China Scale: Loss Amount: Year to Date data was reported at 0.131 RMB bn in Oct 2015. This records an increase from the previous number of 0.122 RMB bn for Sep 2015. China Scale: Loss Amount: Year to Date data is updated monthly, averaging 0.026 RMB bn from Dec 1998 (Median) to Oct 2015, with 102 observations. The data reached an all-time high of 0.131 RMB bn in Oct 2015 and a record low of 0.007 RMB bn in Feb 2006. China Scale: Loss Amount: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Oven, Fan, Scale, Packaging Equipment, etc.: Scale.

Bald Women Dataset

The dataset contains images of women with various stages of hair loss. Each person is represented by 5 images showcasing their condition. The alopecia dataset encompasses diverse demographics, age and ethnicities.

Shooting angles in the dataset: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F71b9592047d71e941679402f36d48e35%2FFrame%2070.png?generation=1704823500618022&alt=media" alt="">

💴 For Commercial Usage: Full version of the dataset includes 1000+ photos of people with different stages of hair loss, leave a request on TrainingData to buy the dataset

The balding dataset is a valuable resource in understanding the progression of hair loss in women, better diagnosis, treatment evaluation, and tracking of results. It can be used in developing machine learning algorithms to automate hair loss detection and potentially improve early intervention methods.

OTHER BIOMETRIC DATASETS:

• Bald Men - Bald Detection Dataset
• Dermatology Dataset - Acne, Skin Redness & Bags
• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The folder files includes: - 5 folders with images of each person in the dataset, identified by the corresponding number - each folder includes 5 images of a person from different angles: front, back, top-down, left side and right side

File with the extension .csv

• id: id of the person,
• gender: gender of the person,
• age: age of the person,
• ethnicity: ethnicity of the person,
• Top-Down: link to access the top-down photo,
• Back: link to access the back photo,
• Front: link to access the front photo,
• Left: link to access the left side's photo,
• Right: link to access the right side's photo

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric dataset, bald people, early balding, female pattern baldness, bald head, hair loss, hairline, bald spot, image dataset, bald dataset, hair segmentation, facial images, bald computer vision, bald classification, bald detection, balding women, baldness, bald scalp, bald head, biometric dataset, biometric data dataset, deep learning dataset, facial analysis, human images dataset, deep learning, machine learning, androgenetic alopecia, hair loss dataset, balding and non-balding

Coral reefs in every region of the world are threatened by climate change, no matter how remote or well protected. Identifying and protecting climate refugia is a popular recom mendation for coral reef management. Climate refugia are locations that maintain suit able environmental conditions for a resident species even when surrounding areas become inhospitable.

This paper shows that climate change will overwhelm current local-scale refugia, with declines in global thermal refugia from 84% of global coral reef pixels in the present-day climate to 0.2% at 1.5˚C, and 0% at 2.0˚C of global warming

Between January and September 2024, healthcare organizations in the United States saw 491 large-scale data breaches, resulting in the loss of over 500 records. This figure has increased significantly in the last decade. To date, the highest number of large-scale data breaches in the U.S. healthcare sector was recorded in 2023, with a reported 745 cases.

The largest reported data leakage as of January 2025 was the Cam4 data breach in March 2020, which exposed more than 10 billion data records. The second-largest data breach in history so far, the Yahoo data breach, occurred in 2013. The company initially reported about one billion exposed data records, but after an investigation, the company updated the number, revealing that three billion accounts were affected. The National Public Data Breach was announced in August 2024. The incident became public when personally identifiable information of individuals became available for sale on the dark web. Overall, the security professionals estimate the leakage of nearly three billion personal records. The next significant data leakage was the March 2018 security breach of India's national ID database, Aadhaar, with over 1.1 billion records exposed. This included biometric information such as identification numbers and fingerprint scans, which could be used to open bank accounts and receive financial aid, among other government services.

Cybercrime - the dark side of digitalization As the world continues its journey into the digital age, corporations and governments across the globe have been increasing their reliance on technology to collect, analyze and store personal data. This, in turn, has led to a rise in the number of cyber crimes, ranging from minor breaches to global-scale attacks impacting billions of users – such as in the case of Yahoo. Within the U.S. alone, 1802 cases of data compromise were reported in 2022. This was a marked increase from the 447 cases reported a decade prior. The high price of data protection As of 2022, the average cost of a single data breach across all industries worldwide stood at around 4.35 million U.S. dollars. This was found to be most costly in the healthcare sector, with each leak reported to have cost the affected party a hefty 10.1 million U.S. dollars. The financial segment followed closely behind. Here, each breach resulted in a loss of approximately 6 million U.S. dollars - 1.5 million more than the global average.

The classical method for evaluating the waveguide ability only focuses on the optical loss coefficient. However, for the micro- or submicroscale, an organic waveguide is demonstrated by the present study whose scale effect should not be neglected. We found that the optical loss coefficient increased remarkably when decreasing the sectional size of the microfibers. Furthermore, simulations based on Finite-Difference Time-Domain also demonstrated the size-dependent effect of the waveguide. Both the experimental and simulating results showed that the optical loss coefficient converges to a certain value, which means that the scale effect can be neglected as the sectional size is large enough. On the basis of the present study, we suggest that the scale-dependent effect on the sectional size of the waveguide should be investigated by evaluating the waveguide ability by the optical loss coefficient.

Description

Excerpted from: http://attrition.org/dataloss/dldos.html

Since July of 2005, attrition.org has been tracking data loss and data theft incidents (whether confirmed, unconfirmed, or disputed) not just from the United States, but across the world. This list includes incidents that may or may not have resulted in information exposure. Our archives go back to the year 2000, and with over 136 MILLION records compromised in over 300 incidents across six years (as of August 30, 2006), we would finally like to introduce a very basic and rudimentiary database that will assist others in tracking these incidents.

DLDOS (Data Loss Database - Open Source) is a simple flat comma seperated value file that can be imported into your database of choice, whether it be MySQL, Microsoft Access, or Oracle (good luck). We provide the date, the company that reported the breach, the type of data impacted, the number of records impacted, third party companies involved, and a few other sortable items that may be of interest. At this point, attrition.org is not hosting an actual database itself, but the raw data is free and available for non-commercial use as long as attrition.org is credited for the use of said data. Really, we're not trying to be jerks, but if you're going to use our data in your research, be it a web site or paper written for a commercial entity, please contact us. A key for DLDOS is also available.

Openness: NOT OPEN

License: at bottom of http://attrition.org/dataloss/:

Copyright 2005-2008 by attrition.org. Permission is granted to use this page and other Data Loss resources in non-profit works and research. Use of these resources for commercial interests requires authorization and licensing arrangements. For more information, please e-mail staff@attrition.org with a brief summary of how you would like to use this information; product, service, research, etc.

China Scale: YoY: Number of Loss Making Enterprise data was reported at 31.250 % in Oct 2015. This records an increase from the previous number of 17.647 % for Sep 2015. China Scale: YoY: Number of Loss Making Enterprise data is updated monthly, averaging 9.800 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 80.000 % in Dec 2011 and a record low of -26.316 % in Feb 2015. China Scale: YoY: Number of Loss Making Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Oven, Fan, Scale, Packaging Equipment, etc.: Scale.

U.S. DATA LOSS PREVENTION SOFTWARE MARKET valued USD 0.6 Billion in 2024 and is projected to surpass USD 3.2 Billion through 2032

OverviewThis data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2020 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced. This data set has been updated five times since its creation, and now includes loss up to 2020 (Version 1.8). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2020, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective logging, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.8 should be performed with caution. Read more about the Version 1.8 update here. When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.Frequency of updates: AnnualDate of content: 2001-2020Resolution: 30x30m

The size of the Data Center Generator Market market was valued at USD 7.92 billion in 2023 and is projected to reach USD 12.97 billion by 2032, with an expected CAGR of 7.3 % during the forecast period. The data center generator market refers to the supply of emergency power solutions that are to be incorporated in data centers to prevent disruption of functioning. These generators are used for powering critical operations that are required during power failure and to support the IT systems as well as protect against data losses. Small-scale examples are data centres, web hosting service providers, interconnect facilities and carrier-neutral colocation facilities. Recent trends in the market also indicate increasing concern towards the use of energy-saving and eco-safe technologies, like dual-frequency generators including renewable power sources. Also, continuing progress in technology is providing impulses; for instance, IoT for real-time monitoring with predictive maintenance possibilities. By nature, the market will continue to experience elevated growth, especially as the consum Recent developments include: In January 2023, EdgeCloudLink (ECL), a startup offering data center-as-a-service, announced its off-grid modular data centers powered by hydrogen. These data centers will be constructed using 3D printing technology in 1MW units. ECL has initiated its first data center project at its headquarters in Mountain View, California, collaborating with a local building services company and utilizing a construction 3D printer. The innovative design will rely on hydrogen from a local source and incorporate a proprietary liquid cooling system. ECL has assured that these hydrogen-powered data centers will operate without diesel generators, further enhancing their sustainability and efficiency , In May 2023, Kohler Power Systems introduced a web-based version to replace its widely used Power Solutions Center desktop app, which is utilized for generator sizing and KOHLER North American industrial product specifications. The newly developed web-based platform, the Power Solutions Center (PSC), offers users convenient access to technical data directly through KohlerPower.com. Users can retrieve product guide specifications, building information modeling (BIM) files, product drawings, and genset performance information through the free PSC software, enhancing their experience and streamlining the process of accessing essential information , In October 2022, LCL, a Belgian data center company, used hydrotreated vegetable oil (HVO) to replace diesel in its backup generators. The company announced that LCL Brussels-West in Aalst, which invested in six new 2.25 MVA generators, would be the first site to adopt this biodiesel. The facility comprises eight backup generators, including two older 1MW units and six new HVO-powered generators. This transition from diesel to renewable fuel sources demonstrates LCL's commitment to sustainability. It paves the way for DC generator manufacturers to innovate and develop advanced products in line with renewable energy requirements , In November 2022, Kohler Power Systems inaugurated the production expansion at its existing generator manufacturing facility in Wisconsin, U.S. This expansion aims to enhance Kohler's manufacturing capabilities in North America, specifically to cater to critical strategic industries such as data centers. By increasing its production capacity, Kohler Power Systems is well-positioned to meet the growing demands of these industries .

China Scale: YoY: Loss Amount: Year to Date data was reported at 567.281 % in Oct 2015. This records an increase from the previous number of 501.185 % for Sep 2015. China Scale: YoY: Loss Amount: Year to Date data is updated monthly, averaging 17.650 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 586.243 % in Jul 2015 and a record low of -47.361 % in Apr 2014. China Scale: YoY: Loss Amount: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Oven, Fan, Scale, Packaging Equipment, etc.: Scale.

The Data Classification Software market is experiencing robust growth, driven by increasing concerns around data privacy regulations (like GDPR and CCPA), rising cyber threats, and the exponential growth of unstructured data. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This growth is fueled by the widespread adoption of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness compared to on-premises deployments. Large enterprises are currently the primary adopters, but the market is witnessing significant expansion among SMEs due to increasing awareness of data security and compliance requirements. Key trends include the integration of AI and machine learning for automated classification, the rise of data loss prevention (DLP) solutions integrated with data classification, and a growing emphasis on granular access control based on classified data sensitivity. However, market growth is constrained by factors such as the complexity of implementing data classification solutions, the high initial investment costs for large-scale deployments, and the ongoing need for skilled professionals to manage and maintain these systems. The competitive landscape is highly fragmented, with established players like Microsoft, IBM, and Amazon competing against specialized vendors like Netwrix and Varonis Systems. The market is witnessing increased innovation in areas like automated classification and integration with other security tools, leading to greater efficiency and cost savings for organizations. The geographical distribution shows strong growth in North America and Europe, with Asia Pacific emerging as a rapidly expanding region due to increasing digitalization and stringent data governance regulations. The on-premises segment is gradually declining in favor of the more agile and scalable cloud-based solutions.

This is the replication package of the manuscript "Beyond Division: Loss-Aversion and Security as Dominant Values of Polarized Societies" see abstract below.Researchers mainly associate political polarization with profound ideological differences, values, and ideals. Strongly polarized societies, however, are similar to each other and differ from less polarized societies in terms of the values their citizens prioritize. This paper aims to introduce an important empirical finding on the cultural and value background of political polarization, opening up a novel perspective in the research of polarization, which, until now, has remained understudied in the literature. It demonstrates that people in more polarized societies are more firmly attached to anxiety-based values, have more conservative value profiles, and are especially concerned about security. The analysis relies on data from the European Social Survey (ESS) collected between 2002 and 2022, resulting in a maximum of 232 observations from 32 countries. As values and cultural dimensions change slowly over time, the hypothesis emerges that they are drivers of – or at least risk factors for - polarization. The results enhance our knowledge of the context and background of polarization, suggesting that polarization may be deeply embedded in cultural, ideological, and value factors.

APLE is a Microsoft Excel spreadsheet model that runs on an annual time-step and estimates field-scale, sediment bound and dissolved P loss (kg ha−1) in surface runoff for agricultural field. APLE is intended to quantify P loss through process-based equations. It has been tested for its ability to reliably predict P loss in runoff for systems with machine-applied manure and for soil P cycling using data from a wide variety of agricultural fields and regions. Resources in this dataset:Resource Title: Annual P Loss Estimator (APLE). File Name: APLE 2.5.2.xlsxResource Description: APLE is a fairly simple, user-friendly, Microsoft Excel spreadsheet model that runs on an annual time-step and estimates field-scale, sediment bound and dissolved P loss (kg ha−1) in surface runoff for agricultural field. To download the spreadsheet, fill out the form at https://www.ars.usda.gov/research/software/download/?softwareid=304 Resource Title: Annual Phosphorus Loss Estimator User’s Manual Version 2.4. File Name: APLEUsersManual24.pdf

Explore the growth potential of Market Research Intellect's Email Data Loss Prevention Market Report, valued at USD 3.5 billion in 2024, with a forecasted market size of USD 6.5 billion by 2033, growing at a CAGR of 8.2% from 2026 to 2033.

Simulation data and model belonging to the manuscript 'Solving the SLoSS debate: Scale-dependent effects of habitat fragmentation on biodiversity loss', by Monique de Jager and Edwin Pos. The folder 'Generated data' holds the generated simulation data. The folder 'Model' contains the 2-dimensional, semi-spatial, near-neutral, individual-based model. A description of the model can be found in the file 'README.md'.

CN: Scale: No of Loss Making Enterprise data was reported at 21.000 Unit in Oct 2015. This records an increase from the previous number of 20.000 Unit for Sep 2015. CN: Scale: No of Loss Making Enterprise data is updated monthly, averaging 28.000 Unit from Dec 1998 (Median) to Oct 2015, with 102 observations. The data reached an all-time high of 59.000 Unit in Feb 2010 and a record low of 10.000 Unit in Dec 2011. CN: Scale: No of Loss Making Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHW: Oven, Fan, Scale, Packaging Equipment, etc.: Scale.

This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, measures areas of tree cover loss across all global land (except Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.This data set has been updated five times since its creation, and now includes loss up to 2022 (Version 1.10). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2022, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Eventually, a future “Version 2.0” will include reprocessing for 2000-2010 data, but in the meantime integrated use of the original data and Version 1.7 should be performed with caution. Read more about the Version 1.7 update here.When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.

Global Data Loss Prevention market size is expected to reach $9.76 billion by 2029 at 27.5%, segmented as by managed security services, continuous monitoring, incident response