Whilst the media and other data sources track reputational and ESG behaviours, NGOs drive them. Changes in government policy, corporate strategic direction or consumer spending habits are frequently a consequence of collective and sustained NGO campaigning.

Hence, tracking NGO signals presents multiple opportunities: - Early discovery of ESG themes that will shape future government, corporate and consumer behaviour. - Early warning of sector and corporate behaviours that are likely to erode shareholder value - Identification of positive sector and corporate behaviours that build shareholder value.

Where other ESG signals are limited to self-reported corporate data, and further limited to those companies compelled to report, NGO data remains an independent source spanning all corporations, regardless of ownership structure.

SIGWATCH is the only data provider to focus exclusively on this critical market signal. SIGWATCH tracks over 9,000 NGOs, with 60,000+ NGO signals spanning the last 10 years, targeting over 18,000 corporations. By going straight to source, rather than media, SIGWATCH’s provides the most extensive and timely NGO data feed on the market.

All NGO signals are tagged, taxonomized and quantified with the end user having access to either scores and/or underlying signal. NGO campaigns are assessed both by sector and by entity – our robust and transparent methodology adjusts signal score based on factors such as NGO influence, signal sentiment and prominence. Tagging includes Tickers (where applicable), ISINs and FIGI codes.

SIGWATCH is used for several use cases: - By quant funds in the search for fresh alpha - By ESG funds for thematic research, screening and monitoring - By corporations for reputation management and third-party monitoring.

SIGWATCH provides both a desktop service and data feeds with several transfer options. Our archive data spans 10+ years, with new signals added daily. The desktop service provides deep analytics capability, facilitating early detection of emerging ESG themes and sector and corporate performance with respect to ESG and broader reputational-impacting activities.

SIGWATCH is offered as multiple products focusing on sector or corporate signals with the option to take just the quantitative assessment or full access to the underlying data.

About this product This specific product provides all quantitative NGO scores by sector. This is offered as a live, daily data service with the facility to choose specific sectors. Historic data can also be purchased, by year.

Snapshot img

Sports Betting Market Size 2025-2029

The sports betting market size is forecast to increase by USD 221.1 billion, at a CAGR of 12.6% between 2024 and 2029.

The market is experiencing dynamic growth, driven by the digital revolution and the emergence of machine learning technologies. These advancements enable more accurate predictions and personalized betting experiences for consumers, creating a competitive edge for market participants. Popular betting options include football (soccer), basketball, tennis, horse racing, cricket, and various other sports events. However, this market landscape is not without challenges. Stringent government regulations and restrictions pose significant obstacles, requiring companies to navigate complex legal frameworks and comply with evolving policies.
As the industry continues to evolve, staying informed of regulatory changes and adapting to technological advancements will be crucial for market success. Companies that effectively balance innovation and regulatory compliance will be well-positioned to capitalize on the growing opportunities in the market.

What will be the Size of the Sports Betting Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, with dynamic market activities shaping its various sectors. Artificial intelligence (AI) is increasingly being integrated into promotional campaigns, enhancing user experience through personalized recommendations and real-time analysis. Spread betting, a popular form of wagering, employs advanced statistical modeling and risk management techniques. Problem gambling remains a significant concern, with player protection measures such as responsible gambling initiatives and KYC procedures being implemented. Betting odds are visualized through data visualization tools, enabling users to make informed decisions. Live streaming and in-play betting provide real-time updates, while API integration and odds comparison tools facilitate seamless data access.

Machine learning algorithms are used for fraud detection and customer segmentation, ensuring secure payment gateways and AML compliance. Bonus offers and loyalty programs are employed as customer acquisition and retention strategies. Data analytics and betting algorithms enable efficient risk management and effective marketing campaigns. Data feeds from sports data providers are crucial for accurate betting odds and real-time score updates. First goalscorer and correct score bets add excitement to the betting experience. Prop bets and Asian handicap betting cater to diverse user preferences. Live score updates and game integrity are ensured through rigorous security protocols and data encryption.

Pre-match betting and futures betting offer opportunities for long-term investment. Ongoing market activities and evolving patterns underscore the continuous dynamism of the market.

How is this Sports Betting Industry segmented?

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

Platform

  Online
  Offline


Type

  Basketball
  Horse riding
  Football
  Others


Betting Type

  Fixed Odds Wagering
  Exchange Betting
  Live/In-Play Betting
  eSports Betting


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    India
    Japan


  Middle East and Africa

    UAE


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Platform Insights

The online segment is estimated to witness significant growth during the forecast period.

The online market is experiencing notable expansion, fueled by technological advancements and favorable regulatory shifts. Key drivers of this growth include the expanding betting market due to continuous innovation in online channels, the increasing availability of mobile platforms with the widespread use of the Internet and smartphones, and the structural migration of customers from retail to online betting in emerging markets. Improvements in platform quality and user experience, particularly through betting applications, further enhance the appeal of online betting. With digitalization on the rise and smartphone penetration increasing, regions such as APAC and MEA present significant opportunities for growth in the online sports betting sector.

Technological advancements have also brought about the integration of various features, such as machine learning algorithms for risk management and player protection, responsible gambling initiatives, API integration, and odds comparison tools. In-play betting, live sc

This dataset (n=982, vars=196) contains records for all children under 5 years of age in the sampled households. It includes data from Module I for children’s anthropometry and infant and young child feeding practices. Anthropometry Z-scores were calculated in SAS during the data management process, using the World Health Organization (WHO) SAS “igrowup” package. The unique identifiers for this file are pbs_id + idcode.

ABSTRACT This study evaluated the apparent digestibility coefficients (ADC) of essential (EAA) and non-essential (NEAA) amino acids of 13 ingredients for tambaqui (Colossoma macropomum) diets. Proteic and energetic ingredients were analyzed separately. The trial with energetic and proteic ingredients were arranged in a randomized block design, with four replicates: energetic ingredients (corn, wheat bran, broken rice, and sorghum) with four treatments, whereas proteic ingredients (corn gluten meal, soybean meal, poultry byproduct meal, salmon meal, fish meal [tilapia processing residue], wheat gluten meal, feather meal, cottonseed meal, and alcohol yeast [spray dried]) with nine treatments. Each block was considered as one round of fecal collection. A total of 420 tambaqui juveniles (mean initial weight: 70±8.58 g) were used. Among energetic ingredients, corn (94.6%) and wheat bran (91.9%) had the highest ADCEAA, followed by broken rice (75.7%), and sorghum (72.8%). On average, ADCEAA and ADCNEAA values of proteic ingredients were 79.5-98.5%, except for alcohol yeast (ADCEAA: 68.4 and ADCNEAA: 76.7%). Tryptophan was the first limiting amino acid in most ingredients tested and had the lowest chemical scores (0.06-0.51), except for wheat bran, corn gluten meal, and soybean meal, in which lysine was the first limiting amino acid. Soybean meal had the highest digestible essential amino acid index (EAAI: 1.02) and the most balanced amino acid profile, whereas wheat gluten meal had the lowest EAAI (0.48). Overall, tambaqui was very efficient to digest proteic and energetic ingredients.

1. OC Waze Partner Hub GeoRSS Cumulative Alert Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals. OC Waze Partner Hub data provide information about traffic jams and events that affect road conditions, either from drivers using Waze, a.k.a. Wazers, or from external sources. Wazers may issue reports from the location at which they are currently located or, if no longer at the location, within 30 minutes after the event occurred. We are also able to provide automatic alerts for what we call Unusual Traffic (or Irregularities) - incidents that affect a large number of users and fall outside the normal traffic patterns for a given day and time.Waze generates traffic jam information by processing the following data sources:Data: includes all traffic data reported by Waze users through the Waze mobile application. Reliability: Each alert gets a reliability score based on other user reactions (‘Thumbs up’, ‘Not there’ etc.) and the level of the reporter (Wazers gain levels by contributing to the map, starting at level 1 and reaching up to level 6. The higher the level, the more experienced and trustworthy the Wazer.) The score (0-10) indicates how reliable the report is. Confidence: Each alert gets a confidence score based on other user reactions (‘Thumbs up’, ‘Not there’). The score ranges between 0 and 10. A higher score indicates more positive feedback from Waze users.2. OC Waze Partner Hub GeoRSS Cumulative Traffic Jam Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals.The traffic jams feed includes data gathered in real time about traffic slowdowns on specific road segments. Waze generates traffic jam information by processing the following data sources: GPS location-points sent from user phones (users who drive while using the app) and calculations of the current average speed vs. free-flow speed (maximum speed measured on the road-segment). For Unusual traffic (irregularities) Waze uses historic average speeds (on 30 minute time-slots). User generated reports - reports shared by Waze users who encounter traffic jams. These appear as regular alerts, and also affect the way we identify and present traffic jams.Original data provided by Waze App. Learn more at Waze.com.

In tropical countries subject to significant quantitative and qualitative variations in the availability of fodder during the year, the dissatisfaction of feed needs is a frequent situation for animals raired in grazing systems without a sufficient supplementation with feed concentrates. The Body Condition Scoring (BCS) is a useful way to assess the state of body reserves (subcutaneous fat, muscle mass) which reflects the animal's production (growth, milk, work) and reproduction capacities. The evaluation of the body reserves of an animal through the scoring of their body condition is important to adjust their diet and appreciate their general state of health, as well as their reproductive and production capacity (meat, milk, work, etc.). Managing body reserves is one way of responding to variability in quality and access to feed resources. In females, reserves play an important buffer role during lactation. They can make up for insufficient intakes from the ration. Indeed, the level of production depends on the nutrients provided by the feeds, but also on the animal's body reserves when the feed-based supplies do not fully cover their needs, especially during the dry season. The BCS impacts the interval between two calving. Overly lean cows show a delay in the return of heat after calving, the direct consequence of which is the increase in the parturition interval and consequently a decrease in herd productivity. The assessment of these reserves through Body Condition Scoring (BCS) represents a management tool for livestock farmers, agricultural advisors and livestock development stakeholders. BCS is a simple, inexpensive and fast method. Several animals can be scored in one session. It allows to compare the BCS of individuals or herds: 1) raised in different production systems or environments, 2) or, during different seasons (dry season and rainy season). BCS can be used as a tool for monitoring and alerting the nutritional level of domestic animal populations. To do this, BCS alert thresholds and a BCS collection and monitoring system must be defined on reasoned samples of animal populations. It is an easy-to-use field tool. However, a good mastery of the BCS grid as well as a regular practice of scoring are necessary to obtain precise and reproducible ratings. It can also be used as a tool for monitoring and alerting the nutritional level of animal populations. Thus, the farmer can be called at any time to intervene on the feed ration and / or the health of the animal. The BCS grid currently used in the intervention area of the Beef Cattle 2 project, does not seem to have been developed with reference to the breeds of buffaloes present in Northern Vietnam. This is the reason why we propose this BCS grid adapted to Buffaloes (Bubalus bubalis). In 2020, Vall proposed a standardized BCS scoring system for tropical livestock animals for large animals (cattle, camels), small animals (sheep, and goats) and for donkeys, and horses. This document presents this BCS system applied to the Buffalo.

Title: Peer-to-peer dialogue about teachers’ written feedback enhances students’ understanding on how to improve writing skills A short description of the study set-up: Second-year university students (N=84) participated in a mixed-method study that included questionnaires and focus groups. The intervention comprised face-to-face dialogue in small groups about the participants’ written peer feedback on a draft report. Instruments Questionnaires A pre-intervention questionnaire before the start of the face-to-face dialogue measured students’ beliefs about written peer feedback (part 1). For this purpose, a validated questionnaire by Huisman, Saab, van Driel, et al. (2019) was used to measure four components: 1) degree to which peer feedback is perceived as meaningful and useful (3 items), 2) the degree to which peer feedback is considered an important skill (3 items), 3) confidence in quality of provided peer feedback (2 items) and 4) confidence in quality of received peer feedback (2 items). A five-point Likert scale was employed, ranging from 1 (=‘Completely disagree’ or ‘Completely not applicable to me’) to 5 (=‘Completely agree’ or ‘Completely applicable to me’). In part 2, students rated the presence of written peer feedback in terms of feed-up, feed-back and feed-forward information for which an adjusted version of a validated questionnaire by De Kleijn, Bronkhorst, Meijer, Pilot, and Brekelmans (2016) was used. This part of the questionnaire was also on a five-point Likert scale, ranging from 1 (=‘Agree not at all’) to 5 (=‘Agree a lot’) and contained four items about Feed up, six items about Feed back and five items about Feed forward. The pre-intervention questionnaire also measured the overall instructiveness of the written feedback on a 10-point scale (1=lowest, 10=highest). A post-intervention questionnaire measured students’ perception of improved understanding of the written feedback through face-to-face peer dialogue and the quality of this dialogue in terms of overall instructiveness, which was measured on a 10-point scale. The post-intervention questionnaire also contained items about Feed up (4 items), Feed back (6 items) and Feed forward (5 items). As in the pre-intervention questionnaire, these items were answered on a five-point Likert scale. A pilot study was conducted to test clarity of both pre- and post-intervention questionnaires items. Focus group Students were invited to participate in a focus group, which resulted in two groups of volunteers: N=9 (3 males, 6 females) and N=7 (4 males, 3 females). The participants all originated from different dialogue groups. Semi-structured, post-measurement interviews were conducted to search for explanations as to why dialogue improved students’ understanding and to distinguish important conditions for better understanding. The focus group sessions lasted one hour and were guided by a moderator (first author) while a second member of the research team (fourth author) acted as observer. The moderator and observer did not know the focus group members. Both interviews were audiotaped. Analysis Quantitative analysis Reliability analysis was performed for each subscale of ‘student beliefs’, as well as for Feed up, Feed back and Feed forward. The reliability of the subscales varied from 0.72 to 0.85, which was considered acceptable (Tavakol & Dennick, 2011). For all pre- and post-intervention variables, the median (Mdn) and interquartile range (IQR) was calculated, besides mean (M) and standard deviation (SD). The authors considered a median score equal or above 4.0 (scale 1–5) or 8.0 (scale 1–10) as very positive. A median score equal or below 3.0 (scale 1–5) or 6.0 (scale 1–10) was considered insufficient, while all the other scores were considered to be positive. A non-parametric Wilcoxon signed-rank test was performed to compare scores on ‘Instructiveness of written feedback’ and ‘Instructiveness of face-to-face dialogue’. Non-parametric Wilcoxon signed-rank tests were also performed to compare scores on pre- and post-intervention subscales of Feed up, Feed back and Feed forward. All tests were performed on the 5% level of significance. Qualitative analysis Both focus groups sessions were transcribed verbatim and two authors (moderator and observer) first analysed the transcripts in a theoretically thematic way (Braun & Clarke, 2006). This method involves deductive or top-down analysis, led by the research questions. Making an inventory of phrases related to the explanations and conditions for an improved understanding by dialogue led the analysis of the transcripts. To this end, in the first phase of the analysis and in an iterative process of three separate rounds, both authors formulated a set of themes comprising explanations and conditions. In the next phase, all authors discussed the formulated themes and reached consensus through discussion. Explanation of the data files: what data is stored in what file? The data files contain 84...

Tempe’s trust data for this measure is collected every month and comes from an average of the “Fairness,” “Respect,” and “Voice” results from the monthly administred Police Sentiment Survey. There are 7 questions which feed into these results. Benchmark data is from cohorts of communities with similar characteristics such as size, population density, and region. This data is collected every calendar year quarter via a reoccurring report. This data table is for the Police Trust Score performance measure.Additional Information:Source: Zencity Contact: Adam SamuelsData Source Type: ExcelPreparation Method: Take the scores for the 3 result groupings of "Fairness," "Respect" and "Voice" each month from the Police Sentiment Survey and average them to get the trust score. This score includes the average of the top two results from each of the 7 feeder questions which comprise the three groupings listed above. These months are then averaged to get the quarterly score.Publish Frequency: MonthlyPublish Method: ManualData Dictionary

Data used to feed Sustainability Compliance Map. This data comes from the NYC Department of Finance, the US EPA's Portfolio Manager, and grading metric based on Local Law 95 of 2019.

Social media platforms use short, highly engaging videos to catch users’ attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions. We conducted a between-subjects experiment (𝑁 = 60) investigating the impact of engaging with TikTok, Twitter, and YouTube while performing a Prospective Memory task (i.e., executing a previously planned action). The study required participants to remember intentions over interruptions. We found that the TikTok condition significantly degraded the users’ performance in this task. As none of the other conditions (Twitter, YouTube, no activity) had a similar effect, our results indicate that the combination of short videos and rapid context-switching impairs intention recall and execution. We contribute a quantified understanding of the effect of social media feed format on Prospective Memory and outline consequences for media technology designers not to harm the users’ memory and wellbeing. Description of the Dataset Data frame: The ./data/rt.csv provides the data frame of reaction times. The ./data/acc.csv provides the data frame of reaction accuracy scores. The ./data/q.csv provides the data frame collected from questionnaires. The ./data/ddm.csv is the learned DDM features using ./appendix2_ddm_fitting.ipynb, which is then used in ./3.ddm_anova.ipynb. Figures: All figures appeared in the paper are placed in ./figures and can be reproduced using *_vis.ipynb files.

The improved growth performance of calves at weaning results from an effective pre-weaning feeding strategy. The type and pasteurization process of liquid feed are among the most variable feeding practices affecting calves’ growth and health. In previous studies that compared waste milk (WM) vs. milk replacer (MR), little consideration has been given to the variations in chemical composition and feeding behavior between them, and there has been a lack of justification for the crude protein: metabolizable energy (CP:ME) ratio adopted. Hence, this study aimed to evaluate the effects of feeding pasteurized WM or MR differing in energy source (fat vs. lactose, respectively) with similar CP:ME ratio on intake, growth, feeding behavior, and health of newborn Holstein calves. Thirty-two male calves (4-d-old; 40.0 ± 0.58 kg BW) were assigned to the trial and randomly allocated to each liquid feed diet (WM or MR). Calves were housed in individual pens with free access to starter feed and fresh water. Calves were weaned on d 61 and assessed until d 101 as the postweaning period. WM-fed calves had greater total nutrient intake (DM, CP, EE, and ME), weight gain, final BW, skeletal growth parameters, and feed efficiency (d 30). Calves WM-fed sorted less against particles retained on the 2.36-mm sieve but more against particles retained on the sieve of 0.6 mm. In WM-fed calves, the sorting index decreased for feedstuff retaining on the bottom pan compared with MR-fed calves. Irrespective of the type of the liquid feed, all calves sorted for particles retaining on the sieve of 4.75 mm and the bottom pan, and against the particles that were retained on the sieves of 2.36- (MR-fed calves only), 1.18- and 0.6-mm. Starter feed nutrient intake and particle size intake from the sieves of 4.75-, 2.36-, and 1.18-mm increased in WM- vs. MR-fed calves. Eating rate and meal size but not meal frequency and length were greater in WM-fed calves, leading to higher pre- and post-weaning starter feed intake. Calves WM-fed spent less time eating and standing but more time ruminating and lying than MR-fed calves. Calves WM-fed had a lower likelihood of having elevated general appearance (score ≥2; hazard ratio = 2.79), diarrhea (score ≥3; hazard ratio = 1.35), and pneumonia (hazard ratio = 4.77). Calves WM-fed experienced shorter days with elevated general appearance, diarrhea, and pneumonia. Overall, feeding WM led to increased starter feed intake by boosting the eating rate and meal size, promoting greater growth than MR. Additionally, compared with MR, WM feeding increased time spent ruminating and lying and reduced susceptibility to diarrhea and pneumonia.

1. OC Waze Partner Hub GeoRSS Cumulative Alert Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals. OC Waze Partner Hub data provide information about traffic jams and events that affect road conditions, either from drivers using Waze, a.k.a. Wazers, or from external sources. Wazers may issue reports from the location at which they are currently located or, if no longer at the location, within 30 minutes after the event occurred. We are also able to provide automatic alerts for what we call Unusual Traffic (or Irregularities) - incidents that affect a large number of users and fall outside the normal traffic patterns for a given day and time.Waze generates traffic jam information by processing the following data sources:Data: includes all traffic data reported by Waze users through the Waze mobile application. Reliability: Each alert gets a reliability score based on other user reactions (‘Thumbs up’, ‘Not there’ etc.) and the level of the reporter (Wazers gain levels by contributing to the map, starting at level 1 and reaching up to level 6. The higher the level, the more experienced and trustworthy the Wazer.) The score (0-10) indicates how reliable the report is. Confidence: Each alert gets a confidence score based on other user reactions (‘Thumbs up’, ‘Not there’). The score ranges between 0 and 10. A higher score indicates more positive feedback from Waze users.2. OC Waze Partner Hub GeoRSS Cumulative Traffic Jam Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals.The traffic jams feed includes data gathered in real time about traffic slowdowns on specific road segments. Waze generates traffic jam information by processing the following data sources: GPS location-points sent from user phones (users who drive while using the app) and calculations of the current average speed vs. free-flow speed (maximum speed measured on the road-segment). For Unusual traffic (irregularities) Waze uses historic average speeds (on 30 minute time-slots). User generated reports - reports shared by Waze users who encounter traffic jams. These appear as regular alerts, and also affect the way we identify and present traffic jams.Original data provided by Waze App. Learn more at Waze.com.

This dataset includes CSV files that contain IDs and sentiment scores of the tweets related to the COVID-19 pandemic. The real-time Twitter feed is monitored for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. The oldest tweets in this dataset date back to October 01, 2019. This dataset has been wholly re-designed on March 20, 2020, to comply with the content redistribution policy set by Twitter. Twitter's policy restricts the sharing of Twitter data other than IDs; therefore, only the tweet IDs are released through this dataset. You need to hydrate the tweet IDs in order to get complete data.

Data of performance (mean ± SE) and feeding behavior (median, min—max) of growing and finishing pigs according to treatment, gender, and period of mixing.

OC Waze Partner Hub GeoRSS Cumulative Alert Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals. OC Waze Partner Hub data provide information about traffic jams and events that affect road conditions, either from drivers using Waze, a.k.a. Wazers, or from external sources. Wazers may issue reports from the location at which they are currently located or, if no longer at the location, within 30 minutes after the event occurred. We are also able to provide automatic alerts for what we call Unusual Traffic (or Irregularities) - incidents that affect a large number of users and fall outside the normal traffic patterns for a given day and time.Waze generates traffic jam information by processing the following data sources:Data: includes all traffic data reported by Waze users through the Waze mobile application. Reliability: Each alert gets a reliability score based on other user reactions (‘Thumbs up’, ‘Not there’ etc.) and the level of the reporter (Wazers gain levels by contributing to the map, starting at level 1 and reaching up to level 6. The higher the level, the more experienced and trustworthy the Wazer.) The score (0-10) indicates how reliable the report is. Confidence: Each alert gets a confidence score based on other user reactions (‘Thumbs up’, ‘Not there’). The score ranges between 0 and 10. A higher score indicates more positive feedback from Waze users.Original data provided by Waze App. Learn more at Waze.com.