularly every year. This dataset is one of the statistical results of the "National R&D Status Survey."2. Full-Time Equivalent (FTE) refers to the number of people engaged in a certain research and development work, converted into the number of people working full-time in that work. For example, if a researcher spends half of the work time on teaching and the other half on research and development throughout the year, then the researcher is equivalent to 0.5 FTE (or 0.5 person-year); if working full-time (calculated at 250 working days per year) on research, then it is equivalent to 1 FTE. Source: Frascati Manual (OECD)3. "..." refers to no value.4. "0" refers to less than one unit.

Background complexity and the detectability of camouflaged targets by birds and humans: Raw data of the experiment

Dataset Card for instruction-background-noise-data-synthetic

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/jan-hq/instruction-background-noise-data-synthetic/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info… See the full description on the dataset page: https://huggingface.co/datasets/Menlo/instruction-background-noise-data-synthetic.

The data set is publically available so that everyone can use it and check its authenticity.

Number of predicted and observed events in the $\mu\mu X$ final states. The quoted uncertainties include statistical and systematic uncertainties.

This dataset allows analyzing the relationship between the departmental population and access to higher education in the regional branches (Caldas, Cesar, Nariño, Valle del Cauca, Arauca, Archipelago of San Andres, Amazonas) of the Universidad Nacional de Colombia, making it possible to evaluate inequalities and plan inclusion strategies.

This study presents a dataset that compares the number of students admitted to the regional campuses of the Universidad Nacional de Colombia with the departmental population of each region. Data from the National Administrative Department of Statistics (DANE) and university admission records were used to analyze patterns of access to higher education. This analysis facilitates the planning of strategies to reduce inequalities and improve the distribution of educational opportunities in Colombia.

Identifying possible access difficulties faced by the population of each regional headquarters will allow improving academic strategies to work on access and permanence in higher education. This dataset compares the population by department where the Universidad Nacional de Colombia is present with the data of active students in the regional branches (Amazonia, Caribe, Manizales, Orinoquia, Palmira, Tumaco and De la Paz).