Luxembourg number dataset is a popular platform for cell phone number lists. Many companies in Luxembourg use our phone number library for promotions. Our services have many advantages. Firstly, you will receive our products within 24 hours after confirming your order and payment. Secondly, our phone number list works on all devices, like smartphones, computers, and tablets. Thirdly, our packages are affordable and fit every budget. Moreover, our Luxembourg number dataset also has a filter option. This allows you to find specific numbers based on your needs. You will also receive a free updated telemarketing list six months after purchase. Our database complies with GDPR and provides over 95% accuracy. If there are any errors, we will fix them for free. This ensures you have accurate and current phone numbers, improving your telemarketing efforts. Luxembourg phone data helps you easily contact people or businesses in Luxembourg. Our system is user-friendly and saves time. It also provides additional details like location, age, and gender. We offer a “Do Not Call” list to avoid legal issues in SMS marketing. You can get both a call list and an SMS marketing list in one package. Also, List to Data helps businesses find the right telephone numbers quickly, which makes the process even easier. In addition, our Luxembourg phone data contains both B2B and B2C phone numbers, which support the growth of your business. You can get our customer-friendly after-sales service. We also provide excellent customer service 24/7. If you have any questions or problems, please call us anytime. We are always here to assist you in any situation. Luxembourg phone number list is a valuable tool. It helps you connect with people in Luxembourg. The list includes phone numbers that help companies reach new customers. With name, age, and contact information, it is perfect for marketing. So, use it for promotions, updates, or feedback. This phone number list is available at a reasonable price. So, buy this mobile phone number list at a low price and get huge benefits. Moreover, our Luxembourg phone number list offers good value for your money. Since they update and ensure its accuracy, it helps you get the best results. Moreover, telemarketing saves money and grows your brand. Our cell phone list increases sales. Therefore, you will get great returns on marketing.

Real Estate Email List is a premium mailing database for your needs. Most importantly, the list is the most popular site in the world. It is the largest data provider. Besides, the list is verified by human checks and automated software. You get new connections instantly. In addition, our expert team builds a qualified email list and checks the accuracy levels from millions of sources. The list is 95% accurate for giving the best results. Moreover, the dataset provides authentic service. This service can help you grow your business in a short time. Also, the leads link is ready for instant download. Furthermore, we give weekly updates and a bounce-back guarantee with Excel and CSV files. The leads give more information about your services. If you want a specific real estate email list, tell us. We make it for you properly. We provide new data for free to replace missing data.

Real Estate Email List provides a free sample for marketing campaigns. You can create any custom order with your desired areas. The leads ensure that you never get inactive email data. After visiting our website, List to Data, contact us. You can purchase this email list to make your business more competitive. The dataset is profitable. In conclusion, you can get instant results for your products and services. Real Estate Email Database gives you verified and updated contact details. Also, it helps you connect with property owners, agents, and investors directly. In fact, this dataset includes names, phone numbers, email addresses, and postal details. Therefore, you can reach the right people in the real estate market quickly. So, you get high-quality leads that can help you grow your business. Likewise, it covers both residential and commercial real estate sectors. As a result, you can target your audience more effectively. Real Estate Email Database is fresh and regularly updated. This way, your campaigns always reach active contacts. Also, the affordable price makes it suitable for businesses of any size.

Therefore, you can boost sales without spending too much. Furthermore, this Email database supports various marketing goals. For example, you can promote property listings, offer investment deals, or build long-term client relationships. Finally, choose our database to enjoy better leads, higher ROI, and steady business growth.

Nepal Number Dataset is an index of Nepal contact numbers that are 100% accurate and valid. We always double-check to make sure that this record is correct. So, when you use this number library, you can trust that Nepal contact numbers work. And if you ever get an incorrect number, you get a replacement guarantee. This means that if a phone number doesn’t work, they will give you a new number at no extra cost. Moreover, the Nepal Number Dataset is very reliable. Use it with confidence, knowing that you are following the right steps for a smooth, successful outreach effort. The people on the list have agreed to share their mobile numbers. So, you are not breaking any rules when you use this database. And, getting the customer’s consent makes contacting them more welcoming and effective. Nepal phone data is detailed information about Nepal contact numbers. Trusted sources collect this phone data to ensure its reliability. The sources from which this library comes may include websites, government records, and phone service providers. We verify each source, and you can check the URLs where we got the data. This ensures that the mobile data is accurate and reliable. Also, Nepal phone data providers offer 24/7 support. Also, Nepal phone data follows an opt-in policy. This means that people can share their numbers. This is good because it ensures that people know they are using their information. You won’t get in trouble for using contact details without permission. List to Data helps you to find Nepal contact data for your business. Nepal phone number list is a collection of phone numbers of people living in Nepal. You can sort these contact numbers by gender, age, and relationship status. This means that you can only see the amount that matches your needs. For example, if you want to contact young and single people, you can do so. Also, this contact list follows GDPR rules. Also, the Nepal phone number list helps you remove invalid data. Sometimes, contact numbers may change or stop working. This list checks this and removes those numbers, so you don’t waste time calling people who don’t answer. Using the Nepal phone number list, you reach the right people. Therefore, you get accurate, current information.

Obtain the Complete WRQS DatasetClick the link below to download the dataset in a geodatabase format:

WRQS (Geodatabase)

Please note, the complete dataset is too large for shapefile format due to the 2GB size limitation of shapefiles. The geodatabase format allows for larger file sizes, making it ideal for the complete WRQS dataset.

If you only need a portion of the WRQS dataset (less than 2,000 records), use the interactive map interface in DNRC's Open Data Portal to filter and select specific features and download your selected data in various available formats. This method is ideal for users who require only specific regions or feature types or prefer working with smaller, more manageable file sizes.Details

The Montana Water Right Query System Dataset contains water rights information for the state of Montana. It comprises 10 datasets. The spatial datasets include Points of Diversion (Estimated locations of water right diversion points), Places of Use (Polygons representing areas where water rights are utilized), Reservoirs (Point locations of water storage facilities). Additionally, the dataset includes 7 associated tables: Public Versions, Owners, Change Authorization Scanned Docs, Geocodes, Other Versions, Cases, and Water Right Types.

This comprehensive dataset is derived from the Department of Natural Resources and Conservation (DNRC) Water Rights Query Systems Database. It provides spatial and tabular information crucial for understanding water rights distribution and management in Montana.

For the most up-to-date version of the water rights database or detailed reference information, users should contact the DNRC Water Resources Division at https://dnrc.mt.gov/Water-Resources/Water-Rights/ or call 406-444-6610.

Bahamas number dataset provides contact information from trusted sources. We clarify this data by collecting phone numbers that come from reliable sources only. To ensure clearness, we provide source URLs. This shows where the data is gathered from. In addition, we offer 24/7 support. If you have any questions or need help, our team is always here. With List to Data, you can find phone numbers from different countries. However, we care about accuracy, so we collect the Bahamas number dataset carefully from trusted sources. So, you can rely on this data for business or personal use. With customer support, you never have to wait for help or more information. We also use opt-in data to respect privacy. This ensures you contact people who want to hear from you. Bahamas phone data gives you access to contacts in Bahamas. Also, you can filter the information by gender, age, and relationship status. However, this makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our team works hard to remove invalid data. This way, you only get correct, useful numbers. In addition, our Bahamas phone data is perfect for businesses looking to target specific groups. Hence, you can easily filter your list to focus on certain types of customers. Besides, we remove invalid data regularly, so you will not have to deal with useless numbers. With regular updates, your phone data will always be ready when you need it. Bahamas phone number list is a collection of phone numbers from people in the Bahamas. We define this list by providing 95% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. As a result, you will always have accurate data. We collect the phone numbers we provide based on customer permission. Moreover, we work hard to provide the best Bahamas phone number list for businesses and personal use. Also, we focus on gathering data correctly, so you don’t have to worry about getting incorrect information. Our replacement guarantee gives you peace of mind, knowing that you will always have valid numbers.

Nigeria number dataset is a precious source for your telemarketing at this time. In other words, you need to do marketing to make people aware of your services. Also, without proper marketing, your business won’t be able to reach its maximum potential. Similarly, to ensure the maximum reach of any services or products you need to promote them in all possible mediums. The Nigeria number dataset from List To Data can be the best buy of all. We all know that in this digital era, actually, it is difficult to sell anything without marketing. So, this contact directory can change the whole scenario for anyone. Nigeria phone data will come in handy and at an affordable price. In fact, it will help promote products to a huge audience through the telephone. As we all know, a total of 55.64 million cellular mobile connections were active in this country, so it would be foolish not to use this list for marketing. Hence, with this authentic Nigeria phone data, you can hope to get the best possible outcome. In short, your business will see massive growth with the country’s mobile number database. Nigeria phone data can be used in any of your preferred CRM systems smoothly and you can analyze the results of your campaigns more easily. Besides, we add basic info about the people on our list, so anyone can use them to segment your leads. It will make your marketing more targeted and increase the prospect of success. Nigeria phone number list will be a useful marketing resource. Also, telemarketing costs less than other traditional methods, so it will save you capital. Thus, your business will progress smoothly with a high return on investment [ROI]. Not only that, but the Nigeria phone number list will also have an effect on your branding. Even, take all the business to the next level with our most updated and active number data. Nigeria phone number list is a cost-friendly resource that you can buy now from List To Data. Likewise, our team guarantees a high accuracy rate for this list as well as a high delivery rate for your messages. Finally, you can be sure of the advantages that your business will get from this database.

Attitudes towards climate change, climate protection and energy transition. Topics: 1. Climate change and climate protection in general: Most important problem worldwide; change vs. no change in the global climate; man-made climate change; assessment of climate protection worldwide and in Germany compared to other economically strong countries. 2. Political measures in the field of climate protection and options for action: Most sensible political measures for climate protection in Germany; actor who would have to do the most for climate protection (politics, industry, electricity producers or consumers); second in line for climate protection; contribution that various sectors can make to climate protection (transport and mobility, energy supply, construction, agriculture); importance of various contributions that each individual can make to climate protection (e.g. eat less meat, fly or drive less, etc.). 3. Attitude towards various measures in the field of climate protection: Awareness of various government climate protection measures and their assessment (subsidies for energy advice for homeowners, subsidies for energy-efficient building renovation and heating systems, purchase premiums for electric cars, tax incentives for electric vehicles, promotion of the expansion of charging stations for electric cars, obligation for renters to present energy certificates for buildings, promotion of ecological agriculture, tightening of CO2 limits for cars) ; awareness of the Climate Cabinet and expectations of its decisions; driving bans on older diesel vehicles are justified; opinion on the introduction of a CO2 tax; expected effects of a CO2 tax on electricity and fuel consumption; opinion on a speed limit on motorways; opinion on the introduction of a kerosene tax; opinion on an increase in VAT on meat to 19%. 4. Energy transition: Assessment of the progress made in energy system transformation; confidence in the federal government with regard to a successful energy transition; expected major problems in implementing the energy transition; assessment of the coal phase-out by 2038; too much vs. too little consideration of the interests of those affected in the expansion of power lines; opinion on greater citizen participation in the planning of new power lines; expected future security of supply for electricity from renewable energies; assessment of the own electricity price; expected price development for electricity from renewable energies; expected financial burden on the own budget due to rising electricity prices. Demography: Sex; age (categorized); school leaving certificate or desired school leaving certificate; university degree; gainful employment; job security; occupational status; household size; number of persons in the household aged 18 and over; German citizenship; party affiliation; federal state. Additionally coded: respondent ID; federal state; Berlin East/West; city size; weighting factor; reached via mobile or fixed line; only mobile: reached at home or elsewhere; reached via an additional fixed line number (homezone or home option) on the mobile phone; fixed line connection in the household; additional mobile number; total number of mobile phone numbers; fixed line: number of fixed line numbers via which one can be reached; mobile phone ownership. Einstellungen zu Klimawandel, Klimaschutz und zur Energiewende. Themen: 1. Klimawandel und Klimaschutz allgemein: Wichtigstes Problem weltweit; Veränderung vs. keine Veränderung des Weltklimas; Klimawandel vom Menschen verursacht; Beurteilung des Klimaschutzes weltweit und in Deutschland im Vergleich zu anderen wirtschaftlich starken Ländern. 2. Politische Maßnahmen im Bereich Klimaschutz und Handlungsmöglichkeiten: Sinnvollste politische Maßnahmen für den Klimaschutz in Deutschland; Akteur, der am meisten für den Klimaschutz tun müsste (Politik, Industrie, Stromerzeuger oder Verbraucher); an zweiter Stelle für den Klimaschutz in der Pflicht; Beitrag, den verschiedene Bereiche für den Klimaschutz leisten können (Verkehr und Mobilität, Energieversorgung, Bauwesen, Landwirtschaft); Wichtigkeit verschiedener Beiträge, die jeder Einzelne zum Klimaschutz leisten kann (z.B. weniger Fleisch essen, weniger fliegen bzw. Auto fahren, etc.). 3. Einstellung zu verschiedenen Maßnahmen im Bereich Klimaschutz: Bekanntheit diverser staatlicher Klimaschutz-Maßnahmen und deren Beurteilung (Zuschüsse für Energieberatung für Eigenheimbesitzer, Zuschüsse für energieeffiziente Gebäudesanierung und Heizungsanlagen, Kaufprämien für Elektroautos, Steueranreize für Elektrofahrzeuge, Förderung des Ausbaus von Ladestationen für Elektroautos, Pflicht für Vermieter, Energieausweis für Gebäude vorzulegen, Förderung ökologischer Landwirtschaft, Verschärfung der CO2-Grenzwerte für PKW); Bekanntheit des Klimakabinetts und Erwartungen an dessen Entscheidungen; Fahrverbote für ältere Dieselfahrzeuge sind gerechtfertigt; Meinung zur Einführung einer CO2-Steuer; erwartete Auswirkungen einer CO2-Steuer auf den Strom- und Kraftstoffverbrauch; Meinung zu einem Tempolimit auf Autobahnen; Meinung zur Einführung einer Kerosin-Steuer; Meinung zu einer Erhöhung der Mehrwertsteuer für Fleisch auf 19%. 4. Energiewende: Beurteilung der Fortschritte bei der Energiewende; Vertrauen in die Bundesregierung im Hinblick auf eine erfolgreiche Energiewende; erwartete größte Probleme bei der Umsetzung der Energiewende; Beurteilung des Kohleausstiegs bis zum Jahr 2038; zu viel vs. zu wenig Rücksichtnahme auf Betroffenen-Interessen beim Ausbau von Stromtrassen; Meinung zu einer stärkeren Bürgerbeteiligung bei der Planung neuer Stromtrassen; erwartete zukünftige Versorgungssicherheit für Strom aus erneuerbaren Energien; Beurteilung des eigenen Strompreises; erwartete Preisentwicklung für Strom aus erneuerbaren Energien; erwartete finanzielle Belastung des eigenen Haushalts durch steigende Strompreise. Demographie: Geschlecht; Alter (kategorisiert); Schulabschluss bzw. angestrebter Schulabschluss; Hochschulabschluss; Erwerbstätigkeit; Sicherheit des Arbeitsplatzes; berufliche Stellung; Haushaltsgröße; Anzahl Personen im Haushalt ab 18 Jahren; deutsche Staatsbürgerschaft; Parteisympathie; Bundesland. Zusätzlich verkodet wurde: Befragten-ID; Bundesland; Berlin Ost/West; Ortsgröße; Gewichtungsfaktor; erreicht über Mobilfunk oder Festnetz; nur Mobil: zuhause oder woanders erreicht; über eine zusätzliche Festnetznummer (Homezone oder ZuhauseOption) auf dem Handy erreichbar; Festnetzanschluss im Haushalt; weitere Handynummer; Anzahl der Handynummern insgesamt; Festnetz: Anzahl der Festnetznummern über die man erreichbar ist; Handybesitz.

Abstract: The French Comedy Chacun Pour Tous (2018) tells the story of the French national team of Basketball ID (“Intellectual Disabilities”) participating in the 2000 Sydney Summer Paralympics. National coach Martin is desperately looking for capable members for his team; to create a competitive squad, he recruits additional non-disabled amateur basketball players, with whom the team eventually competes in Sydney. The narration deals with the individual experiences of the disabled and non-disabled players during the tournament. The film is based on true events: At the Sydney Paralympics 2000, the Spanish Basketball ID team won a gold medal with ten non-disabled players. The fraud was discovered, and the medal had to be given to the second place, Russia. Details: The film opens with Martin, coach of France’s national basketball ID team, desperately trying to recruit players for his team in disabled facilities after some members have left the team. In order to stay competitive and to preserve the French association for Basketball ID, Martin, initially against the wishes of his co-coach Sami, makes a plan to compete in the Paralympics with non-disabled players. The two places an ad and recruit amateur basketball players Stan, Pippo, André, Malik, and Michel in a banlieue. The group meets Martin’s plans with initial suspicion but eventually agrees to the plot. The plan is not confided (for now) to the remaining disabled players, Yohan and Freddie, nor to the team psychologist, Julia, who is also Yohan’s sister. In secret, Martin trains the new team members not only in basketball skills but also in a pseudo-disabled habitus and he is, which is supposed to help the team pass background checks. The players acquire supposedly disabled behaviours and successfully pass the entrance exam. Parallel to these preparations, Martin’s private life is shown, which is marked by the care of his physically disabled daughter Alice. The team travels to Sydney, and the tournament begins. A gap between the disabled and non-disabled players, marked by a lack of understanding and hostility, can be overcome through sporting success and a slow bonding between the members. In particular, the Yohan-Stan and Freddie-Pippo relationships evolve into close and mindful friendships. Additionally, romances seem to develop between Stan and Yohan’s sister Julia and between Pippo and another athlete, Amie, who sits in a wheelchair. At the same time, the entire team faces the challenge of not letting their façade fall, and situations repeatedly arise in which the team becomes conspicuous through “unusual” behaviour. Corresponding moments, for example, when Pippo, Stan, Yohan, and Freddie go to a party with other disabled athletes, repeatedly provide dicey situations. After the party, a one-night stand occurs between Pippo and Amie, with Pippo pretending to be non-disabled. General suspicions rise among officials, journalists, and disabled players. The French team plays exceptionally well and sometimes wins by too much. During the semi-final, Julia discovers the newly recruited players are not disabled. Horrified, she confronts Martin, who does not want to see the faults in his actions. Drunk Julia tells the secret to a Russian stranger in a hotel bar - who does not understand her since she speaks French. Stan seeks Julia out to talk to her about the secret and put it into perspective, to which she does not respond. Martin gets drunk with the non-handicapped players in a bar the night before the final. Freddie begins a romance with a Russian player, Paprika. Julia seeks to talk to Stan and Pippo, and the three make up. As Martin talks to his daughter on the phone, it dawns on him that winning the finals would mean too much attention. He tells the team to lose on purpose. At first, everything goes according to plan. The other final team, Russia, is in the lead at halftime. Julia then finds out that the Russian team is also non-disabled - her flirt from the bar the day before, who was obviously not disabled, is one of the players. Accordingly, the team changes tactics: instead of playing badly on purpose, all members get the best out of each other and eventually defeat the Russian team by two free throws of Yohan. With the final win, however, the team flies off the handle. A horde of journalists confronts Martin at the airport - he sends his staff on the plane and faces the press.

Derived projected datasets for the eight Australian capital cities in 2016-2045 and 2036-2065, centred around 2030 and 2050, respectively. Projects used eight general circulation models (GCMs) under Representative Concentration Pathway [RCP]2.6, RCP4.5, RCP6.0 and RCP8.5. The scenarios were under Coupled Model Intercomparison Project [CMIP]5. The eight GCM models are ACCESS1-0, CESM1-CAM5, CNRM-CM5, CanESM2, GFDL-ESM2M, HadGEM2-CC, MIROC5 and NorESM1-M, and are described online: https://www.climatechangeinaustralia.gov.au/en/obtain-data/application-ready-data/eight-climate-models-data/. Only data from five GCMs are available for RCP2.6 and four for RCP6.0.

For each city, seven*seven 5 km grids were extracted at grid centroids correlating to the centre of its central business district. These coordinates are in the file "City coordinate." The corresponding datasets for each city, RCP, GCM, time period, and meteorological variable are located in their respective city folder in the folder "future." The meteorological variables are relative humidity ("hurs"), solar radiation ("rsds"), average air temperature ("tas"), maximum air temperature "(tasmax") and minimum air temperature ("tasmin"). These were used to create derived .csv files also stored in the "future" folder, which in turn were used to create derived R datasets ("ccia_future.rda" and "ccia_future2.rda") combining all the datasets into one and creating additional meteorological indices using the available data. The R code used to create these datasets is included "CCiA data manipulation.R". It uses functions stored in the R code file "Climate functions.R". The additional meteorological indices include alternate humidity variables, apparent temperature variables and the Excess Heat Factor (EHF). The heatwave thresholds values used to calculate EHF (the 95th percentile of daily mean temperature from a reference period) per city are included in "barra_ehfr.R" and were calculated from a separate dataset (not included) derived from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis (BARRA).

The original projected climate datasets were sourced from Climate Change in Australia (CCiA), published by the Commonwealth Science Industrial Research Organisation (CSIRO). The original datasets are available online: https://data-cbr.csiro.au/thredds/catalog/catch_all/oa-aus5km/Climate_Change_in_Australia_User_Data/Application_Ready_Data_Gridded_Daily/catalog.html. The license under which the data were used is available online: https://www.climatechangeinaustralia.gov.au/en/overview/about-site/licences-and-acknowledgements/.

I acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modelling groups (listed at https://www.climatechangeinaustralia.gov.au/en/obtain-data/application-ready-data/eight-climate-models-data/) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Further information regarding these datasets and meteorological variables is listed in the author's PhD thesis, available online: https://digital.library.adelaide.edu.au/dspace/handle/2440/137773. For any queries, please do not hesitate to contact the author: matthew.borg@adelaide.edu.au.

The short survey on current issues relating to government spending and public debt was conducted by the opinion research institute forsa on behalf of the Press and Information Office of the Federal Government. In the survey period from 18.03.2024 to 20.03.2024, the German-speaking population aged 14 and over was asked in telephone interviews (CATI) about their attitudes to government spending and government debt. In particular, the focus is on the assessment of the debt brake and various options for reforming it. Respondents were selected using a multi-stage random sample as part of a multi-topic survey (policy BUS) including landline and mobile phone numbers (dual-frame sample). Assessment of Germany´s overall financial situation in terms of income and expenditure; assessment of Germany´s debt burden compared to most other industrialized countries; opinion on government debt (government debt should generally be avoided, is generally not a problem, only makes sense if it is used for investments for the future); government spends too much vs. too little money on various political and social tasks (health and care, defense, social affairs, climate protection, housing, integration of immigrants, pensions); opinion on the state only taking out new larger loans in exceptional emergency situations such as natural disasters (debt brake should remain as it is, it should be reformed or it should be abolished completely); evaluation of various proposals for reforming the debt regulation (change the debt limit so that the state can generally take on more debt than before, create a transitional rule so that even in the year following an emergency situation it is still possible to take on slightly more debt than usual, allow higher debt to be taken on if the economic situation is worse than expected, allow higher debt to be taken on for defense spending, allow higher debt to be taken on for investments in climate protection, allow higher debt to be taken on for investments in infrastructure such as roads and railways). Demography: sex; age; education; income level low, medium, high (net equivalent income); city size; party preference in the next federal election; voting behavior in the last federal election. Additionally coded were: Respondent ID; region west/east; weighting factor. Die Kurzumfrage über aktuelle Fragen zu Staatsausgaben und Staatsschulden wurde vom Meinungsforschungsinstitut forsa im Auftrag des Presse- und Informationsamtes der Bundesregierung durchgeführt. Im Erhebungszeitraum 18.03.2024 bis 20.03.2024 wurde die deutschsprachige Bevölkerung ab 14 Jahren in telefonischen Interviews (CATI) zu ihren Einstellungen zu Staatsausgaben und Staatsschulden befragt. Insbesondere geht es um die Bewertung der Schuldenbremse bzw. um verschiedene Möglichkeiten, sie zu reformieren. Die Auswahl der Befragten erfolgte durch eine mehrstufige Zufallsstichprobe im Rahmen einer Mehrthemenbefragung (Politik-BUS) unter Einschluss von Festnetz- und Mobilfunknummern (Dual-Frame Stichprobe). Bewertung der finanziellen Lage Deutschlands insgesamt bezogen auf Einnahmen und Ausgaben; Einschätzung der Schuldenlast Deutschlands im Vergleich zu den meisten anderen Industriestaaten; Meinung zu Staatsschulden (Schulden des Staates sollten grundsätzlich vermieden werden, sind grundsätzlich kein Problem, sind nur dann sinnvoll, wenn sie für Investitionen für die Zukunft eingesetzt werden); Staat gibt zu viel vs. zu wenig Geld aus für verschiedene politische und gesellschaftliche Aufgaben (Gesundheit und Pflege, Verteidigung, Soziales, Klimaschutz, Wohnungsbau, Integration von Zugewanderten, Renten); Meinung zur Neuaufnahme größerer Kredite durch den Staat nur in außergewöhnlichen Notsituationen wie z.B. Naturkatastrophen (Schuldenbremse sollte so bestehen bleiben wie sie ist, sie sollte reformiert werden oder sie sollte vollständig abgeschafft werden); Bewertung verschiedener Vorschläge zur Reform der Schuldenregelung (die Schuldengrenze verändern, damit der Staat generell mehr Schulden aufnehmen kann als bisher, eine Übergangsregel schaffen, sodass man auch im Jahr nach einer Notsituation noch etwas mehr Kredite aufnehmen kann als gewöhnlich, die Aufnahme höherer Schulden erlauben, wenn die Wirtschaftslage schlechter ist als erwartet, die Aufnahme höherer Schulden erlauben für Verteidigungsausgaben, die Aufnahme höherer Schulden erlauben für Investitionen in den Klimaschutz, die Aufnahme höherer Schulden erlauben für Investitionen in die Infrastruktur wie Straßen und Schienen). Demographie: Geschlecht; Alter; Bildung; Einkommenslage niedrig, mittel, hoch (Nettoäquivalenzeinkommen); Ortsgröße; Parteipräferenz bei der nächsten Bundestagswahl; Wahlverhalten bei der letzten Bundestagswahl. Zusätzlich verkodet wurde: Befragten ID; Region West/Ost; Gewichtungsfaktor.

Risk manager email list can play a key role in your business growth. Moreover, risk managers are vital because they protect companies from financial and operational problems. They always ensure workplace safety and find ways to improve business stability. In addition, they often work in industries like insurance, banking, and corporate finance. However, reaching them directly is not always easy. Therefore, you can use our risk manager email list to connect faster. With our verified lists, you can contact professionals in manufacturing, healthcare, finance, and retail. Most importantly, the data is accurate, up-to-date, and human-verified for your benefit. Also, we check the database regularly to maintain quality. So, you can send offers, updates, or proposals without worrying about wrong contacts.

manager email list is affordable and easy to use in any CRM system. Thus, you save time, reduce marketing costs, and target the right audience effectively. So, by choosing our service, you get the correct information that truly helps your business succeed. Therefore, whether you run a small company or a large enterprise, you can benefit. As a result, your marketing can bring better leads, higher engagement, and increased profits. In conclusion, our risk managers email lists give you the right contacts for the right opportunities. So, by choosing our service, you invest in accurate information that helps your business grow and succeed. It is available now at List to Data. Risk manager email database provides the best solution for your business. It allows you to easily reach out to risk managers all over the world. Most importantly, it helps you expand your business and find new clients. We ensure our database is highly reliable by verifying all the information we collect. That makes your marketing efforts much more effective. With the email addresses in this database, you can directly inform risk managers about your products or services. Furthermore, you can easily get our database in either Excel or CSV file format. Moreover, this makes it simple to use the data according to your specific needs. In conclusion, purchasing our dataset will help your business grow faster. We are always committed to providing you with the best possible service.

DESCRIPTION

Johns Hopkins' county-level COVID-19 case and death data, paired with population and rates per 100,000

SUMMARY Updates April 9, 2020 The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County. April 20, 2020 Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well. April 29, 2020 The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

Overview The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

Filter cases by state here

Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

Pull the 100 counties with the highest per-capita confirmed cases here

Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive Embed Code

Caveats This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website. In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules. In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county" This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members. Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates. Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey. The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories --...

Description

This dataset is the "development dataset" for the DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring".

The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task:

ToyCar

ToyTrain

Fan

Gearbox

Bearing

Slide rail

Valve

Overview of the task

Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial-intelligence-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines.

This task is the follow-up from DCASE 2020 Task 2 to DCASE 2022 Task 2. The task this year is to develop an ASD system that meets the following four requirements.

1. Train a model using only normal sound (unsupervised learning scenario)

Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks.

1. Detect anomalies regardless of domain shifts (domain generalization task)

In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2.

1. Train a model for a completely new machine type

For a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning.

1. Train a model using only one machine from its machine type

While sounds from multiple machines of the same machine type can be used to enhance detection performance, it is often the case that sound data from only one machine are available for a machine type. In such a case, the system should be able to train models using only one machine from a machine type.

The last two requirements are newly introduced in DCASE 2023 Task2 as the "first-shot problem".

Definition

We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".

"Machine type" indicates the type of machine, which in the development dataset is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.

A section is defined as a subset of the dataset for calculating performance metrics.

The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.

Attributes are parameters that define states of machines or types of noise.

Dataset

This dataset consists of seven machine types. For each machine type, one section is provided, and the section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.

File names and attribute csv files

File names and attribute csv files provide reference labels for each clip. The given reference labels for each training/test clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:

[filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]...

Recording procedure

Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.

Directory structure

• /dev_data

  • /raw
    • /fan
      • /train (only normal clips)
        • /section_00_source_train_normal_0000_.wav
        • ...
        • /section_00_source_train_normal_0989_.wav
        • /section_00_target_train_normal_0000_.wav
        • ...
        • /section_00_target_train_normal_0009_.wav
      • /test
        • /section_00_source_test_normal_0000_.wav
        • ...
        • /section_00_source_test_normal_0049_.wav
        • /section_00_source_test_anomaly_0000_.wav
        • ...
        • /section_00_source_test_anomaly_0049_.wav
        • /section_00_target_test_normal_0000_.wav
        • ...
        • /section_00_target_test_normal_0049_.wav
        • /section_00_target_test_anomaly_0000_.wav
        • ...
        • /section_00_target_test_anomaly_0049_.wav
      • attributes_00.csv (attribute csv for section 00)
  • /gearbox (The other machine types have the same directory structure as fan.)
  • /bearing
  • /slider (slider means "slide rail")
  • /ToyCar
  • /ToyTrain
  • /valve

Baseline system

The baseline system is available on the Github repository dcase2023_task2_baseline_ae.The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.

Condition of use

This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Citation

If you use this dataset, please cite all the following papers. We will publish a paper on the description of the DCASE 2023 Task 2, so pleasure make sure to cite the paper, too.

Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, and Masahiro Yasuda. First-shot anomaly detection for machine condition monitoring: A domain generalization baseline. In arXiv e-prints: 2303.00455, 2023. [URL]

Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. In Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), 31-35. Nancy, France, November 2022, . [URL]

Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL]

Contact

If there is any problem, please contact us:

Kota Dohi, kota.dohi.gr@hitachi.com

Keisuke Imoto, keisuke.imoto@ieee.org

Noboru Harada, noboru@ieee.org

Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp

Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com

The depth difference dataset (2004) is a depth grid showing the difference between modelled fluvial flood depths and modelled fluvial flood depths under climate change conditions (an increase in peak flows of 20%). Modelled fluvial flood depth and climate change depth difference data* was created for both the 1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning. A two-dimensional hydrodynamic model called JFlow was used to produce both the modelled fluvial flood depth and modelled fluvial flood depth with climate change data. This depth difference data, using these 2 datasets, is provided on a 5x5m grid. Since 2004, some local detailed modelling projects have included scenarios for climate change however this depth difference dataset has not been updated. INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood due to climate change, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used further evidence, verification and studies should be undertaken. Climate change allowances have changed since this work was completed in 2004. More recent, accurate and local detailed modelling depth data with climate change is available for some places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest. This metadata record is for Approval for Access product AfA480 2004 1 in 100 Fluvial Depth Grid Climate Change Difference Modelled fluvial flood depth data with climate change are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference SO. If you are interested in data for another grid reference refer to this Ordnance Survey National Grid document to find the relevant referencing code and search on Data.gov.uk again to download the data. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

This collection includes data from a series of laboratory behavioural experiments. The experiments investigate aspects of perception, action and decision-making. The experiments are described in full in journal articles. Because each dataset is already deposited elsewhere, the collection here serves as a pointer to these deposited data sources.People often have to deal with multiple streams of information at once. For example, imagine you are on a walk out in the woods trying to find a kind of rare bird. You hear a bird call (audio information) and turn towards it. You can see some leaves moving around in a tree (visual information). Neither stream of information is perfectly reliable -- you will tend to make some error one way or another when you try to pinpoint the sound location, and you don't know exactly which leaf the bird was behind -- but both are useful pieces of information. What we see in the lab when we give these kinds of tasks to adults is called 'optimal cue combination'. Adults tend to combine all the different 'cues' available in an 'optimal' way that gets them as close as possible to the right location. To do this, they have to take into account how reliable each cue is, weight each cue by its reliability, and then take a weighted average. Developmental Psychologists have found that children don't begin doing this until they are about 10 or 11 years old; before that, they seem to just ignore one cue or the other (e.g. Nardini, Bedford & Mareschal, 2010). This is surprising because in these studies, children have all the information they need to make more accurate judgments. They are just failing to combine the information in the right way. We want to know why. What is changing at 10-11 years old that allows them to start doing optimal cue combination? We are going to examine two big ideas that might provide good answers to this puzzle. First, children at 10-11 might gain a new ability. They might first develop the ability to learn how to put cues together at this age. The second idea is that it might come down to the quality of the individual cues that children are trying to average together. It's generally a bad idea to do 'optimal cue combination' with a cue that is strongly biased (systematically incorrect) -- in that case, it makes more sense to ignore the biased cue. Even if a cue is not strictly-speaking biased, it might also be so noisy that it is hard to learn how it works. It could be that children under 10-11 years don't show optimal cue combination because the individual cues that they have available are all too biased or noisy. To test these ideas, we will test several predictions that they make. For example, we should be able to train children at 7-9 years as much as we want and they should never learn cue combination; we should be able to prevent adults from learning cue combination with a new cue by inducing child-like biases. It is possible that both of these big ideas might be correct -- it could be that children need a new ability and that the individual cues also have to improve. This work will be made possible by a combination of methods newly developed in our lab especially for this project, including testing and training children's abilities to combine the senses, and to learn a completely "new" sense, in immersive virtual reality. The work will be interesting to cognitive scientists, especially those interested in development and education. It will help us design better future interventions to teach additional senses to children with sensory loss. It will give us a better understanding of how the use of multiple cues responds to training in childhood, which is of interest to safety-oriented organizations since so many behaviours like safe road crossing can rely on multiple senses (seeing and hearing traffic). It will also be of interest to people who design educational tests since we will continue to develop our new test of a major sensory milestone and show people how similar efficient tests can be designed in other areas of education. Experimental tasks run in the laboratory. Participants experienced or made judgments about visual and/or auditory stimuli while their behavioural (e.g. button-press) or brain (e.g. fMRI signal) responses were measured. Participants were healthy adult and child volunteers. Each study used different custom stimuli, age ranges, and sample sizes in order to test specific hypotheses. These are described in detail in the journal articles linked in the data deposit files.

Data collection comprises two parts, reflecting the two parts of the study. Part one comprises a database of 400 or so ABSs licensed by the Solicitors Regulatory Authority (SRA) in which their key characteristics are recorded. This includes: name of firm, location, legal status, sector origin, total number of people in firm, total number of managers approved by the SRA, total number of solicitors in firm, external investment. Part two comprises transcripts of 18 interviews with ABS firms and a small number of investors. In 2007, the UK government introduced the Legal Services Act 2007 in England and Wales, which removed historic restrictions relating to the financing, management and ownership of legal practices. Breaking with normative tradition, the Act permits non-lawyer ownership and management of law firms through the introduction of a new organizational form – ‘Alternative Business Structures’ (ABS). Despite generating extensive international commentary and controversy, academic research on ABSs is virtually non-existent; an oversight that is particularly intriguing since ABSs symbolise a radical departure from the professional partnership – the traditional structure through which lawyers organize themselves. This mixed-method study addresses this gap and represents the first study examining the ABS population and its impact on the legal services sector. Phase One: The study was undertaken in two phases. Phase one entailed compiling a database of 400 ABS firms which were licensed by the Solicitors Regulatory Authority between 2012 and 31st August 2015. As well as presenting a detailed profile of the key characteristics of these entities, Phase one examined the degree to which ABSs has resulted in a change in the solicitor firm population and the degree to which non-lawyer providers have entered the sector. It also explored the extent to which the ABS population has adopted the two innovations that differentiate them from the traditional professional partnership: the appointment of non-lawyers as owners/managers of law firms and the ability to raise external investment (e.g. from private equity firms, stock flotation). In exploring these innovations Phase one assessed the degree to which proposals considered to be radical are producing radical change. Phase Two: The second phase entailed qualitative interviews with ABS firms that had accessed external investment and a small number of private equity investors. Eighteen interviews were conducted and they explored motives for accessing investment and ways in which management practices had changed. Phase 1: A Database of ASBs The database is confined to ABSs licensed between March 2012 when the SRA first started issuing licenses and 31 August 2015. It is based on archival data encompassing a range of media documents and the collection of routine data complied from several sources. A key aim of creating a database of ABS firms was to identify the types of firms that had chosen to do so, and to record their key characteristics in order to ascertain similarities and differences within the population and how they differed from the wider solicitor firm population. The development of the database involved a number of steps. We started with the SRA’s online register of ABS firms, which provides basic information about each firm. From this, we were able to identify the location of the headquarters ABS firms, their legal status (i.e. LLP, limited company), and whether they were part of publicly quoted companies. We then turned to an online directory compiled by the Law Society, called ‘Find a solicitor’, which is based on data supplied by the SRA and provides details about the organizations and people providing legal services in England and Wales. A profile page is created for each organization, which includes contact details, identifies the type of firm it is (ABS or recognized body) and contains links to other pages providing more detailed information about the firm such as that relating to ‘people’. This includes a list of ‘SRA Approved Managers’, that is, individuals who typically assume the position of Partner, Member or Director and which are regulated by the SRA as being accountable to their organization. In ABS firms, ‘SRA Approved Managers’ include non-lawyers and the online directory provided details of these individuals. We also used the Law Society’s directory to ascertain the size of ABS firms as measured by the number of solicitors and the number of partners (including the number of non-lawyer partners). We also searched the internet for new stories relating to each ABS firm. Although the formation of ABSs was occasionally reported in the national press, most stories originated in trade press, notably, Legal Futures, The Lawyer, and the Solicitors Journal. Taking each firm in turn, we looked for all new stories relating to its formation and supplemented this with any data available from other sources, such as firm websites and sites providing basic searches of companies. We used these news stories to populate further the database where we coded information about whether they were new entrants, conversions, their motivations for becoming an ABS, and attitudes about external investment. This was an iterative process and we revised the coding framework as we uncovered data about different types of ABS firms that did not fit the original categories. Phase 2: Qualitative Interviews In the second phase of the study, our aim was to focus specifically on ABSs that had secured external investment. The initial idea was to four or five in-depth case studies to get a detailed understanding of how these firms operate. However, it as it proved difficult to gain access, we revised our research strategy. Subsequently, we contacted all the ABSs with external investment and private equity investors that had shown interest in investing in the legal services sector to find out about their experiences. We secured 18 interviews altogether. Within the ABS firms, these were undertaken with Directors and partners of the Board / Senior Management team. Typically, they tended to be commercial directors or those with an outward facing role and familiar with undertaking interviews with media. They were willing to participate in the interview because they were interested in the research and reported that colleagues would uninterested and/or too busy to do an interview.

Housekeeping voltage, temperature, current, and payload status values returned every 40 s. NOTE: The analog sensor data in these records are based on the nominal BARREL housekeeping layout. Some payloads may have small differences that are not reflected here. If there is specific sensor data that you need that looks questionable, please contact the BARREL team for clarification.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.

Housekeeping voltage, temperature, current, and payload status values returned every 40 s. NOTE: The analog sensor data in these records are based on the nominal BARREL housekeeping layout. Some payloads may have small differences that are not reflected here. If there is specific sensor data that you need that looks questionable, please contact the BARREL team for clarification.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.

Housekeeping voltage, temperature, current, and payload status values returned every 40 s. NOTE: The analog sensor data in these records are based on the nominal BARREL housekeeping layout. Some payloads may have small differences that are not reflected here. If there is specific sensor data that you need that looks questionable, please contact the BARREL team for clarification.The BARREL Mission was a multiple-balloon investigation designed to study electron losses from Earth's Radiation Belts. Selected as a NASA Living with a Star Mission of Opportunity, BARREL was designed to augment the Radiation Belt Storm Probes, RBSP, mission by providing measurements of the spatial and temporal variations of electron precipitation from the radiation belts. The RBSP mission has since been renamed the Van Allen Probes mission. Each BARREL balloon carried an X-ray spectrometer to measure the bremsstrahlung X-rays produced by precipitating relativistic electrons as they collide with neutrals in the atmosphere, and a DC magnetometer to measure ULF-timescale variations of the magnetic field. BARREL observations collected near latitudes close to either the antarctic and arctic circles at stratospheric altitudes at about 30 km. The BARREL instrumentation provided the first balloon measurements of relativistic electron precipitation while comprehensive in situ measurements of both plasma waves and energetic particles were available. Also, the BARREL data has been used to characterize the spatial scale of precipitation at relativistic energies.The initial pair of balloon campaigns that were conducted initially during the Austral summer months of January and February of 2013 and 2014 with launches from two stations located in Antarctica: the British base located at Halley Bay on the Brunt Ice Shelf and the South African SANAE IV base (SANAE stand for South African National Antarctic Expedition) located in Vesleskarvet, Queen Maud Land. For the 2013 and 2014 the balloon campaigns, the launch plan was designed to maintain an array with about five payloads spread across about six hours of magnetic local time, MLT, in the region that magnetically maps to the radiation belts. Thus, the BARREL balloon constellation constituted an evolving and slowly moving array able to study relativistic electron precipitation from the radiation belts.Later campaigns were undertaken in 2015 and 2016 from the Esrange Space Center located in Kiruna, Sweden. The 2015 and 2016 campaigns were undertaken in coordination with the Van Allen Probes mission, the European Incoherent Scatter Scientific Association, EISCAT, incoherent scatter radar system, and other ground and space based instruments. Seven balloon launches occurred during the August 2015 BARREL campaign. A total of eight flights occurred during August 2016.Summing over the four BARREL campaigns, over 50 small, approximately 20 kg, stratospheric balloons were successively launched. The website creeated and hosted by A.J. Halford (see Information URL below) reports that: "By the end of the campaigns, there were over 90 researchers coordinating on a daily basis with the BARREL team working on 7 different satellite missions, 1 other balloon mission, and way too many ground based instruments to count." Although the BARREL mission launched only balloons during the years from 2013 to 2016, research using data collected on these flights is ongoing, so stay tuned for updates! All data and analysis software are freely available to the scientific community.The information listed above in this resource description was compiled by referencing several BARREL related resources including primarily the Millan et al. (2013) Space Science Reviews publication, the BARREL at Dartmouth mission web site, and the website maintained by A.J. Halford.The current release of all BARREL CDF data products are Version 10 files.BARREL will make all its scientific data products quickly and publicly available but all users are expected to read and follow the BARREL Data Usage Policy listed below.BARREL Data Usage PolicyBARREL data products are made freely available to the public and every effort is made to ensure that these products are of the highest quality. However, there may occasionally be issues with either the instruments or data processing that affect the accuracy of data. When possible, a quality flag is included in higher level data products, and known issues are posted in the BARREL data repository. You are also strongly encouraged to follow the guidelines below if you are planning a publication or presentation in which BARREL data are used. This will help you ensure that your science results are valid. Users should always use the highest version numbers of data and analysis tools. Browse/quick-look plots are not intended for science analysis or publication and should not be used for those purposes without consent of the principal investigator, PI. Users should notify the BARREL PI of the data use and investigation objectives. This will ensure that you are using the data appropriately and have the most recent version of the data or analysis routines. Additionally, if a BARREL team member is already working on a similar or related topic, they may be able to contribute intellectually. If BARREL team members are not part of the author list, then users should Credit/Acknowledge the BARREL team as follows: We acknowledge the BARREL team (PI: Robyn Millan) for use of BARREL data. Users are also requested to provide the PI with a copy of each manuscript that uses BARREL data upon submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI.The BARREL PI can be contacted at: Robyn.Millan@dartmouth.edu.An online copy of the BARREL Data Usage Policy document can be found at: https://barrel.rmillan.host.dartmouth.edu/documents/data.use.policy.pdf.

The dataset for this project is represented by photos, photos for the buildings of the University of Salford, these photos are taken by a mobile phone camera from different angels and different distances , even though this task sounds so easy but it encountered some challenges, these challenges are summarized below: 1. Obstacles. a. Fixed or unremovable objects. When taking several photos for a building or a landscape from different angels and directions ,there are some of these angels blocked by a form of a fixed object such as trees and plants, light poles, signs, statues, cabins, bicycle shades, scooter stands, generators/transformers, construction barriers, construction equipment and any other service equipment so it is unavoidable to represent some photos without these objects included, this will raise 3 questions. - will these objects confuse the model/application we intend to create meaning will that obstacle prevent the model/application from identifying the designated building? - Or will the photos be more precise with these objects and provide the capability for the model/application to identify these building with these obstacles included? - How far is the maximum length for detection? In other words, how far will the mobile device with the application be from the building so it could or could not detect the designated building? b. Removable and moving objects. - Any University is crowded with staff and students especially in the rush hours of the day so it is hard for some photos to be taken without a personnel appearing in that photo in a certain time period of the day. But, due to privacy issues and showing respect to that person, these photos are better excluded. - Parked vehicles, trollies and service equipment can be an obstacle and might appear in these images as well as it can block access to some areas which an image from a certain angel cannot be obtained. - Animals, like dogs, cats, birds or even squirrels cannot be avoided in some photos which are entitled to the same questions above.
2. Weather. In a deep learning project, more data means more accuracy and less error, at this stage of our project it was agreed to have 50 photos per building but we can increase the number of photos for more accurate results but due to the limitation of time for this project it was agreed for 50 per building only. these photos were taken on cloudy days and to expand our work on this project (as future works and recommendations). Photos on sunny, rainy, foggy, snowy and any other weather condition days can be included. Even photos in different times of the day can be included such as night, dawn, and sunset times. To provide our designated model with all the possibilities to identify these buildings in all available circumstances.

1. The selected buildings. It was agreed to select 10 buildings only from the University of Salford buildings for this project with at least 50 images per building, these selected building for this project with the number of images taken are:
2. Chapman: 74 images
3. Clifford Whitworth Library: 60 images
4. Cockcroft: 67 images
5. Maxwell: 80 images
6. Media City Campus: 92 images
7. New Adelphi: 93 images
8. New Science, Engineering & Environment: 78 images
9. Newton: 92 images
10. Sports Centre: 55 images

11. University House: 60 images Peel building is an important figure of the University of Salford due to its distinct and amazing exterior design but unfortunately it was excluded from the selection due to some maintenance activities at the time of collecting the photos for this project as it is partially covered with scaffolding and a lot of movement by personnel and equipment. If the supervisor suggests that this will be another challenge to include in the project then, it is mandatory to collect its photos. There are many other buildings in the University of Salford and again to expand our project in the future, we can include all the buildings of the University of Salford. The full list of buildings of the university can be reviewed by accessing an interactive map on: www.salford.ac.uk/find-us

12. Expand Further. This project can be improved furthermore with so many capabilities, again due to the limitation of time given to this project , these improvements can be implemented later as future works. In simple words, this project is to create an application that can display the building’s name when pointing a mobile device with a camera to that building. Future featured to be added: a. Address/ location: this will require collection of additional data which is the longitude and latitude of each building included or the post code which will be the same taking under consideration how close these buildings appear on the interactive map application such as Google maps, Google earth or iMaps. b. Description of the building: what is the building for, by which school is this building occupied? and what facilities are included in this building? c. Interior Images: all the photos at this stage were taken for the exterior of the buildings, will interior photos make an impact on the model/application for example, if the user is inside newton or chapman and opens the application, will the building be identified especially the interior of these buildings have a high level of similarity for the corridors, rooms, halls, and labs? Will the furniture and assets will be as obstacles or identification marks? d. Directions to a specific area/floor inside the building: if the interior images succeed with the model/application, it would be a good idea adding a search option to the model/application so it can guide the user to a specific area showing directions to that area, for example if the user is inside newton building and searches for lab 141 it will direct him to the first floor of the building with an interactive arrow that changes while the user is approaching his destination. Or, if the application can identify the building from its interior, a drop down list will be activated with each floor of this building, for example, if the model/application identifies Newton building, the drop down list will be activated and when pressing on that drop down list it will represent interactive tabs for each floor of the building, selecting one of the floors by clicking on its tab will display the facilities on that floor for example if the user presses on floor 1 tab, another screen will appear displaying which facilities are on that floor. Furthermore, if the model/application identifies another building, it should activate a different number of floors as buildings differ in the number of floors from each other. this feature can be improved with a voice assistant that can direct the user after he applies his search (something similar to the voice assistant in Google maps but applied to the interior of the university’s buildings. e. Top View: if a drone with a camera can be afforded, it can provide arial images and top views for the buildings that can be added to the model/application but these images can be similar to the interior images situation , the buildings can be similar to each other from the top with other obstacles included like water tanks and AC units.

13. Other Questions:

14. Will the model/application be reproducible? the presumed answer for this question should be YES, IF, the model/application will be fed with the proper data (images) such as images of restaurants, schools, supermarkets, hospitals, government facilities...etc.