Chemical concentration, exposure, and health risk data for U.S. census tracts from National Scale Air Toxics Assessment (NATA). This dataset is associated with the following publication: Huang, H., R. Tornero-Velez, and T. Barzyk. Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 27(6): 544-550, (2017).

Analysis of ‘Groceries dataset ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heeraldedhia/groceries-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Association Rule Mining

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.

Association Rules are widely used to analyze retail basket or transaction data and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.

Details of the dataset

The dataset has 38765 rows of the purchase orders of people from the grocery stores. These orders can be analysed and association rules can be generated using Market Basket Analysis by algorithms like Apriori Algorithm.

Apriori Algorithm

Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent itemsets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.

An example of Association Rules

Assume there are 100 customers 10 of them bought milk, 8 bought butter and 6 bought both of them. bought milk => bought butter support = P(Milk & Butter) = 6/100 = 0.06 confidence = support/P(Butter) = 0.06/0.08 = 0.75 lift = confidence/P(Milk) = 0.75/0.10 = 7.5

Note: this example is extremely small. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Some important terms:

• Support: This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

• Confidence: This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears.

• Lift: This says how likely item Y is purchased when item X is purchased while controlling for how popular item Y is.

--- Original source retains full ownership of the source dataset ---

The SAR difference of different confidence degree thresholds in D = 4.

The data is contained in the winrar file - 'DataSet-AssociationMining-India.rar'

Once you open the above winrar file, you will see the below files & folders:

• File: "IndiaData-ForAssociationMining.xlsx" is the primary data retrieved from 'Refinitiv-Datastream' which was used in the project.

• Folder-1MetricsGT-NSE50 o This folder has MS-Excel macro files used to create return determinant data to be eventually used in the 'Final-Transaction-Table' from which associations would be mined. o This folder also has computed returns for different holding periods for different stocks considered in this study. File: "0_nYrRtnGTNSE50.xlsm" o This folder also has the 'Final-Sheet' used for mining of association rules.

• Folder: 2Analysis-GTNSE50 o This folder has the R-program used to mine associations. It also has the final sheets used in association mining for different holding periods. And the output of the association rules mined is also stored here (File name: RulesRHS_1YrRtnGTNSE50.csv and so on)

• Folder: 3Validation o This folder has data related to the validation carried out in the project. It has 2 sub-folders: § 1-MetricsForValidation: This folder has excel-macro files to compute the metrics required in the Final-Table for validation of the association rules. § 2-BetaCalc-PortRtns: This folder has the Final transaction sheet which will be later used to compute portfolio beta and portfolio returns for each association rule. This also has the computation of portfolio beta & portfolio returns for each of the 10 association rules analyzed in this paper.

• Folder: 4LogitRegression o This folder has the 'R' program used to carry out Logit regression and different model consistency test. It also has the input file for the Logit regression (Filename: India-LogitRegression-csv.csv) o The sub-folder 'Regression_OP' has the output of Logit regression for all association rules for different holding periods.

Association Rule Mining

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.

Association Rules are widely used to analyze retail basket or transaction data and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.

Details of the dataset

The dataset has 38765 rows of the purchase orders of people from the grocery stores. These orders can be analysed and association rules can be generated using Market Basket Analysis by algorithms like Apriori Algorithm.

Apriori Algorithm

Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent itemsets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.

An example of Association Rules

Assume there are 100 customers 10 of them bought milk, 8 bought butter and 6 bought both of them. bought milk => bought butter support = P(Milk & Butter) = 6/100 = 0.06 confidence = support/P(Butter) = 0.06/0.08 = 0.75 lift = confidence/P(Milk) = 0.75/0.10 = 7.5

Note: this example is extremely small. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Some important terms:

• Support: This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

• Confidence: This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears.

• Lift: This says how likely item Y is purchased when item X is purchased while controlling for how popular item Y is.

The one-item SAR of D = 3.

Many previous studies have shown that by using variants of "guilt-by-association", gene function predictions can be made with very high statistical confidence. In these studies, it is assumed that the "associations" in the data (e.g., protein interaction partners) of a gene are necessary in establishing "guilt". In this paper we show that multifunctionality, rather than association, is a primary driver of gene function prediction. We first show that knowledge of the degree of multifunctionality alone can produce astonishingly strong performance when used as a predictor of gene function. We then demonstrate how multifunctionality is encoded in gene interaction data (such as protein interactions and coexpression networks) and how this can feed forward into gene function prediction algorithms. We find that high-quality gene function predictions can be made using data that possesses no information on which gene interacts with which. By examining a wide range of networks from mouse, human and yeast, as well as multiple prediction methods and evaluation metrics, we provide evidence that this problem is pervasive and does not reflect the failings of any particular algorithm or data type. We propose computational controls that can be used to provide more meaningful control when estimating gene function prediction performance. We suggest that this source of bias due to multifunctionality is important to control for, with widespread implications for the interpretation of genomics studies.

Results obtained: Results.zip contains the results obtained by merged and unmerged methods for different query constraints across the five datasets in NSRR. (ZIP 199 kb)

1. Animal social network analysis (SNA) often uses proximity data obtained from automated tracking of individuals. Identifying associations based on proximity requires deciding on quantitative criteria such as the maximum distance or the longest time interval between visits of different individuals to still consider them associated. These quantitative criteria are not easily chosen based on a priori biological arguments alone.
2. Here we propose a procedure for optimising proximity-based association criteria in SNA, whereby different spatial and temporal criteria are screened to determine which combination detects more network structure. If we assume that biologically-relevant associations among individuals are non-random, and that proximity data are mostly influenced by those associations, then it is logical to select criteria that minimise random associations and show the underlying network structure more clearly.
3. We first used simulations to evaluate which of four simple descriptors of network structure remain unbiased (i.e., do not change directionally) when reducing the number of observations, since unbiased descriptors are necessary for comparing the structure of networks using different association criteria. Then, using two of those descriptors (coefficient of variation of the strength of associations, and network entropy), and empirical proximity data from automated tracking of common waxbills (Estrilda astrild) in a mesocosm environment, we found that the structure-based optimisation procedure selected the most biologically-relevant combination of spatial and temporal proximity criteria, in the sense that those criteria were also the best at distinguishing between previously known social sub-groups of individuals.
4. These results indicate that, provided that the assumptions for structure-based optimisation are met, this procedure can find the most biologically-relevant association criteria. Thus, under the condition that proximity data are shaped by non-random social associations, and if using adequate descriptors of network structure, structure-based optimisation may be a useful tool for SNA, particularly when a priori biological arguments are insufficient to inform the choice of proximity-based association criteria.

This repository provides the raw data, analysis code, and results generated during a systematic evaluation of the impact of selected experimental protocol choices on the metagenomic sequencing analysis of microbiome samples. Briefly, a full factorial experimental design was implemented varying biological sample (n=5), operator (n=2), lot (n=2), extraction kit (n=2), 16S variable region (n=2), and reference database (n=3), and the main effects were calculated and compared between parameters (bias effects) and samples (real biological differences). A full description of the effort is provided in the associated publication.

Stereotypes are beliefs about traits that are shared by members of a social group. Categorisation on the basis of group membership provides cognitive economy, condensing our experience of many group members into a general impression of that group. But generalisation comes at a price if it causes us to overlook an individual's unique characteristics and prejudge them on the basis of group membership. Application of negative stereotypes in particular (eg "members of group X are lazy") has damaging effects on society, generating fear, anger and resentment towards certain groups. It is thus important to understand how stereotypes form, and by extension how they might be unlearned. To the extent that stereotypes form on the basis of our experience with group members, they can be analysed using models of associative learning developed on the basis of studies of animal conditioning. Formation of a stereotype can be modelled as formation of an association between a group label (group X) and a trait (lazy), with the strength of the stereotype corresponding to the strength of this association. An associative view of stereotypes allows us to bring a wide body of research on animal and human associative learning (along with formal models derived from this research) to bear on the question of how stereotypes are formed. In particular, it suggests ways in which the stereotype formation process might be biased, causing us to develop irrational and objectively incorrect views of social groups. This idea forms the focus of the proposed research project.

Association Management Software Market size is valued at USD 2.08 Billion in the year 2024 and it is expected to reach USD 4.44 Billion in 2031 at a CAGR of 10.94% over the forecast period of 2024 to 2031.

Association Management Software Market Drivers

Digital Transformation and Automation: Organizations are increasingly adopting digital tools to streamline operations, reduce manual processes, and improve efficiency. AMS platforms offer comprehensive solutions for managing memberships, events, communications, and finances, which aligns with the broader trend of digital transformation across industries.

Increased Membership Engagement: Associations and non-profits are focusing on enhancing member engagement to retain and attract members. AMS provides tools for personalized communication, member portals, and social media integration, facilitating better engagement and a more tailored member experience.

Growth of Non-Profit Sector: The expansion of the non-profit sector globally has led to a higher demand for efficient management tools. AMS helps these organizations manage their growing membership bases, donations, and event planning, driving the demand for these solutions.

Global Acrylic Associative Thickener Market Report 2023 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2023-2029. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

Data supporting the paper Luciano et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nature Genetics (2017). doi: 10.1038/s41588-017-0013-8

This dataset is associated with NIST TN publication: Review of Smart Grid Standards for Testing and Certification (T&C) landscape analysis. It includes a list of 240 reviewed smart grid standards for T&C landscape analysis using a set of functional metrics that include information models and model mapping, communication protocols and protocol mapping, device physical performance, test method, guide and practice, and cybersecurity of standards. These functional metrics are used to analyze smart grid standards and their T&C program availability.

Association analysis of GRS and ANM.

Above-threshold regions in an ALE meta-analysis of all included experiments.

Collection description

Functional neuroimaging data on paired associate recollection have expanded over the years, raising the need for an integrative understanding of the literature. The present study performed a quantitative meta-analysis of the data to fulfill that need. The meta-analysis focused on the three most widely used types of activation contrast: Hit > Miss, Intact > Rearranged, and Memory > Perception.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

meta-analysis

Cognitive paradigm (task)

episodic recall

Map type

R

Association Management Software Market Report is Segmented by Deployment (Cloud-Based and On-Premise), Organization Size (Large Enterprises and SMEs), and Geography (North America, Europe, Asia Pacific, Latin America, Middle East, and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

This dataset provides geospatial location data and scripts used to analyze the relationship between MODIS-derived NDVI and solar and sensor angles in a pinyon-juniper ecosystem in Grand Canyon National Park. The data are provided in support of the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States". The data and scripts allow users to replicate, test, or further explore results. The file GrcaScpnModisCellCenters.csv contains locations (latitude-longitude) of all the 250-m MODIS (MOD09GQ) cell centers associated with the Grand Canyon pinyon-juniper ecosystem that the Southern Colorado Plateau Network (SCPN) is monitoring through its land surface phenology and integrated upland monitoring programs. The file SolarSensorAngles.csv contains MODIS angle measurements for the pixel at the phenocam location plus a random 100 point subset of pixels within the GRCA-PJ ecosystem. The script files (folder: 'Code') consist of 1) a Google Earth Engine (GEE) script used to download MODIS data through the GEE javascript interface, and 2) a script used to calculate derived variables and to test relationships between solar and sensor angles and NDVI using the statistical software package 'R'. The file Fig_8_NdviSolarSensor.JPG shows NDVI dependence on solar and sensor geometry demonstrated for both a single pixel/year and for multiple pixels over time. (Left) MODIS NDVI versus solar-to-sensor angle for the Grand Canyon phenocam location in 2018, the year for which there is corresponding phenocam data. (Right) Modeled r-squared values by year for 100 randomly selected MODIS pixels in the SCPN-monitored Grand Canyon pinyon-juniper ecosystem. The model for forward-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle. The model for back-scatter MODIS-NDVI is log(NDVI) ~ solar-to-sensor angle + sensor zenith angle. Boxplots show interquartile ranges; whiskers extend to 10th and 90th percentiles. The horizontal line marking the average median value for forward-scatter r-squared (0.835) is nearly indistinguishable from the back-scatter line (0.833). The dataset folder also includes supplemental R-project and packrat files that allow the user to apply the workflow by opening a project that will use the same package versions used in this study (eg, .folders Rproj.user, and packrat, and files .RData, and PhenocamPR.Rproj). The empty folder GEE_DataAngles is included so that the user can save the data files from the Google Earth Engine scripts to this location, where they can then be incorporated into the r-processing scripts without needing to change folder names. To successfully use the packrat information to replicate the exact processing steps that were used, the user should refer to packrat documentation available at https://cran.r-project.org/web/packages/packrat/index.html and at https://www.rdocumentation.org/packages/packrat/versions/0.5.0. Alternatively, the user may also use the descriptive documentation phenopix package documentation, and description/references provided in the associated journal article to process the data to achieve the same results using newer packages or other software programs.

Understanding the spatial scale of local adaptation and the factors associated with adaptive diversity are important objectives for ecology and evolutionary biology, and have significant implications for effective conservation and management of wild populations and natural resources. In this study, we used an environmental association analysis (EAA) to identify important bioclimatic variables correlated with putatively adaptive genetic variation in a benthic marine invertebrate – the giant California sea cucumber (Parastichopus californicus) – spanning coastal British Columbia and southeastern Alaska. We used a redundancy analysis (RDA) with 3,699 SNPs obtained using RAD sequencing to detect candidate markers associated with 11 bioclimatic variables, including sea bottom and surface conditions, across two spatial scales (entire study area and within sub-regions). At the broadest scale, RDA revealed 59 candidate SNPs, 86% of which were associated with mean bottom temperature. Similar pat...