A dataset detailing the ten smallest countries in the world by land area, providing insights into their geographical sizes and locations.

World Countries is a detailed layer of country level boundaries which is best used at large scales (e.g. below 1:2m scale). For a more generalized layer to use at small-to-medium scales, refer to the World Countries (Generalized) layer. It has been designed to be used as a layer that can be easily edited to fit a users needs and view of the political world. Included are attributes for name and ISO codes, along with continent information. Particularly useful are the Land Type and Land Rank fields which separate polygons based on their areal size. These attributes are useful for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.This dataset represents the world countries as they existed in January 2015.

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

World Countries provides a detailed basemap layer for the country boundaries of the world as they existed in January 2024. It has been designed to be used as a basemap and includes fields for local and official names and country codes, along with fields for capital, continent, and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale views.The data is sourced from Garmin International, Inc. and was published here in October 2024. This layer is updated every 12-18 months or as significant changes occur.

World Countries is a detailed dataset of country level boundaries which can be used at both large and small scales. It has been designed to be used as a basemap and includes an additional Disputed Boundaries layer that can be used to edit boundaries to fit a users needs and view of the political world.

Included are attributes for local and official names and country codes, along with continent and display fields. Particularly useful are the Land_Type and Land_Rank fields which separate polygons based on their size. These attributes can be used for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Average income of small-scale food producers, PPP (constant 2011 international $)





  Dataset Description

This dataset provides country-level data for the indicator "2.3.2 Average income of small-scale food producers, PPP (constant 2011 international $)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/average-income-of-small-scale-food-producers-ppp-for-african-countries.

The GIS database has been developed under the project "Renewable Energy Mapping: Small Hydro Tanzania". This study is part of a technical assistance project, ESMAP funded, being implemented by Africa Energy Practice of the World Bank in Tanzania which aims at supporting resource mapping and geospatial planning for small hydro. Please refer to the country project page for additional outputs and reports: http://esmap.org/re_mapping_TNZ The GIS database contains the following datasets: Administrative Boundaries Hydrology Protected Areas Satellite Imagery Land Cover Geology Topography Population Infrastructure: Power/ Transport each accompanied by a metadata file Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Tanzania Small Hydro GIS Atlas, 2018, https://energydata.info/dataset/tanzania-small-hydro-gis-database-2018"

NOTE: The included files cover the data and replication code for each of the three working papers that comprise this dissertation. By the time these files are available, it is likely that the author will have updated versions of each of these files. If you are interested in using these data, please contact the author directly or visit his website for the most updated versions. Concerns about domestic authority shape how governments conduct their foreign policies. However, this influence is often difficult to observe in highly opaque, non-democratic political systems. In the first part of the dissertation, I investigate the link between domestic authority and foreign policy in the context of diplomacy and trade in late imperial China, a period that spans the Ming (1368-1644) and Qing (1644-1911) dynasties. I argue that international diplomacy can serve leaders’ domestic political needs when it is highly visible to relevant audiences; conducted with counterparts held in relatively high esteem domestically; when certain diplomatic practices are historically associated with regime authority; or when diplomacy is wielded by leaders with relatively low levels of legitimacy. Using an original dataset of over 5,000 Ming and Qing tribute exchanges, I demonstrate that Chinese emperors newly in power conducted a disproportionately high volume of diplomatic activity. I find weaker evidence that this effect was more salient among low-legitimacy emperors. An accompanying case study illustrates how the Yongle Emperor deployed tribute diplomacy as a tool for domestic authority consolidation. Turning to the trade policies of the same period, I argue that beyond leaders, other autocratic elites who participate in foreign policy making are motivated by similar authority concerns. Extant research on non-democratic trade policy has largely neglected this group of actors. I develop a theory that predicts variation in elite policy preferences based on top-down and bottom-up authority relations with the leader and local trading communities, respectively. To assess these claims, I introduce a dataset on the maritime trade preferences of several hundred individual elite officials in late imperial China created through 10 months of archival work in Beijing and Taipei. The data suggest that coastal provincial officials became key pro-trade advocates during the Qing dynasty. The findings offer an example of how trade preferences can vary within a non-democratic regime, and how historical cases can be especially useful for empirically studying these preferences. In the third paper, the dissertation then flips the focus from the domestic politics of Chinese foreign policy to how other states’ internal politics shape their engagement with contemporary China. I argue that leaders of small developing countries can seek greater domestic authority by acquiring “prestige projects,” defined as highly visible, nationally salient international development projects. After identifying a set of Chinese government-financed prestige projects using a new dataset on Chinese development finance, I show that these projects are overwhelmingly concentrated in the world’s poorest and smallest countries, and that their implementation may be associated with higher public support for recipient governments. I also find that China’s government supplies more prestige projects to states that increase their support for Chinese diplomatic objectives.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)





  Dataset Description

This dataset provides country-level data for the indicator "2.3.1 Productivity of small-scale food producers (agricultural output per labour day, PPP) (constant 2011 international $)" across African nations, sourced… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/productivity-of-small-scale-food-producers-for-african-countries.

Use this country model layer when performing analysis within a single country. This layer displays a single global land cover map that is modeled by country for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

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Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

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Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

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Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

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Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

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Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

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Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

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Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering the most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries worldwide. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa have the largest gender gap Looking at the different world regions, the Middle East and North Africa have the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

Household surveys:

Subnational information from different available household surveys of farmers and smallholders in developing and emerging countries.

Household data provides an overview on farmer households livelihoods, decisions, constraints, among other dimensions. One of the main purposes of this suite of data is to provide farmer information disaggregated at different subnational levels, as well as georeferenced information, when available.

The surveys available in this section provide information divided in ten main dimensions: Production, Consumption, Income, Capital, Inputs, Access to markets, Labor, Technology adoption, Infrastructure, and Social.

Currently available is the Data Portrait of Small Family Farms, more will be added.

Data Portrait:

The Data Portrait of Small Family Farms is a project developed by FAO with the objective to set the ground for a standardized definition of smallholders across countries as well as provide consistent measures of inputs, production, sociodemographic characteristics of smallholder farmers across the world. It generates an image on how small family farmers in developing and emerging countries live their lives, putting in numbers the constraints they face, and the choices they make so that policies can be informed by evidence to meet the challenge of agricultural development.

The Data Portrait of Small Family Farms makes use of household surveys developed by national statistical offices in conjunction with the World Bank as part of its Living Standards Measurement Study (LSMS).

The Data Portrait of Small Family Farms collected data for 19 countries across the world, and for some of them data was reported for more than one round, resulting in a total of 29 surveys. The following table shows the sources of the data. Country and year available information is also presented. Find the link to the table here

This dataset is the aggregation of the following datasets;

1. Household Survey (Data Portrait - Admin 1 - GADM - 1998-2012)
2. Household Survey (Data Portrait - Admin 2 - GADM - 1998-2013)
3. Household Survey (Data Portrait - Admin 3 - GADM - 1992-2013)
4. Household Survey (Data Portrait - Admin 1 - GAUL - 1992-2012)
5. Household Survey (Data Portrait - Admin 2 - GAUL - 1992-2012)

Abstract (en): The Global Digital Activism Data Set (GDADS), released February 2013 by the Digital Activism Research Project (DARP) at the University of Washington in Seattle, features coded cases of online digital activism from 151 countries and dependent territories. Several features from each case of digital activism were documented, including the year that online action commenced, the country of origin of the initiator(s), the geographic scope of their campaign, and whether the action was online only, or also featured offline activities. Researchers were interested in the number and types of software applications that were used by digital activists. Specifically, information was collected on whether software applications were used to circumvent censorship or evade government surveillance, to transfer money or resources, to aid in co-creation by a collaborative group, or for purposes of networking, mobilization, information sharing, or technical violence (destructive/disruptive hacking). The collection illustrates the overall focus of each case of digital activism by defining the cause advanced or defended by the action, the initiator's diagnosis of the problem and its perceived origin, the identification of the targeted audience that the campaign sought to mobilize, as well as the target whose actions the initiators aimed to influence. Finally, each case of digital activism was evaluated in terms of its success or failure in achieving the initiator's objectives, and whether any other positive outcomes were apparent. Through GDADS and associated works, DARP aims to study the effect of digital technology on civic engagement, nonviolent protest, and political change around the world. The GDADS contains three sets of data: (1) Coded Cases, (2) Case Sources, and (3) Coded Cases 2.0. The Coded Cases dataset contains 1179 coded cases of digital activism from 1982 through 2012. The Case Sources dataset is an original deposited Excel document that contains source listings from all cases documented by researchers, including those that were ultimately excluded from the original Coded Cases dataset. Coded Cases 2.0 contains 426 additional cases from 2010 through 2012; these cases were treated with a revised coding scheme and an extended review process. GDADS was assembled with the following inclusion criteria: cases needed to exhibit either (1) an activism campaign with at least one digital tactic, or (2) an instance of online discourse aimed at achieving social or political change, and (3) needed to be described by a reliable third party source. In addition to these inclusion criteria, researchers required that the digital activism be initiated by a traditional civil society organization, such as a nongovernmental organization or a nonprofit, or by the collaborative effort of one or more citizens. Digital activism cases initiated by governments or for-profit entities were not included in the collection. The data were assembled by a team of volunteers searching Web sites that are known to document global digital activism; researchers also collected data from peer reviewed journal articles that included digital activism case studies. This data collection does not feature a weighting scheme. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Global occurrences of online digital activism and journal article case studies of digital activism from 1982 through 2012. Smallest Geographic Unit: country Dataset 1: Coded Cases, contains the entire collection of coded cases, according to the inclusion criteria, for 1982-2009, but is incomplete for 2010-2012. Dataset 2: Case Sources, is an original deposited Excel document that contains links and citations used to code dataset 1 cases, plus 166 cases collected but not included in dataset 1. Dataset 3: Coded Cases 2.0, contains additional cases using purposive, multi-source, multilingual, sampling. For more information on sampling, please refer to the Methodology section in the ICPSR Codebooks. 2014-06-12 The collection has been updated with file set 3, Coded Cases 2.0, which contains additional cases that use an updat...

The number of small and medium-sized enterprises in Germany was forecast to continuously increase between 2024 and 2029 by in total 0.8 thousand enterprises (+0.38 percent). According to this forecast, in 2029, the number will have increased for the sixth consecutive year to 212.45 thousand enterprises. According to the OECD an enterprise is defined as the smallest combination of legal units, which is an organisational unit producing services or goods, that benefits from a degree of autonomy with regards to the allocation of resources and decision making. Shown here are small and medium-sized enterprises, which are defined as companies with 1-249 employees.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the number of small and medium-sized enterprises in countries like Austria and Switzerland.

Countries featuring attributes of imports and exports of Electronics commodities in 2014 and e-waste. Trade data from UN Comtrade Database (http://comtrade.un.org/data/) All imports and Exports in 2014 for commodity code 85: Electrical machinery and equipment and parts thereof; sound recorders and reproducers, television image and sound recorders and reproducers, and parts and accessories of such articles

E-Waste data from United Nations University, "The Global E-Waste Monitor 2014" http://i.unu.edu/media/ias.unu.edu-en/news/7916/Global-E-waste-Monitor-2014-small.pdf