Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD)
10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

The Port Moody Ecological Society is a 100% volunteer based organization whose mandate is to operate a salmon hatchery and water quality laboratory located in the City of Port Moody. We have been working tirelessly for over 30 years to foster a love of our environment through education and outreach in the Tri-Cities. Water quality data of our nearby salmon streams are collected weekly for on-site laboratory analysis.

We compile raw data from the Datastream database for all stocks traded on the Tokyo Stock Exchance, Osaka Exchange, Fukuoka Stock Exchange, Nagoya Stock Exchange and Sapporo Securities Exchange. Particularly, we collect the following data series, on a monthly basis: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), and (iv) primary SIC codes. Following Griffing et al. (2010), we exclude non-common equity securities from Datastream data. Additionally, we remove all companies with less than 12 observations in RI series for the period under analysis. Hence, our sample comprises 5,627 stocks, considering all companies that started trading or were delisted in the period under analysis. We use the three-month Treasury Bill rate for Japan, as provided by the OECD database, as a proxy for the risk-free rate. Accordingly, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV_M: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_20_Portfolios_MOM_M: Monthly returns for 20 momentum portfolios rebalanced in June of each year. (Raw data source: Datastream database)
3. Japan_61_Portfolios_SECTOR_M: Monthly returns for 61 industry portfolios. (Raw data source: Datastream database)
4. Japan_RF_M: Three-month Treasury Bill rate for Japan. (Raw data source: OECD)
5. Japan_C_Q: Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
6. Japan_Trade_Y: Trade openness for Japan, as measured by the variation rate of exports plus imports. (Raw data source: OECD)
7. Japan_RD_Y: Variation rate of R&D investment for Japan. (Raw data source: OECD)
8. Japan_IK_Y: Investment-capital ratio for Japan., determined using the methodology suggested by Cochrane (1991) (Raw data source: OECD)
9. Japan_CCI_M: Consumer confidence index for Japan. (Raw data source: OECD)

REFERENCES:

Cochrane, J.H. (1991), Production-based asset pricing and the link between stock returns and economic fluctuations. The Journal of Finance, 46, 209-237. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

We compile raw data from the Datastream database for all stocks traded on the Spanish equity market. Particularly, we compile the following data series: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) dividend yield (DY series), (vii) price-to-earnings ratio (PE series), and (viii) effective tax rate (WC08346 series). We use the filters suggested by Griffin, Kelly, and Nardari (2010) for the Datastream database to exclude assets other than ordinary shares from our sample. Hence, our sample comprises 443 companies, including all firms that started trading within the time interval under study, as well as those that were delisted. As a proxy for the risk-free rate, we use the three-month Treasury Bill rate for Spain, as provided by the OECD. Accordingly, the dataset comprises the following series:

1. Spain_9_Portfolios_SIZE_BEME: Monthly returns for 9 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Spain_9_Portfolios_DY_PE: Monthly returns for 9 dividend yield-price-to-earnings ratio, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Spain_9_Portfolios_SIZE_TR: Monthly returns for 9 size-effective tax rate portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Spain_FF_3_Factors: Monthly returns for the constituents of the three classic factors of Fama and French, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Spain_FF_5_Factors: Monthly returns for the constituents of the five factors of Fama and French, following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Spain_RF: Three-month Treasury Bill rate for Spain. (Raw data source: OECD)
7. Spain_Avg_Tax_Rate: Value-weighted effective tax rate paid by companies traded in Spain. (Raw data source: Datastream database)

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

These data are collected as part of the Fisheries and Oceans Canada (DFO) Pacific Science Enterprise Centre (PSEC) Community Stream Monitoring (CoSMo) project, which is a collaborative monitoring initiative that strives to produce quality, long-term datasets for use in resource management, research, and stewardship. We currently monitor streams in southwest BC in the region spanning from Howe Sound, south to the USA border, and east to Abbotsford, primarily using automated dataloggers. This project is made possible thanks to the many stewardship groups in the region whose dedicated volunteers are committed to protecting, conserving, and educating the public about their local streams.

We rely on volunteers to download the data from the dataloggers. If you download and/or use the CoSMo data, we would appreciate if you could send a quick email to Nikki Kroetsch (Nikki.Kroetsch@dfo-mpo.gc.ca) with a brief (1-2 sentence) description of what you will be using the data for. Though not a requirement for using the data, this information reassures our volunteers that their data collection efforts are appreciated and worthwhile, which motivates them to continue to help collect the data.

Partner Organizations: Alouette River Management Society, Cariboo Heights Forest Preservation Society, Cougar Creek Streamkeepers, City of Surrey, Bowen Island Fish and Wildlife Club, West Vancouver Streamkeepers, Stoney Creek Environment Committee, City of Port Moody, Capilano Golf/Country Club, Eagle Creek Streamkeepers, North Shore Streamkeepers, Nicomekl Enhancement Society, Hoy/Scott Watershed Society, Hyde Creek Watershed Society, WaterWealth Project, Univ. of BC, Burrard Inlet Marine Enhancement Society, Yorkson Watershed Enhancement Society, Johnston Heights Secondary School (Surrey), Seymour Salmonid Society, Still Creek Streamkeepers, PSEC staff

Dedicated volunteers (not associated with an organization) who steward Carlson Creek, Mosquito Creek, McDonald Creek, and McNally Creek.

Datasys provides one of the largest consumer data sets with over 350M Consumer Profiles, having 500+ demographic and psychographic key elements, and 4,000+ online behavior segments, with MAIDs matched to PIl and other identifiers.

One of the largest proprietary deterministic data sets, composed of exclusive opt-in information and continually enriched in real-time by thousands of offline and online predictive signals.

We compile raw data from the Datastream database for all stocks traded on the Tokyo Stock Exchance, Osaka Exchange, Fukuoka Stock Exchange, Nagoya Stock Exchange and Sapporo Securities Exchange. Particularly, we collect the following data series, on a monthly basis: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), and (iv) dividend yield (DY series). Following Griffing et al. (2010), we exclude non-common equity securities from Datastream data. Hence, our sample comprises 5,627 stocks, considering all companies that started trading or were delisted in the period under analysis. We use the three-month Treasury Bill rate for Japan, as provided by the OECD database, as a proxy for the risk-free rate. Accordingly, the dataset comprises the following series:

1. Japan_25_portfolios_size-BEME_M: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_20_momentum_portfolios_M: Monthly returns for 20 momentum portfolios rebalanced in June of each year. (Raw data source: Datastream database)
3. Japan_3_Factors_M: Monthly returns for the constituents of the three classic factors of Fama and French, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Japan_Consumption_Q: Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
5. Japan_Dividend_yield_M: Value-weighted dividend yield for the Japanese equity market. (Raw data source: Datastream database)
6. Japan_epsilon_DY_Q: Errors provided by the regression of consumption growth on the value-weighted dividend yield for Japan. (Raw data source: Datastream database and OECD)
7. Japan_RF_M: Three-month Treasury Bill rate for Japan. (Raw data source: OECD)

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.

This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.

This is the harmonized aoscpc datastream with additional QC applied to the aoscpc.a1 datastream

The objective of the study is to explore the relationship between country governance practices along with political stability and Economic policy uncertainty, and stock market performance of two different economies, Pakistan and Kurdistan region of Iraq. To meet our objectives, we used the 25 years past data from 1996 to 2021. Data is collected from the DataStream database. The regression analysis is used as the method of estimation for linear and moderation effect. Our results show that regulatory quality, rules of law and political stability has significant positive relationship with stock market performance of Pakistan, but all the governance indicators have significant positive relationship with stock market performance of the Kurdistan Region of Iraq. Moreover, political stability has significant moderating impact between the governance practices and the performance of the stock markets of both economies indicating that the governance practices perform well with the political stability that leads to rise in the stock market indices of selected countries. Economic policy uncertainty has significant negative moderation impact due to creating the risk in both economies that decrease the performance of the stock markets of the selected economies. Finally, our study advocated some implications for the investors to increase their confidence on the stock of high political stability and low economic policy uncertainty economies. Government can take significant measures to control the uncertainty of the policy and portfolio managers can adjust their risk on the ground of the political stability and efficient governance practices countries.

These are raw ship and buoy (moored and drifting) observations provided by World Meteorological Organization (WMO) Member States. Data are transmitted through the WMO Global Telecommunication System (GTS) and picked up by the National Weather Service's Telecommunication Gateway (NWS-TG) where it is made available to NCEI . Member States are transitioning from ASCII to BUFR formats and this collection will assure that data in both formats are archived for future use.

We monitor a number of mentions and their sentiment on Reddit, Twitter, and Telegram for the top 100 major crypto coins by liquidity.

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This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.

The KAZR-ARSCL VAP provides cloud boundaries and best-estimate time-height fields of radar moments. The VAP merges corrected KAZR moments from all active radar modes with cloud base and cloud mask observations from the micropulse lidar (MPL), cloud base from the ceilometer, as well information from soundings, rain gauge, and microwave radiometer instruments to produce two data streams, one with best-estimate cloud base and cloud layer boundaries, and another which also includes best-estimate time-height fields of radar moments. This DOI is for the data stream that contains all variables produced by the VAP.

Fort Folly Habitat Recovery's water temperature monitoring aims to build a thermal profile for sites in the Petitcodiac River Watershed used in our inner Bay of Fundy Atlantic salmon recovery work. Temperatures are collected through the deployment of Hobo Pendent MX data loggers set to record every 30 minutes. Data collected will be analyzed to determine long term temperature trends and suitable sites for future salmon recovery actions.

Our clickstream data offers unparalleled access to a vast array of global datasets, capturing user interactions across websites, apps, and digital platforms worldwide. With coverage spanning multiple industries and geographies, our data provides detailed insights into consumer behavior, online trends, and digital engagement patterns.

Whether you're analyzing traffic flows, identifying audience interests, or tracking competitive performance, our clickstream datasets deliver the scale and granularity needed to inform strategic decisions. Updated regularly to ensure accuracy and relevance, this robust resource empowers businesses to uncover actionable insights and stay ahead in a dynamic digital landscape.

These are raw radiosonde and pilot balloon observations taken from various locations at various times around the globe transmitted through the National Weather Service Telecommunications Gateway (NWSTG) in a World Meteorological Organization (WMO) Binary Universal Form for the Representation of meteorological data (BUFR) format beginning in May 2017. Variables include Temperature, humidity, Wind direction and speed, pressure, height, elapsed time and position displacement since launch, and some metadata. Vertical and temporal resolution varies.

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-earnings ratio (PE series), and (vii) industry (SECTOR series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations. Accordingly, our sample comprises a total number of 5,212 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.