Contains scans of a bin filled with different parts ( screws, nuts, rods, spheres, sprockets). For each part type, RGB image and organized 3D point cloud obtained with structured light sensor are provided. In addition, unorganized 3D point cloud representing an empty bin and a small Matlab script to read the files is also provided. 3D data contain a lot of outliers and the data were used to demonstrate a new filtering technique.

This data set combines organized crime and GDP to make an correlation analysis. I added also the ISO3-Codes to represent them with plotly. Additionally there is one data set that already merged everything together.

This paper studies the long-run economic impact of dismissing city councils infiltrated by organized crime. Applying a matched difference-in-differences design to the universe of Italian social security records, we find that city council dismissals (CCDs) increase employment, the number of firms, and industrial real estate prices. The effects are concentrated in Mafia-dominated sectors and in municipalities where fewer incumbents are re-elected. The dismissals generate large economic returns by weakening the Mafia and fostering trust in local institutions. The analysis suggests that CCDs represent an effective intervention for establishing legitimacy and spurring economic activity in areas dominated by organized crime.

Reorganized version of Wild-Heart/Disney-VideoGeneration-Dataset. This is needed for Mochi-1 fine-tuning.

A collection of 3 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Over a hundred seed-based resting state functional connectivity maps (group averages of 280 healthy young adults) were decomposed into 3 interdependent gradients using multidimensional scaling.

Dataset is about the HSI Operation Popeye which was conducted in collaboration with partners in Italy, Colombia, and the United States to dismantle multiple organized crime groups operating in domestically and in Europe, while simultaneously disrupting a Colombian drug cartel representing one of the largest suppliers of cocaine and fentanyl to Europe and North America. Operation Popeye led to the seizure of 4.4 tons of cocaine, 66 kilograms of heroin, €1.85 million (approximately $2.04 million), and 63 arrests. This investigation leveraged HSI’s unique domestic and international authorities and resources to coordinate one of the largest and most complex undercover operations ever accomplished through United States and international law enforcement partnerships.

This dataset contains the images and data used in the the paper "Topologically Organized Networks in the Claustrum Reflect Functional Modularization".

This dataset focuses on CTOC oversees programmatic areas targeting TCOs involved in money laundering, financial fraud, bulk cash smuggling, document fraud, benefit fraud, labor exploitation, human smuggling, narcotics trafficking, racketeering, violent gang activity, and other crimes

This dataset is about books. It has 3 rows and is filtered where the book subjects is Organized crime-Nevada-Las Vegas-History-20th century. It features 9 columns including author, publication date, language, and book publisher.

Professional Organizer Market Outlook

The global professional organizer market size was valued at approximately USD 8.2 billion in 2023 and is projected to reach USD 12.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.2% during the forecast period. This market is driven by the increasing demand for organizational services to combat clutter, improve productivity, and streamline operations across both residential and business environments.

The growth of the professional organizer market is primarily fueled by the surge in consumer awareness regarding the benefits of an organized space. In a fast-paced world where time management and efficiency are crucial, organized environments contribute to reduced stress levels and increased productivity. The rise of minimalism as a lifestyle choice has also promoted the need for professional organizing services. As people increasingly seek to declutter and simplify their lives, the demand for these services has soared. Additionally, the COVID-19 pandemic has heightened the awareness of the importance of a well-organized home, as many individuals have transitioned to working from home, requiring optimized spaces for productivity.

Technological advancements have further propelled the growth of the professional organizer market. With the advent of specialized software and apps that assist in organization and time management, professionals in this field are able to enhance their service offerings. These tools allow for efficient space planning, inventory management, and scheduling, thus providing a comprehensive solution for clients. Moreover, virtual organizing services have gained momentum, enabling organizers to offer their expertise remotely, which expands their reach and caters to a global clientele. This technical evolution is expected to continue driving market growth.

Another significant growth factor is the increasing tendency of businesses to outsource non-core activities, including organizing services. Corporations recognize that a well-organized office can lead to improved employee morale, better time management, and ultimately, enhanced productivity. As such, professional organizing services are being integrated into corporate wellness programs and employee benefit packages. The trend is particularly noticeable in small to medium-sized enterprises (SMEs) that may lack the internal resources to maintain an orderly workspace. Outsourcing such services allows these businesses to focus on their core competencies while ensuring a productive work environment.

The regional outlook for the professional organizer market reveals varied growth patterns across different geographies. North America holds a significant share due to high consumer awareness and the prevalence of professional organizing businesses. Europe follows closely, with increasing adoption of organizing services in both residential and corporate settings. The Asia Pacific region is expected to witness the highest growth rate due to rapid urbanization, increasing disposable incomes, and the growing adoption of Western lifestyles. Latin America and the Middle East & Africa are also expected to contribute to market growth, albeit at a slower pace, as awareness and adoption gradually increase in these regions.

Service Type Analysis

The professional organizer market is segmented by service type into residential organizing, business organizing, virtual organizing, and others. Residential organizing services are in high demand as homeowners increasingly seek to declutter and optimize their living spaces. The rise of home-based work and activity during the COVID-19 pandemic has further amplified this demand. Professional organizers assist clients in managing household items, creating efficient storage solutions, and maintaining an orderly environment, which contributes to improved mental well-being and productivity.

Business organizing services cater to the needs of corporate clients, ranging from small businesses to large enterprises. These services include office space planning, document management, workflow optimization, and time management training. Businesses recognize the importance of a well-organized workspace in fostering a productive and efficient work environment. As companies strive to enhance employee performance and reduce operational bottlenecks, the demand for business organizing services continues to grow.

Virtual organizing has emerged as a significant segment within the professional organizer market. Leveraging technology, organizers offer remote consultations and guidance, allowing cl

This feature layer provides digital tax parcels for the Organized Towns of the State of Maine. Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. "Maine Parcels Organized Towns Feature" and "Maine Parcels Organized Towns ADB" are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions. In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), the Maine Revenue Service is the authoritative source for parcel data. "Maine Parcels Unorganized Territory Feature" is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services (https://www.maine.gov/revenue/taxes/property-tax) to compile up-to-date parcel ownership information. Property maps are a fundamental base for many municipal activities. Although GIS parcel data cannot replace detailed ground surveys, the data can assist municipal officials with functions such as accurate property tax assessment, planning and zoning. Towns can link maps to an assessor's database and display local information, while town officials can show taxpayers how proposed development or changes in municipal services and regulations may affect the community. In many towns, parcel data also helps to provide public notices, plan bus routes, and carry out other municipal services.

This dataset contains municipality-submitted parcel data along with previously developed parcel data acquired through the Municipal Grants Project supported by the Maine Library of Geographic Information (Maine GeoLibrary). Grant recipient parcel data submissions were guided by standards presented to the Maine GeoLibrary Board on May 21, 2005, which are outlined in the "Standards for Digital Parcel Files" document available on the Maine GeoLibrary publications page (https://www.maine.gov/geolib/policies/standards.html). This dataset also contains municipal parcel data acquired through other sources; the data sources are identified (where available) by the field “FMSCORG”. Note: Join this feature layer with the "Maine Parcels Organized Towns ADB" table (https://maine.hub.arcgis.com/maps/maine::maine-parcels-organized-towns-feature/about?layer=1) for available ownership information. A date field, “FMUPDAT”, is attributed with the most recent update date for each individual parcel if available. The "FMUPDAT" field will not match the "Updated" value shown for the layer. "FMUPDAT" corresponds with the date of update for the individual data, while "Updated" corresponds with the date of update for the ArcGIS Online layer as a whole. Many parcels have not been updated in several years; use the "FMUPDAT" field to verify currency.

Contains the Supplemental Appendix, Stata Do File, and various datasets/codebooks associated with the tables and figures in the Social Science Quarterly paper.

The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflects a smooth mapping of a unified representational space.

The goal of this study was to assess the nature and scope of Soviet emigre crime in the United States, with a special focus on the question of whether and how well this crime was organized. The research project was designed to overcome the lack of reliable and valid knowledge on Soviet emigre crime networks through the systematic collection, evaluation, and analysis of information. In Part 1, the researchers conducted a national survey of 750 law enforcement specialists and prosecutors to get a general overview of Soviet emigre crime in the United States. For Parts 2-14, the researchers wanted to look particularly at the character, operations, and structure, as well as the criminal ventures and enterprises, of Soviet emigre crime networks in the New York-New Jersey-Pennsylvania region. They were also interested in any international criminal connections to these networks, especially with the former Soviet Union. The investigators focused particularly on identifying whether these particular networks met the following criteria commonly used to define organized crime: (1) some degree of hierarchical structure within the network, (2) continuity of that structure over time, (3) use of corruption and violence to facilitate and protect criminal activities, (4) internal discipline within the network structure, (5) involvement in multiple criminal enterprises that are carried out with a degree of criminal sophistication, (6) involvement in legitimate businesses, and (7) bonding among participants based upon shared ethnicity. Data for Parts 2-14 were collected from a collaborative effort with the Tri-State Joint Project on Soviet Emigre Organized Crime. From 1992 through 1995 every investigative report or other document produced by the project was entered into a computer file that became the database for the network analysis. Documents included undercover observation and surveillance reports, informant interviews, newspaper articles, telephone records, intelligence files from other law enforcement agencies, indictments, and various materials from the former Soviet Union. Every individual, organization, and other entity mentioned in a document was considered an actor, given a code number, and entered into the database. The investigators then used network analysis to measure ties among individuals and organizations and to examine the structure of the relationships among the entries in the database. In Part 1, National Survey of Law Enforcement and Prosecutors Data, law enforcement officials and prosecutors were asked if their agency had any contact with criminals from the former Soviet Union, the types of criminal activity these people were involved in, whether they thought these suspects were part of a criminal organization, whether this type of crime was a problem for the agency, whether the agency had any contact with governmental agencies in the former Soviet Union, and whether anyone on the staff spoke Russian. Part 2, Actor Identification Data, contains the network identification of each actor coded from the documents in Part 3 and identified in the network data in Parts 4-14. An actor could be an individual, organization, concept, or location. Information in Part 2 includes the unique actor identification number, the type of actor, and whether the actor was a "big player." Part 3, Sources of Data, contains data on the documents that were the sources of the network data in Parts 4-14. Variables include the title and date of document, the type of document, and whether the following dimensions of organized crime were mentioned: sources of capital, locational decisions, advertising, price setting, financial arrangements, recruitment, internal structure, corruption, or overlapping partnerships. Parts 4-14 contain the coding of the ties among actors in particular types of documents, and are named for them: indictments, tips, investigative reports, incident reports, search reports, interview reports, arrest reports, intelligence reports, criminal acts reports, confidential informant reports, newspaper reports, social surveillance reports, other surveillance reports, and company reports.

This repository serves as a comprehensive resource for replicating the findings presented in the research paper titled "How prejudice shapes public perceptions of minority-organized spaces: the case of community education," authored by Julia Steenwegen and Maurits J. Meijers and published in the Journal of Ethnic and Migration Studies. The repository includes the following components: README.txt: This text file serves as the main entry point for the repository. It provides an overview of the replication files, instructions for replication, and additional notes for users. data.sav: This file contains the dataset utilized in the study. It is formatted for use with Stata statistical software. exp_study.do: Stata script for the experimental study. This script contains the code used to analyze data and generate results for the experimental portion of the study. obs_study.do: Stata script for the observational study. This script contains the code used to analyze data and generate results for the observational portion of the study.

There are many ways for girls to stay active, including joining sports teams, participating in recreational leagues, or taking part in school sports. During an April 2019 survey, 27 percent of girls who played sports in the United States were part of a school team, while 53 percent were part of a local recreational team.

According to the organized crime index, resilience to organized crime in Africa was highest in West Africa with an average score of 4.28 points. Central Africa, on the other hand, ranked last with 3.23 index points.

The economic costs of organized crime have been estimated for the case of southern Italy by Pinotti (Economic Journal 2015; 125, F203?F232, 2015): using synthetic control methods, he finds that, due to the advent of the Italian Mafia in the regions Apulia and Basilicata, GDP per capita dropped by 16%. Replicating this study in a narrow sense by estimating the same model with the same data, but using different software implementations, we observe minor differences stemming from the different implementations. By identifying the correct implementation, we find that the loss in GDP per capita due to the presence of the Mafia has been slightly overestimated.

Series Name: Gender parity index for participation rate in organized learning (one year before the official primary entry age) (ratio)Series Code: SE_PRE_GPIPARTNRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedTarget 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsGoal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNAseq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster, we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.