List of Top Authors of Studies in Classification, Data Analysis, and Knowledge Organization sorted by citations.

here, we provide plankton image data that was sorted with the web applications ecotaxa and morphocluster. the data set was used for image classification tasks as described in schröder et. al (in preparation) and does not include any geospatial or temporal meta-data.plankton was imaged using the underwater vision profiler 5 (picheral et al. 2010) in various regions of the world's oceans between 2012-10-24 and 2017-08-08.this data publication consists of an archive containing "training.csv" (list of 392k training images for classification, validated using ecotaxa), "validation.csv" (list of 196k validation images for classification, validated using ecotaxa), "unlabeld.csv" (list of 1m unlabeled images), "morphocluster.csv" (1.2m objects validated using morphocluster, a subset of "unlabeled.csv" and "validation.csv") and the image files themselves. the csv files each contain the columns "object_id" (a unique id), "image_fn" (the relative filename), and "label" (the assigned name).the training and validation sets were sorted into 65 classes using the web application ecotaxa (http://ecotaxa.obs-vlfr.fr). this data shows a severe class imbalance; the 10% most populated classes contain more than 80% of the objects and the class sizes span four orders of magnitude. the validation set and a set of additional 1m unlabeled images were sorted during the first trial of morphocluster (https://github.com/morphocluster).the images in this data set were sampled during rv meteor cruises m92, m93, m96, m97, m98, m105, m106, m107, m108, m116, m119, m121, m130, m131, m135, m136, m137 and m138, during rv maria s merian cruises msm22, msm23, msm40 and msm49, during the rv polarstern cruise ps88b and during the fluxes1 experiment with rv sarmiento de gamboa.the following people have contributed to the sorting of the image data on ecotaxa:rainer kiko, tristan biard, benjamin blanc, svenja christiansen, justine courboules, charlotte eich, jannik faustmann, christine gawinski, augustin lafond, aakash panchal, marc picheral, akanksha singh and helena haussin schröder et al. (in preparation), the training set serves as a source for knowledge transfer in the training of the feature extractor. the classification using morphocluster was conducted by rainer kiko. used labels are operational and not yet matched to respective ecotaxa classes.

We developed an experimental method that can be used to study organization design and grouping decisions more specifically. We demonstrate the method in a study with 285 participants. The participants were asked to group a set of nine roles into units using card-sorting. The role descriptions indicated that there were interdependencies between some of the roles. Participants’ grouping decisions were quantified and compared against an algorithmic solution that minimized coordination costs. It was found that a relatively small difference in task complexity between groups greatly affected participants’ performance. The files that are uploaded here contain the raw data and "distance scores" for study of how people make organization design decisions. See the appendices in the article for tips on how to set up similar studies.

Meet Thomas McElrath, a insect collection manager at the Illinois Natural History Survey and beetle researcher. Tommy explains the value of data standards while discussing beetles and variations in sex.

List of Top Schools of Studies in Classification, Data Analysis, and Knowledge Organization sorted by citations.

Card Sorting Software Market Outlook

According to our latest research, the global card sorting software market size reached USD 690 million in 2024, reflecting robust adoption across industries. The market is expected to grow at a CAGR of 13.6% from 2025 to 2033, reaching an estimated USD 2.16 billion by 2033. This significant growth is driven by the rising emphasis on user experience (UX) optimization, digital transformation initiatives, and the increasing complexity of digital products that require intuitive information architecture.

One of the primary growth drivers for the card sorting software market is the accelerating focus on user-centered design by organizations worldwide. As digital interfaces become more complex, businesses are leveraging card sorting tools to simplify navigation structures and enhance usability. This trend is particularly pronounced in sectors such as e-commerce, IT, and healthcare, where a seamless user experience directly correlates with customer satisfaction and retention. The proliferation of digital transformation initiatives further fuels demand, as companies seek to streamline their digital assets and ensure that websites, apps, and platforms are intuitively organized. Furthermore, the integration of card sorting software with other UX research and prototyping tools is making it easier for teams to collaborate remotely, thereby expanding the market’s reach.

The increasing adoption of agile development methodologies and rapid prototyping is also propelling the growth of the card sorting software market. Agile teams, working in fast-paced environments, require efficient tools to continuously test and refine information architecture. Card sorting software enables quick feedback loops, allowing designers and product managers to make data-driven decisions that improve the user journey. This capability is especially valuable in industries facing frequent content updates or evolving user needs, such as media, education, and SaaS platforms. Additionally, the trend towards remote work has amplified the need for cloud-based card sorting solutions, as geographically dispersed teams seek to maintain productivity and collaboration.

Another key growth factor is the expansion of digital learning and academic research, where card sorting software is increasingly used to teach information architecture principles and conduct usability studies. Educational institutions and research organizations are leveraging these tools to engage students in practical UX exercises and gather insights into cognitive processes related to content categorization. As educational technology (EdTech) investments surge globally, the demand for versatile, user-friendly card sorting platforms is expected to rise. Moreover, the availability of customizable templates, analytics dashboards, and integration capabilities with learning management systems (LMS) is making card sorting software more accessible to non-technical users, further broadening its adoption.

From a regional perspective, North America currently dominates the card sorting software market, accounting for over 38% of global revenue in 2024. This leadership is attributed to the region’s advanced digital infrastructure, high concentration of technology companies, and early adoption of UX best practices. However, Asia Pacific is poised for the fastest growth during the forecast period, driven by rapid digitalization in emerging economies, increasing investments in IT and education, and a burgeoning startup ecosystem. Europe also demonstrates strong potential, particularly in sectors like healthcare, fintech, and retail, where regulatory requirements and customer-centric strategies are fueling demand for sophisticated UX tools. As organizations across all regions prioritize digital transformation, the global card sorting software market is set for sustained expansion.

Deployment Mode Analysis

The deployment mode segment of the card sorting software market is bifurcated into cloud-based and on-premises solutions, each catering to distinct organizational needs and security considerations. Cloud-based card sorting software has gained substantial traction in recent years, primarily due to its scalability, cost-effectiveness, and ease of access. Organizations are increasingly favoring cloud deployment to support remote workforces and facilitate real-time collaboration among globally distributed t

Card Sorting Software Market Outlook

According to our latest research, the global Card Sorting Software market size in 2024 stands at USD 295.7 million, reflecting the rapid adoption of user experience (UX) optimization tools across industries. The market is poised for robust growth, projected to reach USD 941.6 million by 2033, expanding at a CAGR of 13.8% during the forecast period. This significant growth is primarily driven by the escalating demand for intuitive digital interfaces, the proliferation of digital transformation initiatives, and the increasing emphasis on usability testing and information architecture design across diverse end-user verticals.

The surge in digitalization across businesses is a foundational growth driver for the Card Sorting Software market. As organizations strive to create seamless and engaging user experiences, the need to understand user behavior and optimize information structures has become paramount. Card sorting software provides a systematic approach for gathering user input on content organization, enabling product teams to make data-driven decisions that enhance usability. With the expansion of web and mobile applications, businesses are increasingly investing in advanced UX research methodologies, further propelling the adoption of card sorting software solutions globally. The integration of card sorting tools with broader UX research platforms and analytics suites is also amplifying their value proposition, making them indispensable in the modern product development lifecycle.

Another critical factor influencing market growth is the rising complexity of digital content and the diversity of user groups. As enterprises expand their digital offerings, managing and structuring vast volumes of information becomes a challenge. Card sorting software enables organizations to capture real-world insights from target users, ensuring that navigation structures, content hierarchies, and workflows align with user expectations. This is particularly vital in sectors such as healthcare, retail, and education, where clear information architecture directly impacts user satisfaction and operational efficiency. The growing focus on accessibility and inclusivity in digital design is also encouraging the adoption of card sorting methodologies, as these tools help identify and address usability barriers faced by diverse user segments.

The proliferation of remote work and distributed teams has accelerated the shift towards cloud-based card sorting solutions, further fueling market expansion. Cloud deployment offers unparalleled flexibility, scalability, and collaboration capabilities, allowing organizations to conduct usability studies with participants from different geographical locations. This has democratized access to UX research tools, enabling small and medium enterprises (SMEs) to leverage advanced card sorting software without significant upfront investments in IT infrastructure. Additionally, the integration of artificial intelligence and machine learning into card sorting platforms is enhancing data analysis, providing deeper insights, and automating the identification of user patterns, thereby increasing the softwareÂ’s effectiveness and adoption rate.

From a regional perspective, North America currently leads the Card Sorting Software market, driven by the presence of major technology companies, a mature digital ecosystem, and high awareness of UX best practices. Europe and Asia Pacific are also witnessing substantial growth, fueled by digital transformation initiatives, regulatory mandates for digital accessibility, and the rapid expansion of the e-commerce and healthcare sectors. Emerging markets in Latin America and the Middle East & Africa are gradually embracing card sorting software, supported by rising internet penetration and increasing investments in digital infrastructure. The competitive landscape remains dynamic, with both established vendors and innovative startups introducing feature-rich solutions to cater to evolving customer needs.

In parallel with card sorting software, Conjoint Analysis Software is gaining traction as a vital tool for understanding user preferences and decision-making processes. This software allows businesses to dissect complex decision-making scenarios by evaluating how users value different

We propose a novel methodology to uncover the sorting pattern in labor markets. We identify the strength of sorting solely from a ranking of firms by profits. To discern the sign of sorting, we build a noisy ranking of workers from wage data. Our test for the sign of sorting is consistent even with noisy worker rankings. We apply our approach to a panel dataset that combines social security earnings records with detailed financial data for firms in the Veneto region of Italy. We find robust evidence of positive sorting. The correlation between worker and firm types is about 52%.

Reverse-Logistics Sorting-Display Market Outlook

As per our latest research, the global Reverse-Logistics Sorting-Display market size reached USD 4.2 billion in 2024, with robust momentum driven by the increasing complexity of supply chains and the surging volume of returns across industries. The market is projected to grow at a CAGR of 9.1% during the forecast period, with the market size anticipated to reach USD 9.3 billion by 2033. This upward trajectory is primarily attributed to the rapid adoption of advanced sorting technologies and the growing emphasis on sustainability and resource optimization in reverse logistics operations worldwide.

The primary growth factor for the Reverse-Logistics Sorting-Display market is the escalating volume of product returns, especially in the retail and e-commerce sectors. With consumers increasingly expecting hassle-free return policies and retailers striving to maintain customer satisfaction, efficient reverse logistics systems have become essential. The proliferation of online shopping platforms has amplified return rates, necessitating sophisticated sorting and display solutions to manage, process, and reintegrate returned goods seamlessly. This demand has stimulated significant investments in automated and semi-automated sorting technologies, enabling faster turnaround times, reduced operational costs, and enhanced accuracy in sorting processes.

Another critical driver is the rapid technological advancement in sorting-display systems, particularly the integration of artificial intelligence, machine learning, and IoT-enabled devices. These innovations have revolutionized how returned goods are identified, categorized, and redirected within supply chains. Smart sensors, real-time data analytics, and automated sorting mechanisms contribute to higher productivity, reduced manual intervention, and improved traceability. As organizations seek to minimize losses from returns and maximize resource recovery, the adoption of these cutting-edge technologies is accelerating, further propelling the growth of the Reverse-Logistics Sorting-Display market.

Sustainability concerns and regulatory pressures are also shaping the market landscape. Companies are under increasing scrutiny to manage waste effectively, reduce their carbon footprint, and comply with environmental regulations related to product disposal and recycling. Reverse logistics sorting-display solutions enable organizations to efficiently separate reusable, recyclable, and disposable items, facilitating circular economy initiatives. By optimizing the reverse flow of goods, businesses not only achieve compliance but also unlock new revenue streams through remanufacturing, refurbishment, and secondary sales, reinforcing the market’s upward momentum.

Regionally, North America and Europe are at the forefront of the Reverse-Logistics Sorting-Display market, driven by mature retail and e-commerce ecosystems, stringent environmental regulations, and significant investments in automation. However, Asia Pacific is rapidly emerging as a high-growth region, fueled by expanding consumer markets, increasing digitalization, and the rise of organized retail. The region’s manufacturing hubs and growing third-party logistics sector are also contributing to the adoption of advanced reverse logistics solutions. Latin America and the Middle East & Africa, while still nascent, are expected to witness steady growth as businesses in these regions recognize the value of efficient return management and sustainability.

Component Analysis

The Component segment of the Reverse-Logistics Sorting-Display market comprises hardware, software, and services, each playing a pivotal role in the overall ecosystem. Hardware solutions form the backbone of sorting-display systems, encompassing conveyor belts, scanners, sensors, and display units that facilitate the physical movement and identification of returned goods. The demand for robust, scalable, and energy-efficient hardware is particularly high a

This dataset contains results presented in Nawaz, Urbanska, Herbig et al ‘Intelligent image-based deformation-assisted cell sorting with molecular specificity’ Nat Methods 17, 595–599 (2020)https://doi.org/10.1038/s41592-020-0831-y (preprint available at https://doi.org/10.1101/862227) and includes:(i) 29 '*.rtdc' files containing measurements performed with real time fluorescence and deformability cytometry 1,2 1 '*.fcs' file containing a measurement performed with a standard flow cytometer (BD LSR II, BD Biosciences)(iii) 1 '*.xlsx' file containing results from AFM-based indentation experiments.Sorting real-time fluorescence and deformability cytometry (soRT-FDC) is a robust sorting platform, combining image-based morphological cell analysis with mechanical characterisation and subsequent active sorting by feeding real-time fluorescence and deformability cytometry (RT-FDC) [2] information to a down-stream SSAW-based cell sorter [3].Each filename of this dataset starts with the name of the figure to which the datafile corresponds. The measured samples are briefly described below. The 'Initial' measurements refer to the samples used for sorting, and the 'Target' measurements to the samples collected in the target outlet after sorting. For more detailed description of samples please refer to the manuscript.The '*.rtdc' files are HDF5 files and can be analysed using a Python library called dclab [4], or a software called Shape-Out [5]. They can also be opened using other HDF viewer programs.For the correct assignment of sample names when loading multiple ‘’*.rtdc’ measurements from one folder, please use ShapeOut1.0.0 or above. [1] Otto et al., "Real-time deformability cytometry: on-the-fly cell mechanical phenotyping". Nature Methods, 12(3):199–202, 2015. https://doi.org/10.1038/nmeth.3281[2] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. https://doi.org/10.1038/nmeth.4639[3] Nawaz et al., “Acoustofluidic Fluorescence Activated Cell Sorter”. Analitycial Chemistry 87(24): 12051–12058, 2015. https://doi.org/10.1021/acs.analchem.5b02398[4] https://github.com/ZellMechanik-Dresden/dclab[5] https://github.com/ZellMechanik-Dresden/ShapeOutFigure1d_01_Beads_FL_Initial.rtdc, Figure1d_02_Beads_FL_Target.rtdcsample: 1:5 mixture of polyacrylamide microgel beads labeled with AlexaFluor488 and unlabeled ones, these beads were produced in house; sorted for fluorescencesorting gates: FL-1 maximum 1000 – 10000, area ratio: 1.0 - 1.1Figure1d_03_Beads_Size_Initial.rtdc, Figure1d_04_Beads_Size_Target.rtdcsample: 1:6 mixture of 13.79 ± 0.59 μm silica beads (SiO2-F-L3519-1; Microparticles, Germany) and 17.23 ± 0.24 μm poly(methyl methacrylate) beads (PMMM-F-B1423; Microparticles); sorted for sizesorting gates: size 220 – 300 μm2, def 0.00 – 0.01, are ratio 1.0 – 1.1Figure1d_05_Beads_DefSize_Initial.rtdc, Figure1d_06_Beads_DefSize_Target.rtdcsample: a mixture of two polyacrylamide microgel bead populations of different stiffness, these beads were produced in house; sorted for deformation and sizesorting gates: size 95 – 105 μm2, def 0.000 – 0.016, are ratio 1.0 – 1.1Figure2b_Blood_Initial_Inlet.rtdcsample: RBC-depleted blood sample analysed in the inlet region of the chip Figure2c_Blood_Initial_Channel.rtdcsample: RBC-depleted blood sample analysed in the usual ROI at the end of the constricting channel Figure2d_Blood_Target_RBCs.rtdcsample: RBC-depleted blood, sorted RBCssorting gates: size 25 – 65 μm2, def 0.16 – 0.40, are ratio 1.0 – 1.1Figure2e_Blood_Target_ly.rtdcsample: RBC-depleted blood, sorted lymphocytessorting gates: size 25 – 45 μm2, def 0.00 – 0.10, are ratio 1.0 – 1.1Figure2f_Blood_Target_my.rtdcsample: RBC-depleted blood, sorted myeloid cellssorting gates: size 53 – 120 μm2, def 0.00 – 0.15, are ratio 1.0 – 1.1Figure2g_Blood_Initial.rtdc, Figure2h_Blood_Target_neuBr.rtdcsample: RBC-depleted blood, brightness-based neutrophils sorting* sorting gates: size 56 – 100 μm2, brightness 75 – 78, are ratio 1.0 – 1.1>> this is also source data for Extended Fig. 8Figure3_Blood_DNN_Training(1).rtdc, Figure3_Blood_DNN_Training(2).rtdcsample: RBC-depleted blood, datasets for training of deep neural network (DNN)*Figure3_Blood_DNN_Validation.rtdcsample: RBC-depleted blood, dataset for validation of trained DNN*Figure3d_Blood_Initial.rtdc, Figure3e_Blood_Target_neuDNN.rtdcsample: RBC-depleted blood, DNN-based neutrophil sorting**for identification of neutrophils (CD66+/CD14−), these samples were stained with APC-conjugated anti-human CD14 (dilution 1:20, #17- 809 0149-42, eBioscience, CA, USA; recorded with FL-3 channel) and PE-conjugated anti-human CD66a/c/e (dilution 1:40, 810 #34303, BioLegend, CA, USA; recorded with FL-2 channel); for cross-talk compensation of the fluorescence signal in channels FL-2 and FL-3 for these samples introduce the following correction factor in the ShapeOut software: spill from channel 2 to 3 = 0.025, spill from channel 3 to 2 = 0.100ExtData_Fig2a_BeadMix_RT-FDC.rtdc, ExtData_Fig2b_BeadMix_soRT-FDC.rtdc, ExtData_Fig2c_BeadMix_FC.fcssample: a mixture of 4 bead types with different diameter, each made of different material:9.78 ± 0.15 μm melamine beads (MF-FluoBlau-L948), 13.79 ± 0.59 μm silica beads (SiO2-F-L3519-1),15.21 ± 0.31 μm polystyrene beads (PS/Q-F-KM194), 17.23 ± 0.24 μm poly(methyl methacrylate) beads (PMMM-F-B1423), all purchased from Microparticles, Germany.ExtData_Fig3a_Beads_FLDef_Initial.rtdc, ExtData_Fig3a_Beads_FLDef_Target.rtdcsample: a mixture of fluorescent and non-fluorescent polyacrylamide beads with different mechanical properties, these beads were produced in house; sorted for fluorescence and deformationsorting gates: FL-1 maximum 1000 – 10000, def 0.01 – 0.02, are ratio 1.0 – 1.1ExtData_Fig3b_Beads_BrSize_Initial.rtdc, ExtData_Fig3b_Beads_BrSize_Target.rtdcsample: a mixture of 4 bead types as in Extended Data Fig. 2; sorted for brightness and sizesorting gates: size 160 – 240 μm2, brightness 75 – 85, are ratio 1.0 – 1.1ExtData_Fig4a_Kc167_HL60_Size_Initial.rtdc, ExtData_Fig4b_Kc167_HL60_Size_Target.rtdcsample: a mixture of Kc167 Drosophila cells and HL60/S4 human promyelocytic leukaemia cells, sorted for sizesorting gates: size 25 – 77 μm2, def 0.00 – 0.15, are ratio 1.0 – 1.1ExtData_Fig5a_RBC_Def_Initial.rtdc, ExtData_Fig5b_RBC_Def_Target1.rtdc, ExtData_Fig5c_RBC_Def_Target2.rtdcsample: blood anticoagulated with citrate, RBCs sorted for deformation sorting gates: def 0.15 – 0.40 (Target1) / def 0.00 – 0.10 (Target2), are ratio 1.0 – 1.2ExtData_Fig7_AFM_AfterSorting.xlsxsample: HL60/S4 cells collected after sorting experiment; the initial sample was prepared as for the soRT-FDC experiment but not loaded onto the chip, the default sample was collected in the default outlet, i.e., run through the sorting chip but not exposed to SSAW, and the target sample corresponds to cells exposed to SSAW; reported values correspond to apparent Young’s modulus estimated from AFM indentation experiments

Photo Organization Software Market Outlook

According to our latest research, the global photo organization software market size reached USD 1.82 billion in 2024, demonstrating robust momentum with a CAGR of 10.5% projected through the forecast period. By 2033, the market is expected to attain a size of USD 4.56 billion, driven by the increasing prevalence of digital photography, the proliferation of smart devices, and the need for efficient media management solutions among individuals and enterprises. The growth trajectory is further catalyzed by advances in artificial intelligence (AI) and machine learning, which are enhancing the automation and accuracy of photo sorting, tagging, and retrieval processes.

A primary growth factor for the photo organization software market is the exponential increase in digital content creation, particularly photographs, across both personal and professional domains. The ubiquity of smartphones and digital cameras has resulted in a massive influx of images that require systematic organization, storage, and retrieval. This surge in digital assets is compelling users to seek sophisticated photo organization solutions that offer features such as automated sorting, facial recognition, geotagging, and cloud synchronization. Moreover, the integration of AI-driven tools within these software platforms is significantly improving user experience by automating repetitive tasks and enabling smarter categorization, which is especially valuable for users managing large photo libraries.

Another significant driver is the growing adoption of cloud-based solutions, which offer seamless accessibility, scalability, and collaboration capabilities. Enterprises, professional photographers, and educational institutions are increasingly leveraging cloud-based photo organization software to manage, share, and protect their visual assets across multiple devices and locations. The shift towards remote work and digital collaboration has further accelerated the demand for cloud-enabled platforms, as they facilitate real-time access and sharing of media files while ensuring data security and backup. Additionally, subscription-based models are making advanced photo organization tools more accessible to a broader range of users, from individuals to large organizations, thereby expanding the marketÂ’s reach.

The rising importance of data privacy and security is also shaping the evolution of the photo organization software market. With growing concerns around unauthorized access, data breaches, and compliance with regulations such as GDPR, software providers are investing in robust encryption, user authentication, and access control features. This focus on security is particularly crucial for enterprise and educational users who handle sensitive or proprietary visual content. Furthermore, the ability to integrate with other digital asset management systems and third-party applications is becoming a key differentiator for vendors, as users increasingly seek holistic solutions that streamline workflows and enhance productivity.

As the photo organization software market continues to evolve, the role of Computational Photography is becoming increasingly significant. This innovative field combines computer science and photography to enhance and extend the capabilities of digital cameras. By leveraging algorithms and computational techniques, computational photography enables the creation of images that surpass the limitations of traditional photography. This is particularly relevant in the context of photo organization software, where advanced image processing can improve the accuracy of sorting and categorization. The integration of computational photography techniques can lead to more efficient management of photo libraries, offering users enhanced tools for editing, organizing, and sharing their visual content.

From a regional perspective, North America currently dominates the photo organization software market, accounting for the largest revenue share in 2024. This leadership is attributed to the high penetration of digital devices, advanced IT infrastructure, and the presence of leading software vendors in the region. Europe follows closely, driven by strong demand from professional photographers, creative agencies, and enterprises. Meanwhile, the Asia Pacific region is expected to exhibit the fastest growth during

Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2006 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed.

LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA).

Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset.

Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2006 to present. Frequency: Data is updated daily.

This paper studies the impact of incentives on worker self-selection in a controlled laboratory experiment. Subjects face the choice between a fixed and a variable payment scheme. Depending on the treatment, the variable payment is a piece rate, a tournament, or a revenue-sharing scheme. We find that output is higher in the variable-payment schemes compared to the fixed-payment scheme. This difference is largely driven by productivity sorting. In addition, different incentive schemes systematically attract individuals with different attitudes, such as willingness to take risks and relative self-assessment as well as gender, which underlines the importance of multidimensional sorting. (JEL C91, D81, D82, J16, J31)

The Parcel High Speed Automatic Sorting Machine market is witnessing significant transformation, fueled by the exponential growth of e-commerce and the demand for efficient logistics solutions. These state-of-the-art machines are designed to optimize the parcel sorting process in various sectors, including retail, l

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights:

A better understanding of how casual riders and annual riders differ Why would a casual rider become an annual one How digital media can affect the marketing tactics Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (03/2022 – 02/2023) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,785,180 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,785,180 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

Common garden trait dataMeasures of floral and plant traits from 539 plants grown in a common garden. Blank cells indicate plants for which only plant level traits (height and date of first flower) were measured--floral traits were not measured on these plants.CG_All_Traits_Feb_2016.csvCo-occurrence dataData on species co-occurrence from the lower Kern River canyon and Oiler canyon. Any stop that contained multiple species is listed twice (e.g. if stop 1 contained C. xantiana and C. unguiculata, there are two separate entries in the file--stop 1: X and stop 1: U). Species were abbreviated as follows: C = Clarkia cylindrica, D = C. dudleyana, E = C. exilis, P = C. xantiana ssp. parviflora, S = C. speciosa, U = C. unguiculata, X = C. xantiana ssp. xantiana.Co_occurrence_data_long_canyons.csv

Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2006 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed.

LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA).

Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset.

Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2006 to present. Frequency: Data is updated daily.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

The Robotic Waste Sorting System market is experiencing significant transformation as organizations worldwide seek more efficient and sustainable solutions to manage waste. These automated systems utilize advanced robotics and artificial intelligence (AI) to enhance the efficiency of waste sorting processes, reducin

List of City of Chicago licensed Public Chauffeurs, who may operate a licensed Taxicab, Livery, or Horse-Drawn Carriage. For more information on the Public Chauffeur program, please see http://www.cityofchicago.org/city/en/depts/bacp/supp_info/public_chauffeurinformation.html. To find drivers by name, enter any part of the name in the filter boxes along the right side of the screen. You can search for multiple names by adding lines and remove names by unchecking the lines.