Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

Ten years of daily data for the Dow Jones Industrial Average (DJIA) market index. Each point of the dataset is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper right corner of the chart.

This dataset provides predicted prices, return on investment (ROI), and sentiment analysis for OriginTrail over a series of dates. The dataset includes predictions for OriginTrail's price, with low, average, and high values, as well as ROI figures for each predicted date. This data helps users forecast market trends for OriginTrail and make informed trading decisions.

This dataset provides predicted prices, return on investment (ROI), and sentiment analysis for Golem over a series of dates. The dataset includes predictions for Golem's price, with low, average, and high values, as well as ROI figures for each predicted date. This data helps users forecast market trends for Golem and make informed trading decisions.

This dataset is about book series and is filtered where the books is Managing for value : achieving high quality at low cost. It has 10 columns such as book series, earliest publication date, latest publication date, average publication date, and number of authors. The data is ordered by earliest publication date (descending).

Data processing by Wolfgang Gloeden, Maritime Klimaüberwachung (KU24), Deutscher Wetterdienst, http://www.dwd.de

We present the first ecosystem-scale methane flux data from a northern Siberian tundra ecosystem covering the entire snow-free period from spring thaw until initial freeze-back. Eddy covariance measurements of methane emission were carried out from the beginning of June until the end of September in the southern central part of the Lena River Delta (72°22' N, 126°30' E). The study site is located in the zone of continuous permafrost and is characterized by Arctic continental climate with very low precipitation and a mean annual temperature of -14.7°C. We found relatively low fluxes of on average 18.7 mg/m**2/d, which we consider to be because of (1) extremely cold permafrost, (2) substrate limitation of the methanogenic archaea, and (3) a relatively high surface coverage of noninundated, moderately moist areas. Near-surface turbulence as measured by the eddy covariance system in 4 m above the ground surface was identified as the most important control on ecosystem-scale methane emission and explained about 60% of the variance in emissions, while soil temperature explained only 8%. In addition, atmospheric pressure was found to significantly improve an exponential model based on turbulence and soil temperature. Ebullition from waterlogged areas triggered by decreasing atmospheric pressure and near-surface turbulence is thought to be an important pathway that warrants more attention in future studies. The close coupling of methane fluxes and atmospheric parameters demonstrated here raises questions regarding the reliability of enclosure-based measurements, which inherently exclude these parameters.

Data processing by Wolfgang Gloeden, Maritime Klimaüberwachung (KU24), Deutscher Wetterdienst, http://www.dwd.de

Average Wage: Urban Non-private: Year to Date: Heilongjiang data was reported at 95,750.000 RMB in Dec 2023. This records an increase from the previous number of 88,235.000 RMB for Dec 2022. Average Wage: Urban Non-private: Year to Date: Heilongjiang data is updated quarterly, averaging 14,465.500 RMB from Jun 2003 (Median) to Dec 2023, with 56 observations. The data reached an all-time high of 95,750.000 RMB in Dec 2023 and a record low of 2,713.000 RMB in Mar 2004. Average Wage: Urban Non-private: Year to Date: Heilongjiang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GC: Average Wage: Region.

The value of the DJIA index amounted to 43,191.24 at the end of March 2025, up from 21,917.16 at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29th of 2008, for instance, the Dow had a loss of 106.85 points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by 81.66 percent in one year, and 1933, year when the index registered a growth of 63.74 percent.

This dataset provides predicted prices, return on investment (ROI), and sentiment analysis for Shiba Inu over a series of dates. The dataset includes predictions for Shiba Inu's price, with low, average, and high values, as well as ROI figures for each predicted date. This data helps users forecast market trends for Shiba Inu and make informed trading decisions.

An improved understanding of life history responses to current environmental variability is required to predict species-specific responses to anthopogenic climate change. Previous research has suggested that cooperation in social groups may buffer individuals against some of the negative effects of unpredictable climates. We use a 15-year dataset on a cooperative-breeding arid-zone bird, the southern pied babbler Turdoides bicolor, to test i) whether environmental conditions and group size correlate with survival of young during three development stages (egg, nestling, fledgling), and ii) whether group size mitigates the impacts of adverse environmental conditions on reproductive success. Exposure to high mean daily maximum temperatures (mean Tmax) during early development was associated with reduced survival probabilities of young in all three development stages. No young survived when mean Tmax > 38°C across all group sizes. Low reproductive success at high temperatures has broad implications for recruitment and population persistence in avian communities given the rapid pace of advancing climate change. That impacts of high temperatures were not moderated by group size, a somewhat unexpected result given prevailing theories around the influence of environmental uncertainty on the evolution of cooperation, suggests that cooperative breeding strategies are unlikely to be advantageous in the face of rapid anthropogenic climate change. An improved understanding of life history responses to current environmental variability is required to predict species-specific responses to anthopogenic climate change. Previous research has suggested that cooperation in social groups may buffer individuals against some of the negative effects of unpredictable climates. We use a 15-year dataset on a cooperative-breeding arid-zone bird, the southern pied babbler Turdoides bicolor, to test i) whether environmental conditions and group size correlate with survival of young during three development stages (egg, nestling, fledgling), and ii) whether group size mitigates the impacts of adverse environmental conditions on reproductive success. Exposure to high mean daily maximum temperatures (mean Tmax) during early development was associated with reduced survival probabilities of young in all three development stages. No young survived when mean Tmax > 38°C across all group sizes. Low reproductive success at high temperatures has broad implications for recruitment and population persistence in avian communities given the rapid pace of advancing climate change. That impacts of high temperatures were not moderated by group size, a somewhat unexpected result given prevailing theories around the influence of environmental uncertainty on the evolution of cooperation, suggests that cooperative breeding strategies are unlikely to be advantageous in the face of rapid anthropogenic climate change.

Methods Study site and system

Fieldwork was conducted at the Kuruman River Reserve (33 km2, KRR; 26°58’S, 21°49’E) in the southern Kalahari. Mean summer daily maximum temperatures at the study site, from 1995-2015, averaged 34.7 ± 9.7°C and mean annual precipitation averaged 186.2 ± 87.5mm [49]. The Kalahari region is characterised by hot summers and periodic droughts [50], with extremely variable rainfall between years [51] and increases in both the frequency and severity of high temperature extremes over the last 20 years [52]. Pied babblers are medium-sized (60–90 g), cooperatively-breeding passerines endemic to the Kalahari where they live in territorial groups ranging in size from 3–15 adults [53]. They breed during the austral summer, from September to March [54]. Pied babbler groups consist of a single breeding pair with subordinate helpers [55], and all adult group members (individuals > 1 year old) engage in cooperative behaviours, including territory defence and parental care [48,54]. Previous research has shown that high temperatures and drought negatively affect many aspects of this species’ ecology, including foraging efficiency, body mass maintenance, and provisioning of young [56–58].

Birds in the study population are marked as nestlings with a unique combination of metal and colour rings for individual identification, and are habituated to observation at distances of 1–5 m [48]. Habituated groups are visited weekly during the breeding season to check group composition and record life history events, including breeding activity.

Data collection

Data were collected for each austral summer breeding season from September 2005–February 2019 (14 breeding seasons in total).

Nest life history data

Nest monitoring (location of nests, determination of incubation, hatch, and fledge or failure dates, records of group size and brood size followed Ridley & van den Heuvel [33]. Nests were located by observing nest-building, and incubation start, hatch and fledge dates were determined by checking nests every two to three days. Breeding attempts were considered to have failed when nests were no longer attended, or when dependent fledglings were not seen on two consecutive visits. Failure dates were calculated as the midpoint between the date of the last pre-fail nest/group check and the date when the nest was no longer attended or the fledgling was missing. In most cases, it was not possible to determine the proximate cause of nest failure or death, although common causes of nest failure in this species include predation, abandonment, and nestling starvation [53,59].

Group size (number of adults present in the group; range: 2–10, mean = 4.2 ± 1.5) was recorded for each nest incubated. Brood size was recorded 11 days after hatching (range: 1–5 nestlings, mean = 2.7 ± 0.8), when nestlings were ringed. We defined early development as the period between initiation of incubation and nutritional independence at 90 days of age [48]. Average time from initiation of incubation to hatching is 14 ± 1.2 days. Average time between hatching and fledging is 15.4 ± 1.7 days. Pied babbler are nutrionally independent (receiving < 1 feed per hour) by 90 days of age [48].

Sexing & nestling mass

Pied babblers are sexually monomorphic (Ridley, 2016) and molecular sexing was used to determine the sex of individuals (sensu Fridolfsson & Ellegren 1999). Blood samples were collected by brachial venipuncture and stored in Longmire’s lysis buffer. Nestlings were ringed, blood sampled, and weighed to 0.1 g on a top-pan scale 11 days post-hatching (Mass11).

Temperature and rainfall

Daily maximum temperature (°C) and rainfall (mm) data were collected from an on-site weather station (Vantage Pro2, Davis Instruments, Hayward, USA). Missing weather data from 2009, 2010, and 2011 were sourced from a nearby South African Weather Services weather station (Van Zylsrus, 28 km away), which produces significantly repeatable temperature measurements (Lin’s concordance correlation coefficient rc = 0.957, 95 % CI: 0.951–0.962), and moderately repeatable rainfall measurements (rc = 0.517, 95 % CI: 0.465–0.566) in comparison with the on-site weather station. Absolute differences in measured rainfall were small (average difference = 0.045 ± 3.075 mm, 95 % CI = -5.981–6.072 mm), suggesting that both weather stations adequately detected wet vs. dry periods.

Daily minimum (Tmin) and maxium (Tmax) temperatures, daily temperature variation (Tmax - Tmin), were averaged for each development stage: incubation (mean TminInc, mean TmaxInc, mean TvarInc), nestling (mean TminBrood, mean TmaxBrood, mean TvarBrood), and fledgling (mean Tmin90, mean Tmax90, mean Tvar90). Rainfall was summed for the 60 days prior to initiation of incubation (Rain60), and for the period between fledging and independence (Rain90).

Long term historical dataset of the NASDAQ Composite stock market index since 1971. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

The “Tesla Stock Price Data (Last One Year)” dataset is a comprehensive collection of historical stock market information, focusing on Tesla Inc. (TSLA) for the past year. This dataset serves as a valuable resource for financial analysts, investors, researchers, and data enthusiasts who are interested in studying the trends, patterns, and performance of Tesla’s stock in the financial markets.It consists of 9 columns referring to date, high and low prices, open and closing value, volume, cumulative open and of course changing of price.At a first glance in order to better understand the data we should plot the time series of each attribute.The cumulative Open Interest(OI) is the total open contracts that are being held in a particular Future or Call or Put contracts on the Exchange. We can see that the biggest drop of the stock happened in January of 2023 and after 5 to 6 months it regained its stock value round the summer of the same year with opening and closing price around 300.As a next step we are going to plot some more plots in order ro better understand the relation between our target column(change price) with every other attribute. In order to interpret the results:

Linear Regression:

Mean Absolute Error (MAE): 6.28 This model, on average, predicts the “Price Change” within approximately 6.28 units of the true value. Mean Squared Error (MSE): 52.97 MSE measures the average of squared differences, and this value suggests some variability in prediction errors. Root Mean Squared Error (RMSE): 7.28 RMSE is the square root of MSE and is in the same units as the target variable. An RMSE of 7.28 indicates the typical prediction error. R-squared (R2): 0.0868 R-squared represents the proportion of the variance in the target variable explained by the model. An R2 of 0.0868 suggests that the model explains only a small portion of the variance, indicating limited predictive power. Decision Tree Regression:

Mean Absolute Error (MAE): 9.21 This model, on average, predicts the “Price Change” within approximately 9.21 units of the true value, which is higher than the Linear Regression model. Mean Squared Error (MSE): 150.69 The MSE is relatively high, indicating larger prediction errors and more variability. Root Mean Squared Error (RMSE): 12.28 RMSE of 12.28 is notably higher, suggesting that this model has larger prediction errors. R-squared (R2): -1.598 The negative R-squared value indicates that the model performs worse than a horizontal line as a predictor, indicating a poor fit. Random Forest Regression:

Mean Absolute Error (MAE): 6.99 This model, on average, predicts the “Price Change” within approximately 6.99 units of the true value, similar to Linear Regression. Mean Squared Error (MSE): 62.79 MSE is lower than the Decision Tree model but higher than Linear Regression, suggesting intermediate prediction accuracy Root Mean Squared Error (RMSE): 7.92 RMSE is also intermediate, indicating moderate prediction errors. R-squared (R2): -0.0824 The negative R-squared suggests that the Random Forest model does not perform well and has limited predictive power.

The National Ocean Service (NOS) maintains a long-term database containing water level measurements and derived tidal data for coastal waters of the United States and U.S. territories. These data allow for the determination and maintenance of vertical reference datums used for surveying and mapping, coastal construction, waterborne commerce, water level regulation, marine boundary determination, and tide prediction, and for the determination of long-term water level variations (e.g. trends). The data also supports other U.S. government programs, including the National Weather Service (NWS) Tsunami Warning System, the NWS storm surge monitoring programs, and the NOAA Climate and Global Change Program. The database contains an extended series of water level measurements recorded at different tide observation stations. These data are processed to generate a number of products, including monthly and yearly averages for mean tide level, mean sea level, diurnal tide level, mean high and low water, mean range, diurnal mean range, monthly extremes for high and low waters, and frequency and duration of inundations (the number of times and length of time at which the water level has equaled or exceeded a specific elevation for a period of analysis). Data are compiled for coastal waters of the United States, Puerto Rico, the Virgin Islands, and U.S. territories in the Pacific region. Water levels are monitored from a network of over 200 permanent, continuously operating tide observation stations and from numerous stations operated for short-term and long-term projects. Water level measurements are compiled for a variety of observation periods, depending upon the location. For some tide observation stations, records date back to the late 1800s. Observed water level values are compiled primarily at six minute increments. In addition, some stations provide real-time data for planning and emergency situations. The observed values are processed to generate mean and extreme values for different temporal intervals, as noted above. The data consist simply of elevations of water, in feet, observed at specific geographic locations and temporal periods. All water level measurements are referenced to staff '0' and can be referenced to other datums, such as the North American Vertical Datum of 1988 (NAVD 88). Recent data are recorded to the hundredth of a foot; data collected prior to the mid-1960s are recorded to the tenth of a foot. The foundation of the water level database is the National Water Level Observation Network (NWLON), a system of long-term operating tide stations maintained by NOS. Data also are obtained through short-term and long-term cooperative projects with other federal, state, and local agencies and governments to accomplish mutual goals in water level measurement. For example, tide stations are operated temporarily for marine boundary determination and hydrographic survey projects. NOS also maintains several cooperative stations with foreign governments for the Climate and Global Change Program. Indices of tide stations maintained by NOS are available which include for each station the latitude, longitude, dates of observations, bench mark sheet publication date, and tidal epoch. NOS also issues tidal bench mark sheets upon completion of a data collection series or as needed for long-term NWLON stations. Tidal bench mark sheets provide location descriptions and vertical elevations referenced to tidal datums of the station bench marks. A table of tidal datums and the 1929 NGVD, when available, are referenced to the station reference datum. A number of products are issued monthly and annually, for free or on a cost recovery basis. The products are distributed on either hard copy, floppy disk, CD, or over the web and include the following: o Tide Observation Station Lists o Tides, 6-Minute Heights o Tides, Hourly Heights of Tides, Times and Heights of High and Low Waters o Tides, Monthly Mean Summaries o Tidal Bench Mark Sheets with Tidal Datums o Frequency and Duration Analysis of Tidal Water Levels o Daily Mean Sea Level

Uzbekistan Average Monthly Nominal Wage: Year to Date: Industry data was reported at 2,836.900 UZS th in Mar 2019. This records an increase from the previous number of 2,731.100 UZS th for Dec 2018. Uzbekistan Average Monthly Nominal Wage: Year to Date: Industry data is updated quarterly, averaging 2,313.596 UZS th from Mar 2017 (Median) to Mar 2019, with 9 observations. The data reached an all-time high of 2,836.900 UZS th in Mar 2019 and a record low of 1,815.584 UZS th in Mar 2017. Uzbekistan Average Monthly Nominal Wage: Year to Date: Industry data remains active status in CEIC and is reported by State Committee of the Republic of Uzbekistan on Statistics. The data is categorized under Global Database’s Uzbekistan – Table UZ.G011: Average Monthly Nominal Wage: by Economic Activities: Year to Date.

This dataset contains records of daily solar data as well as data collected from magnetic classes...

This dataset was assembled with data from ftp://ftp.swpc.noaa.gov/pub/warehouse/.

The date the data was assembled is 2017-01-15 (yyyy-mm-dd).

The original data source is provided by the Space Weather Prediction Center - SWPC, which is linked to the National Oceanic and Atmospheric Administration - NOAA from US Department of Commerce.

Data description:

• radio_flux_10.7cm: the solar radio flux at 10.7 cm (2800 MHz) is an indicator of solar activity. It is also called the F10.7 index and is one of the longest running records of solar activity. Radio emissions originate high in the chromosphere and low in the corona of the solar atmosphere.
• sesc_sunspot_number: it refers to the number of sunspots computed on a given day. Also called Wolf's number of sunspots, it is given by R = k(10g + s), where k is a scalable factor indicating the combined effects of observation conditions, g is the number of active regions and s the number of sunspots in all these groups.
• sunspot_area: it refers to the sum of the corrected area of all observed sunspots. It is measured in units of millionths of the solar hemisphere.
• goes15_xray_bkgd_flux: it corresponds to the daily average background X-ray flux that is measured by the SWPC primary GOES satellite. To calculate this value, sensors register 24 X-ray measures for a given day, one for each hour. Then, the SWPC creates 3 groups of periods of 8 hours. For these groups, the SWPC registers the lowest values of flux, creating 3 minimal values, one for each group. Then, they calculate the average between the minimum values of the first and the third group. After the average calculation, they must compare this value to the minimal value of the second group. The minimum value from the last comparison gives the result of the X-ray background flux.
• mwl_alpha: binary attribute indicating the presence of apha magnetic class in any observed spot.
• mwl_beta: binary attribute indicating the presence of beta magnetic class in any observed spot.
• mwl_gamma: binary attribute indicating the presence of gamma magnetic class in any observed spot.
• mwl_beta_gamma: binary attribute indicating the presence of beta-gamma magnetic class in any observed spot.
• mwl_delta: binary attribute indicating the presence of delta magnetic class in any observed spot.
• mwl_beta_delta: binary attribute indicating the presence of beta-delta magnetic class in any observed spot.
• mwl_beta_gamma_delta: binary attribute indicating the presence of beta-gamma-delta magnetic class in any observed spot.
• mwl_gamma_delta: binary attribute indicating the presence of gamma-delta magnetic class in any observed spot.
• c_class_flares: number of c class flares observed.
• m_class_flares: number of m class flares observed.
• x_class_flares: number of x class flares observed.

The data collected refer to the period between january 01, 1997 to january 15, 2017.