The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

About A Global Database on Migrant Boat Losses at Sea, 1990 to 2015 contains information on migrant boat losses worldwide between 1990 and 2015. To construct this database, we employed a mixed methodology designed to locate, record and analyse boat losses to fill gaps we identified in the border externalisation literature. This involved collecting data from media aggregators and additional sources using content analysis for descriptive and time series statistical analysis. Our novel contributions via the database included to generalise time and geography in our analysis of boat losses, use of statistical methods therein and the provision of new empirical evidence related to the claim in migration studies that tougher border enforcement in the name of deterrence is generally ineffective in reducing migrant flows. Summary While migration by boat is an ancient human phenomenon, recent increases in deaths of migrants crossing the sea reached historical highs among those trying to land on sovereign territory of nation-states of the “Global North”. Increases in deaths also were accompanied by significant increases in global media coverage and resources dedicated to enforcement operations in the annual budgets of border enforcement activities. Despite this, little existing scholarship tracked this relationship between increased enforcement and migrant losses at sea. This project, therefore, worked to empirically demonstrate correlations between observed boat losses and enforcement using statistical methods. Our findings were published in the journal International Migration in 2018 under the title, “Between Enforcement and Precarity: Externalization and Migrant Deaths at Sea”. In this article, and based on this database, we argued that although discourse about boat interception and externalisation has shifted to humanitarian rescue narratives, offshore enforcement by any other name continues to be highly correlated with migrant deaths. Our analysis continues to be timely due to empirical spikes in human displacement worldwide. Data Structure We built the collected data into a structured database sourced from targeted queries on two large media databases, LexisNexis and Factiva, as well as search engines to locate reports on migrant boat losses. We analysed and stored these articles in portable document format (PDF) for recording in our database. We were interested in a number of variables, including data of the loss incident, ship name, location, region, estimated passengers, estimated losses, ship origin, passenger origins, desired destination and related enforcement activities. The resulting data were linked through a comma-separated value (CSV) table to the PDF files for analysis in Stata 14. In terms of linkages in the database, we named each article file after the case it represents; “1.pdf”, therefore, represents the first case/observation. Multiple but distinct articles for the same case featured the same number appended by a lower-case Roman numeral (e.g., “1a.pdf” and “1b.pdf”). The database, “losses_at_sea_database_10102017.csv”, contains a variable, Files, which associated the given case with its corresponding articles. By the end of the project, we had collected 250 media reports on 218 discrete boat loss incidents and stored them per best practises in data management. We also catalogued 30 photographs related to these incidents that appeared in reports. Final case and report counts were obtained after quality assurance of all data. Data Sources As noted, our primary data sources were the media aggregator engines LexisNexis and Factiva; however, we also used search engines. While initial searches focused solely on terms like “migrant boat incident”, we quickly began to identify more robust keywords and phrases in order to create more accurate searches. In attempts to exhaust reports from these sources, we employed multiple search terms and compared our outputs to contemporary data sets. As we analysed the documents, we decided to code additional variables not previously considered. For example, while many reports had estimates of passenger survivals or deaths, it became apparent that they also recorded the number of passengers missing from a loss. Some variables we had sought to record, like ship name, were nearly universally absent from available reports, but were included as missing observations where appropriate. We also found that most incidents featured more than one report, some of which recorded different but important details for the project. As stated, we therefore stored and used multiple reports of the same boat loss or operation for the data sets to enhance the completeness and reliability of the data. If you use these data, please cite the original source at Williams, K., & Mountz, A. (2018). Between Enforcement and Precarity: Externalization and Migrant Deaths at Sea. International Migration, 56(5), 74-89. Should you have any comments, questions or requested edits or...

Snapshot img

Alternative Data Market Size 2025-2029

The alternative data market size is valued to increase USD 60.32 billion, at a CAGR of 52.5% from 2024 to 2029. Increased availability and diversity of data sources will drive the alternative data market.

Major Market Trends & Insights

North America dominated the market and accounted for a 56% growth during the forecast period.
By Type - Credit and debit card transactions segment was valued at USD 228.40 billion in 2023
By End-user - BFSI segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 6.00 million
Market Future Opportunities: USD 60318.00 million
CAGR from 2024 to 2029 : 52.5%

Market Summary

The market represents a dynamic and rapidly expanding landscape, driven by the increasing availability and diversity of data sources. With the rise of alternative data-driven investment strategies, businesses and investors are increasingly relying on non-traditional data to gain a competitive edge. Core technologies, such as machine learning and natural language processing, are transforming the way alternative data is collected, analyzed, and utilized. Despite its potential, the market faces challenges related to data quality and standardization. According to a recent study, alternative data accounts for only 10% of the total data used in financial services, yet 45% of firms surveyed reported issues with data quality.
Service types, including data providers, data aggregators, and data analytics firms, are addressing these challenges by offering solutions to ensure data accuracy and reliability. Regional mentions, such as North America and Europe, are leading the adoption of alternative data, with Europe projected to grow at a significant rate due to increasing regulatory support for alternative data usage. The market's continuous evolution is influenced by various factors, including technological advancements, changing regulations, and emerging trends in data usage.

What will be the Size of the Alternative Data Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Alternative Data Market Segmented ?

The alternative data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Credit and debit card transactions
  Social media
  Mobile application usage
  Web scrapped data
  Others


End-user

  BFSI
  IT and telecommunication
  Retail
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

Alternative data derived from credit and debit card transactions plays a significant role in offering valuable insights for market analysts, financial institutions, and businesses. This data category is segmented into credit card and debit card transactions. Credit card transactions serve as a rich source of information on consumers' discretionary spending, revealing their luxury spending tendencies and credit management skills. Debit card transactions, on the other hand, shed light on essential spending habits, budgeting strategies, and daily expenses, providing insights into consumers' practical needs and lifestyle choices. Market analysts and financial institutions utilize this data to enhance their strategies and customer experiences.

Natural language processing (NLP) and sentiment analysis tools help extract valuable insights from this data. Anomaly detection systems enable the identification of unusual spending patterns, while data validation techniques ensure data accuracy. Risk management frameworks and hypothesis testing methods are employed to assess potential risks and opportunities. Data visualization dashboards and machine learning models facilitate data exploration and trend analysis. Data quality metrics and signal processing methods ensure data reliability and accuracy. Data governance policies and real-time data streams enable timely access to data. Time series forecasting, clustering techniques, and high-frequency data analysis provide insights into trends and patterns.

Model training datasets and model evaluation metrics are essential for model development and performance assessment. Data security protocols are crucial to protect sensitive financial information. Economic indicators and compliance regulations play a role in the context of this market. Unstructured data analysis, data cleansing pipelines, and statistical significance are essential for deriving meaningful insights from this data. New

This project includes the supplementary files accompanying the paper "Paradigm uniformity effects on French liaison" :

study1b-data.csv: this file contains the dataset collected in Study 1. The variables are described in study1b-rscript.R

study1b-rscript.R: R script that was used to produce the figure in Study 1 and the corresponding statistical analysis

study2-data.csv: this file contains the dataset collected in Study 2 (2021). The variables are described in study2-rscript.R

study2-rscript.R: R script that was used to produce the figures in Study 2 and the corresponding statistical analysis.

check-study2-data.csv: this file contains the dataset that was used to evaluate the reliability of the coding in Study 2. A subset of the sound files used in Study 2 (50 /ti/ sequences) were annotated again by the author two years after the annotation. The first annotation was made in February-March 2021 and the second one in May 2023.

study2-reliability-study.R: R script that was used to test the reliability of the annotations in Study 2

prosodic-ambiguity-final-2.txt (tab-delimited text file): this file contains the constraint-based analysis of Encrevé's (1988) data on liaison enchaînée and liaison non-enchaînée. The file can be run with OT-Soft 2.5 (https://linguistics.ucla.edu/people/hayes/otsoft/OTSoftManual_2.5_April_2021.pdf).

phonetic-ambiguity-final-2.txt (tab-delimited text file): this file contains the constraint-based analysis of Côté's (2014) data on affrication of stable word-final consonants, stable word-initial initial, and liaison consonants. The file can be run with OT-Soft 2.5.

COGETO-prosodic-ambiguity.zip: this folder contains the input file and output files of the CoGeTo analysis of the case study on prosodic ambiguity (see prosodic-ambiguity-final-2.txt). CoGeTo is a software developed by Giorgio Magri and Arto Anttila to explore the typological predictions of constraint-based grammars (https://cogeto.stanford.edu/home).

COGETO-phonetic-ambiguity.zip: this folder contains the input file and output files of the CoGeTo analysis of the case study on prosodic ambiguity (see phonetic-ambiguity-final-2.txt).

Files linked to the first version of the paper can also be found here (the first version of the paper is available on lingbuzz: https://ling.auf.net/lingbuzz/006457):

prosodic-ambiguity.txt (tab-delimited text file): this file contains the constraint-based analysis of Encrevé's (1988) data on liaison enchaînée and liaison non-enchaînée. The file can be run with OT-Soft 2.5 (https://linguistics.ucla.edu/people/hayes/otsoft/OTSoftManual_2.5_April_2021.pdf).

phonetic-ambiguity.txt (tab-delimited text file): this file contains the constraint-based analysis of Côté's (2014) data on affrication of stable word-final consonants, stable word-initial initial, and liaison consonants. The file can be run with OT-Soft 2.5.

study1-data.csv: this file contains the dataset collected in Study 1 (first version). The variables are described in study1-rscript.R

study1-rscript.R: R script that was used to produce the figures in Study 1 (first version) and the corresponding statistical analysis.

BackgroundNaltrexone is a pharmacological intervention widely used for alcohol use disorder (AUD), opioid use disorder (OUD), and several off-label conditions. Systematic reviews (SRs) play a critical role in synthesizing data on the efficacy and safety of such interventions to inform clinical guidelines and decision-making. However, adequate reporting of harms in SRs remains inconsistent, limiting the ability to fully assess the safety profile of naltrexone. This study evaluates completeness of harms reporting and methodological quality in SRs focusing on naltrexone.MethodsA comprehensive search of MEDLINE, EMBASE, Epistemonikos, and the Cochrane Database of Systematic Reviews was conducted. The study employed masked, duplicate screening and data extraction. Included SRs were evaluated for completeness of harms reporting using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) harms checklist and other established frameworks. Methodological quality was appraised using the A MeaSurement Tool to Assess Systematic Reviews-2 (AMSTAR-2) tool, and primary study overlap among SRs was assessed through corrected covered area (CCA) analysis.ResultsA total of 87 SRs were included in the analysis. Only 1.1% (1/87) utilized severity scales to classify harms, and 4.6% (4/87) defined harms in their methods. Nearly half (48.3%) of SRs failed to address harms as either a primary or secondary outcome. A total of 82.8% (72/87) of SRs were rated as “critically low” quality by AMSTAR-2. Statistical analysis revealed a significant relationship between “critically low” AMSTAR-2 ratings and incomplete harms reporting (p = 0.0486). Additionally, four SR pairs demonstrated “high” overlap (>50%) of primary studies, accompanied by inconsistencies in harms reporting.ConclusionOur findings underscore the critical need for improved and standardized harms reporting in SRs on naltrexone. Inconsistent and incomplete reporting limits the ability of clinicians to fully assess the safety profile of naltrexone within systematic reviews. Adopting established frameworks such as PRISMA harms extensions and severity scales is imperative to enhance transparency and reliability in SRs. This study advocates for methodological improvements in SRs to support comprehensive safety evaluations and evidence-based prescribing of naltrexone.

Abstract

Introduction

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. Industrial sector occupies an important position in the State economy and has a pivotal role to play in the rapid and balanced economic development. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

Coverage of the Annual Survey of Industries extends to the entire Factory Sector, comprising industrial units (called factories) registered under section 2(m)(i) and 2(m)(ii) of the Factories Act.1948, wherein a "Factory", which is the primary statistical unit of enumeration for the ASI is defined as:- "Any premises" including the precincts thereof:- (i) wherein ten or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on with the aid of power or is ordinarily so carried on, or (ii) wherein twenty or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on without the aid of power. In addition to section 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, electricity units registered with the Central Electricity Authority and Bidi & Cigar units, registered under the Bidi & Cigar Workers (Conditions of Employment) Act,1966 are also covered in ASI.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948. Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI. The geographical coverage of the Annual Survey of Industries, 2002-03 has been extended to the entire country except the states of Arunachal Pradesh, Mizoram and Sikkim and Union Territory of Lakshadweep.

Kind of data

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2002-03 is a circular systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All industrial units belonging to the six less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands.
b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns. c) After excluding the Census Sector units as defined above, all units belonging to the strata (State by 4-digit of NIC-04) having less than or equal to 4 units are also considered as Census Sector units.

Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of workers and samples are then drawn circular systematically considering sampling fraction of 20% within each stratum (State X Sector X 4-digit NIC) for all the states. An even number of units with a minimum of 4 are selected and evenly distributed in two sub-samples. The sectors considered here are Biri, Manufacturing and Electricity.

Sampling deviation

There was no deviation from sample design in ASI 2002-03

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A :IDENTIFICATION PARTICULARS BLOCK B : PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C : FIXED ASSETS BLOCK D : WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H : INPUT ITEMS (indigenous items consumed) BLOCK I : INPUT ITEMS – directly imported items only (consumed) BLOCK J : PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

Code list, State code list, Tabulation program and ASICC code are also may be refered in the External Resources which are used for editing and data processing as well..

Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 2002-03 data and the extracted data from ASI 01-02 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A ,pos:62-70 (Please refer STRUC03.XLS) for ASI 2002-03 data. The multiplier is calculated for each sub-stratum (i.e. State X NIC'98(4 Digit) X sub-stratum) after adjusting for non-response cases.

Status of unit code 17-20 may always be considered for all processing.

Merging of unit level data As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise the loss of information.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula. Programs developed in Visual Foxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

The Early Numeracy Test-Revised (ENT-r) development was followed by several provisional standardization processes to adapt to this new version. Subsequently, the ENT-r underwent translation into Spanish and a shift from a paper-and-pencil format to a computerized version, intending to make it accessible online for schools. This paper introduces the adapted Spanish version of ENT-r and outlines the provisional standardization procedure conducted with a group of Spanish children. In this initial pilot study, 141 children aged between 4 and 7 underwent individual assessments. Among them, 71 were girls (50.3%), and 70 were boys (49.6%). Selected from three public schools in a middle-class area, the children were evaluated by experienced researchers with expertise in assessing young children. The study involved three provisional statistical analyses using ENT-r data. Initially, a descriptive analysis was conducted, followed by a cross-age score comparison to assess scores across different age groups. Finally, a reliability study was performed. Preliminary results from these analyses indicate that the ENT-r demonstrates reliability, and its items effectively discriminate between prerequisite and counting tasks. Finally, an approximate statistical estimation was carried out regarding the level of mathematical competence, which is one of the parameters provided by the test, allowing the development of alternative improvement programs for the less prominent values.

The precise determination of leaf shape is crucial for the quantification of morphological variations between individual leaf ranks and cultivars and simulating their impact on light interception in functional-structural plant models (FSPMs). Standard manual measurements on destructively collected leaves are time-intensive and prone to errors, particularly in maize (Zea mays L.), which has large, undulating leaves that are difficult to flatten. To overcome these limitations, this study presents a new camera method developed as an image-based computer vision approach method for maize leaf shape analysis. A field experiment was conducted with seven commonly used silage maize cultivars at the experimental station Heidfeldhof, University of Hohenheim, Germany, in 2022. To determine the dimensions of fully developed leaves per rank and cultivar, three destructive measurements were conducted until flowering. The new camera method employs a GoPro Hero8 Black camera, integrated within an LI-3100C Area Meter, to capture high-resolution videos (1920 × 1080 pixels, 60 fps). A semi-automated software facilitates object detection, contour extraction, and leaf width determination, including calibration for accuracy. Validation was performed using pixel-counting and contrast analysis, comparing results against standard manual measurements to assess accuracy and reliability. Leaf width functions were fitted to quantify leaf shape parameters. Statistical analysis comparing cultivars and leaf ranks identified significant differences in leaf shape parameters (p < 0.01) for term alpha and term a. Simulations within a FSPM demonstrated that variations in leaf shape can alter light interception by up to 7%, emphasizing the need for precise parameterization in crop growth models. The new camera method provides a basis for future studies investigating rank-dependent leaf shape effects, which can offer an accurate representation of the canopy in FSPMs and improve agricultural decision-making.

ObjectiveOur research aimed to measure the importance of self-esteem in assessing the disease burden in patients with vitiligo, which, according to our knowledge, had not been studied earlier. The purpose of this research study was to expand the state of knowledge regarding the influence of vitiligo on the quality of patients’ life, with a particular focus on their self-esteem. We have formulated the following two hypotheses which include H1: two latent factors characterize the self-esteem of patients with vitiligo; H2: the self-esteem of the patients with Vitiligo is correlated with their life quality, by influencing it to a high degree.MethodsWe have used two validated questionnaires called Rosenberg (Q1), for the evaluation of self-esteem (for proving H1), and Dermatology Life Quality Index (DLQI) (Q2), to measure the health-related quality of life of patients (for proving H2). Both questionnaires with 10 questions were applied to the same set of 114 carefully selected patients with no missing values to questions. An in-depth statistical and reliability analysis was performed on the outcomes provided by Q1, applying a scale and subscale reliability analysis, using the Cronbach’s alpha reliability indicator (Cα). An exploratory analysis called Principal Axis Factoring (PAF) with Oblimin with Kaiser Normalization rotation was applied to prove H1, verifying the assumptions regarding the average variance extracted (AVE) and convergent and discriminant validity (CDV). A scale reliability analysis of outcomes provided by Q2 was performed for proving H2, by calculating Cα. Additionally, a nonparametric correlation analysis was performed, by calculating the Spearman r correlation coefficient between the Rosenberg index and DLQI index, and the 95% confidence interval (CI).ResultsBased on the provided data, the value of Cα obtained in Q1 was 0.84. As a result of applying PAF on Q1, H1 has been proven and two latent factors of self-esteem have been extracted. These factors were named competence (eigenvalue = 4.126; 41.258% of total variance explained) and value (eigenvalue = 1.857; 18.57% of total variance explained). For the two subscales determined by the two factors, we have obtained the Cα values of 0.848 and 0.8, all indicating good reliability. For testing H2, on Q2 data we obtained Cα = 0.914. The Spearman correlation coefficient r = −0.734 (p 

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects – yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

PurposeThis study aimed to use three statistical methods to investigate the inter-rater and intra-rater reliability of the Chinese version of the Short Orientation-Memory-Concentration Test (C-SOMC) for people who have had a stroke.MethodsForty-four participants (31 men and 13 women) with a mean age of 59.05 ± 10.79 years who have had experienced a single episode of stroke were enrolled in this study. To determine the inter-rater reliability of the C-SOMC, the test was administered to each participant on the same day by two raters (A and B) who each had more than 1 year of work experience. To determine intra-rater reliability, rater B re-evaluated 36 of the 44 participants with the C-SOMC on the subsequent day. Intraclass correlation coefficients (ICCs), paired-samples t-tests, and Bland-Altman plots were used to analyze the inter-rater and intra-rater reliability.ResultsThe evaluation of inter-rater reliability for the total score and item 1, 4, 5, 6, and 3/7 showed respective ICCs of 0.959, 0.918, 1.000, 0.942, 0.905 and 0.913, indicating excellent inter-rater reliability for the C-SOMC. Item 2 had an ICC of 0.796, indicating moderate to good inter-rater reliability. The evaluation of intra-rater reliability showed an ICC of 0.978 for the total score, and respective ICCs of 1.000, 1.000, 1.000, 0.968, 0.973 and 0.929 for the individual items, indicating excellent intra-rater reliability for the C-SOMC. The paired-samples t-test for the C-SOMC showed no statistically significant differences (P > 0.05) between ratings made by two different raters or by the same rater on separate occasions. The minimal detectable change value at the 95% confidence threshold (MDC95) of the SOMC score was found to be 2.14. Bland-Altman plots showed a mean difference of 0.02 and 95% limits of agreement (95% LOA) ranging from −3.86 to 3.90 for the inter-rater measurements and a mean difference of 0.33 and 95% LOA of −2.71 to 3.37 for the intra-rater measurements.ConclusionsThe C-SOMC demonstrated excellent inter-rater and intra-rater reliability when administered to people who have had a stroke. The C-SOMC may be used to screen for cognitive impairment in people who have had a stroke.

Systematic reviews play a crucial role in evidence-based practices as they consolidate research findings to inform decision-making. However, it is essential to assess the quality of systematic reviews to prevent biased or inaccurate conclusions. This paper underscores the importance of adhering to recognized guidelines, such as the PRISMA statement and Cochrane Handbook. These recommendations advocate for systematic approaches and emphasize the documentation of critical components, including the search strategy and study selection. A thorough evaluation of methodologies, research quality, and overall evidence strength is essential during the appraisal process. Identifying potential sources of bias and review limitations, such as selective reporting or trial heterogeneity, is facilitated by tools like the Cochrane Risk of Bias and the AMSTAR 2 checklist. The assessment of included studies emphasizes formulating clear research questions and employing appropriate search strategies to construct robust reviews. Relevance and bias reduction are ensured through meticulous selection of inclusion and exclusion criteria. Accurate data synthesis, including appropriate data extraction and analysis, is necessary for drawing reliable conclusions. Meta-analysis, a statistical method for aggregating trial findings, improves the precision of treatment impact estimates. Systematic reviews should consider crucial factors such as addressing biases, disclosing conflicts of interest, and acknowledging review and methodological limitations. This paper aims to enhance the reliability of systematic reviews, ultimately improving decision-making in healthcare, public policy, and other domains. It provides academics, practitioners, and policymakers with a comprehensive understanding of the evaluation process, empowering them to make well-informed decisions based on robust data.