Set Of Tools Research Articles

An analysis of green lean six sigma deployment in MSMEs: a systematic literature review and conceptual implementation framework

PurposeGreen Lean Six Sigma (GLSS) is a comprehensive approach that aims to reduce waste, emissions and non-value added activities, thus mark an effective impact on sustainability of firms. Despite the numerous benefits of GLSS implementation, it is evident that Micro Small and Medium Enterprises (MSMEs) are still struggling to understand, integrate and implement this strategy. This research work provides a comprehensive analysis of GLSS within MSMEs and proposes a conceptual integration framework of GLSS for improving economic and environmental dimensions of sustainability MSMEs.Design/methodology/approachA systematic literature review (SLR) methodology was planned to assess and analyse the research articles from 2007 to 2022. Different key elements of GLSS such as barriers, enablers and tool sets have been thoroughly reviewed and analysed for MSME organisations to understand their behaviour and effectively adopt the GLSS approach in their operations.FindingsThis study provides the analysis of different perspectives of GLSS and this will contribute to improve different metrics related to emissions and quality in MSMEs. It provides MSMEs industrial managers with a comprehensive knowledge base of GLSS elements, enabling effective deployment.Practical implicationsThe present study provides a significant knowledge base and know-how of GLSS to researchers that will assist in deploying this sustainable approach in different industrial domains. The study also assists industrial managers by providing a systematic framework of GLSS for MSMEs. Further, the study also supports society by proving pathway to improve the environmental dynamics through the execution GLSS.Originality/valueThe study is of the first kind that review different facets of GLSS related to MSME. The study not only enhances theoretical know how of GLSS but also guides practitioners how to implement comprehensive GLSS program for improved environmental sustainability.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box.

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Socio‐technical issues in the platform‐mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper

AbstractThe gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions.

Defining priority areas for conservation based on multispecies functional connectivity

Habitat fragmentation poses a significant challenge to biodiversity conservation. Well-designed ecological corridors can effectively enhance connectivity between habitat areas, particularly within Protected Areas (PAs), thereby mitigating biodiversity loss. Herein, we proposed a methodological approach for identifying Priority Areas for Conservation (PACs) of biodiversity based on functional connectivity for different species of birds and terrestrial mammals. We applied the proposed methodology in a biodiverse region in Brazil that harbors a mosaic of PAs and remaining areas of natural vegetation under anthropogenic pressure, primarily from mineral extraction activities. We used ecological corridor simulations as input data and a set of geographic information systems tools to delineate the priority areas. In total, we identified 24 priority areas for the conservation of 10 bird and 5 mammal species under different extinction threat levels and preferential habitat types, which varied among seasonal semideciduous forests, savannas, and rupestrian grasslands. The sizes of the priority areas identified ranged from 834.42 to 7954.36 ha. The proposed methodology can be applied to define priority areas for biodiversity conservation at a regional scale for different species or groups of species worldwide.

The Contribution of Typological Studies to the Integrated Rehabilitation of Traditional Buildings: Heritage Enhancement of Urban Centres in Inner Alentejo, Portugal

The urban centres of inland Alentejo (southern Portugal) have long faced a depopulation crisis which, besides undermining the cohesion of the communities, compromises the conservation of the architectural heritage. The tendency to apply the discourses on tourism and population pressures from coastal cities to these inland territories can be detrimental to their analysis given the sheer difference in demographic dynamics. Transformations in traditional architecture—a key facet for defining these historic urban landscapes—require analysing within this social context. The imperative need to rehabilitate traditional buildings, endowing them with the living conditions communities today require, must be guided by morphological analysis, knowledge of housing history, typologies, and traditional construction techniques. This rehabilitation concept integrates into the transformation processes that traditional architecture has been undergoing for centuries, constituting adaptable and flexible structures across their organisational variants, which should be studied through a prospective approach. The article characterises the transformation of urban domestic architecture in the region, from the early modern period to the present. The results of various research projects are summarised, gathering over 500 cases. The conclusion argues that the historical process itself results in a set of themes, tools, and opportunities for these buildings’ adaptation to current needs.

Development of Image Processing and AI Model for Drone Based Environmental Monitoring System

Data from environmental monitoring can be used to identify possible risks or adjustments to ecological patterns. Early detection reduces risks and lessens the effects on the environment and public health by allowing for prompt responses to ecological imbalances, pollution incidents, and natural disasters. Decision-making and analysis can be done in real time when Artificial Intelligence (AI) is integrated with Unmanned Aerial Vehicles (UAV) technology. With the help of these technologies, environmental monitoring is made possible with a more complete and effective set of tools for assessment, analysis, and reaction to changing environmental conditions. Multiple studies have shown that forest fires in India have been happening more often recently. Lightning, extremely hot weather, and dry conditions are the three main elements that might spontaneously ignite a forest fire. Both natural and man-made ecosystems are affected by forest fires. Forest fire photos are pre-processed using the Sobel and Canny filter. A Convolutional Neural Network (CNN)–based Forest Fire Image Classification Network (DFNet) using the publicly accessible Kaggle dataset is proposed in this study. The suggested DFNet classifier's hyperparameters are fine-tuned with the help of Spotted Hyena Optimizer (SHO). With a performance level of 99.4 percent, the suggested DFNet model outperformed the state-of-the-art models, providing substantial backing for environmental monitoring.

Diagnosing homo digitalis: towards a standardized assessment for digital tool competencies.

In the 21st century, digital devices have become integral to our daily lives. Still, practical assessments designed to evaluate an individual's digital tool competencies are absent. The present study introduces the "Digital Tools Test" ("DIGI"), specifically designed for the evaluation of one's proficiency in handling common applications and functions of smartphones and tablets. The DIGI assessment has been primarily tailored for prospective use among older adults and neurological patients with the latter frequently suffering from so-called apraxia, which potentially also affects the handling of digital tools. Similar to traditional tool use tests that assess tool-selection and tool-action processes, the DIGI assessment evaluates an individual's ability to select an appropriate application for a given task (e.g., creating a new contact), their capacity to navigate within the chosen application and their competence in executing precise and accurate movements, such as swiping. We tested the implementation of the DIGI in a group of 16 healthy adults aged 18 to 28 years and 16 healthy adults aged 60 to 74 years. All participants were able to withstand the assessment and reported good acceptance. The results revealed a significant performance disparity, with older adults displaying notably lower proficiency in the DIGI. The DIGI performance of older adults exhibited a correlation with their ability to employ a set of novel mechanical tools, but not with their ability to handle a set of familiar common tools. There was no such correlation for the younger group. In conclusion, this study introduces an innovative assessment tool aimed at evaluating common digital tool competencies. Our preliminary results demonstrate good acceptance and reveal expected group differences. For current cohorts of older adults, the results seem to indicate that the ability to use novel tools may aid digital tool use. In the next step, the psychometric properties of the DIGI assessment should be evaluated in larger and more diverse samples. The advancement of digital tool competency assessments and rehabilitation strategies is essential when we aim at facilitating societal inclusion and participation for individuals in affected populations.

Assessment of subvisible particles in biopharmaceuticals with image feature extraction and machine learning

An image classification tool was developed to classify subvisible particles, namely silicone oil (SO) and non-silicone oil (NSO; protein aggregate, rubber closure, and air bubble) particles, present in biopharmaceuticals using feature extraction in FlowCam® images, and the outcomes were validated with six machine learning (ML) classifiers. The image classification tool set at specific configuration: condition 1 − CDEP {(compactness, diameter, elongation, perimeter) = (8.06, 10.44, 13.30, 27.20)} identified SO, while the configuration set at condition 2 – CDEL {(compactness, diameter, elongation, length) = (3.14, 24.48, 1.97, 4.28)} detected NSO. The classification tool was particularly useful in detecting the release of SO after exposure to the stress sources. Additionally, the morphological features-based classification tool (p < 0.05) enhanced the predictive accuracy of the ML classification tools (≥97.2 %). Specifically, CNN (100 %) outperformed naïve Bayes (99.3 %), linear discriminant analysis (98.4 %), artificial neural network (98.1 %), support vector machine (SVM 97.2 %). Bootstrap forest was excluded because it failed to classify SO in a large dataset. The developed classification tool could be an alternative in classifying the image datasets without the burden of complex ML tools. Such image-based classification tool can be computationally economical solution in quality control of the protein formulations.

Գրադարանի և գրադարանավարի դերի փոխակերպումը տեխնոլոգիական առաջընթացի համատեքստում

The relevance of this article is due to the fact that the information explosion, the digital age pre-sents us with a completely different set of tools, provided services and mechanisms, and the development of activities with a new logic. The article refers to the significance and role of the library sector in the life of 21st century societies. In the era of information explosion, one of the greatest challenges of the library sector is the influence of artificial intelligence, which reduces the importance of the human factor. Thus, the circumstance of the librarian carrying out a function in the library is reduced incomparably, despite the fact that the reader gets any information he needs, saving space and time. This conjuncture gives rise to deep thinking among representatives of the library sector. In this case, each library should study the international experience, prospectively repeating what is useful, which will only improve the latter's work. This should be done taking into account a number of features of what kind of library we are dealing with, national, academic, scientific, children's, etc. However, with good library management, the right strategy and the necessary resources, it is possible to turn challenges into favorable conditions, taking advantage of new and limitless opportunities.

Application of Lean Six Sigma to Reduce Delays in Engineering Changes

Quality methodologies have provided improvements for the organizations, increasing their competitive nature. Therefore, the present article was developed toward the reduction in the incidence of delays while developing engineering changes in an agricultural machine factory through the application of Lean Six Sigma concepts. An engineering change consists of a multitasking process for a product change. The article stages were developed following the sequence proposed by the DMAIC methodology. Several tools associated with Lean Six Sigma, such as process mapping, Ishikawa diagram, brainstorming, and Kanban were used in this article. Also, statistical tests were carried out to check the consistency of the goals established by the organization regarding the deadline for carrying out the analyzed tasks and for proposing new goals. The development of this article allowed the identification and elimination of root causality which culminated in delays during the implementation of engineering changes, and in turn, reduced the incidence of delays by 95&#x0025;. The article shows the importance of companies applying scientific methodologies and statistical analysis in their processes to improve their results. Finally, it contributes to the scientific community, exposing an adaptation of tools and showing that the implementation of a set of management tools can contribute to process improvements.

Target-activated multicolor fluorescent dyes for 3D imaging of plasma membranes and tracking of apoptosis.

Real-time tracking of dynamic changes in the three-dimensional morphology of the cell plasma membrane is of great importance for a deeper understanding of physiological processes related to the cell plasma membrane. However, there is a lack of imaging dyes that can specifically be used for a long term labelling of plasma membranes, especially for plant cells. Here, we have used molecular engineering strategies to develop a series of target-activated multicolour fluorescent dyes that can be used for long-term and three-dimensional imaging of plant cell plasma membranes. By combining different electron acceptors and donors, four molecular backbones with different emission colours from green to NIR have been obtained. In the designed styrene-based dyes, referred to as the SD dyes, several functional groups were introduced into the backbones to achieve the properties of target-activated fluorescence, rapid and wash-free staining, high plasma membrane targeting ability and long-term imaging function. Using onion epidermal cells as a platform, these dye molecules can provide high-quality imaging of the plasma membrane for up to 6 hours, providing a powerful tool for long-term monitoring of plasma membrane-related biological events. Calcium-mediated apoptosis of plant cells has been tracked for the first time by monitoring the morphological changes of the plasma membrane in real time using SD dyes. These dyes also exhibit excellent 3D imaging performance of the plasma membrane and were further used to track in real time the 3D morphological changes of the plasma membrane during plasmolysis of plant cells, providing a powerful imaging tool for three-dimensional (3D) biology. This work provides a set of multi-colour dye tools for long-term and three-dimensional imaging of plant cell plasma membranes, and also provides molecular design principles for guiding the transmembrane transport of small molecules.

Using CloudCompare software editing tools for processing a three-dimensional point cloud of an urban development site

This article describes the functions that can be performed by CloudCompare, an open-source triangular mesh and 3D point cloud processing software developed through a collaboration between Telecom ParisTech and EDF R&amp;D. CloudCompare software is used to study and analyze archaeological sites and create their 3D models. Helps engineers monitor the condition of bridges, roads, buildings, etc., identify problems and find solutions to eliminate them. The program provides a set of basic tools for editing and rendering. Some basic editing tools for working in the CloudCompare software are studied and demonstrated, such as cloning, cutting an element from the main object of study, scaling. It is concluded that the CloudCompare software is easy to use and effectively performs the functions contained in it.

Differentiable Design Galleries: A Differentiable Approach to Explore the Design Space of Transfer Functions.

The transfer function is crucial for direct volume rendering (DVR) to create an informative visual representation of volumetric data. However, manually adjusting the transfer function to achieve the desired DVR result can be time-consuming and unintuitive. In this paper, we propose Differentiable Design Galleries, an image-based transfer function design approach to help users explore the design space of transfer functions by taking advantage of the recent advances in deep learning and differentiable rendering. Specifically, we leverage neural rendering to learn a latent design space, which is a continuous manifold representing various types of implicit transfer functions. We further provide a set of interactive tools to support intuitive query, navigation, and modification to obtain the target design, which is represented as a neural-rendered design exemplar. The explicit transfer function can be reconstructed from the target design with a differentiable direct volume renderer. Experimental results on real volumetric data demonstrate the effectiveness of our method.

ОРГАНІЗАЦІЙНО-ЕКОНОМІЧНІ ЗАСАДИ ФОРМУВАННЯ ТУРИСТИЧНОГО ПРОДУКТУ

The organizational and economic basis for the formation of a tourist product is a set of principles, methods, mechanisms, tools and resources that are used to create, supply, sell and consume tourist services and goods that meet the needs and demand of tourists. The organizational and economic foundations of the formation of a tourist product are based on the analysis and assessment of tourist value, potential, attractiveness, competitiveness, tourist quality, efficiency and tourist sustainability of a country, region, object or enterprise. Organizational and economic principles of forming a tourist product provide for the development and definition of the concept, goals, strategy, policy, standards, norms, rules and criteria of a tourist product; research and selection of markets, segments, target groups, niches, positioning and branding of a tourist product; design and development of the structure, composition, content, form, volume, characteristics, properties, functions, advantages, features and innovations of a tourist product; evaluation and determination of the value, price, costs, income, profit, profitability, risks, opportunities, prospects, barriers, restrictions and resources of a tourist product; production, purchase, supply, storage, transportation, distribution, sale, advertising, promotion, Service, Support, Warranty, after-sales service of a tourist product; control, monitoring, audit, evaluation, regulation, adjustment, improvement, modernization, innovation, development, liquidation of a tourist product. These aspects require separate study, taking into account the diverse experience of countries, their historical and economic conditions. A tourist product is a complex product that includes various elements that are necessary to meet the needs of tourists during travel. The tourist product as a complex system includes tangible and intangible elements. The formation of a tourist product is a complex and Multi-Factor process that depends on such factors as the needs of tourists, the competitive environment, legislative regulation, and so on. For the successful formation of a tourist product, it is necessary to adhere to such principles as customer orientation, competitiveness, stability and efficiency. Tour operators need to constantly analyze the market and adapt their product to changes in consumer needs. They should carefully consider the structure of the tourist product so that it meets the needs and desires of tourists.

Data-Driven Decision Support Systems for Business Process Improvement

The accessibility of data is altering how businesses make decisions at different levels. Scholars and professionals are investigating the ways in which Business Process suppliers can profit from the availability and application of data, particularly in relation to decision-making concerning service provision. Business Process Improvement is one of the applications that is anticipated to gain the most from the accessibility of information. Suppliers of services can avoid failures by making prompt and well-informed decisions based on the evaluation of the resource's health state. Despite this, providing data-driven BPI services is not simple, and providers must set up their systems to correctly gather, process, and utilize past and current data. This study introduces a data-driven business intelligence framework to provide use full insights for improving business process activities. This framework offers a set of visualization tools that help interpret the relation between different factors that can improve the management of different business processes. Moreover, our framework provides successful integration of random forests to allow predictive modeling of sales, profits, and discounts across different regions.

Investigating Period Variability Mechanisms in Eclipsing Binary Stars through Eclipsing Time Variation Analysis: A Case Study of TZ Bootis

We present an effective strategy for extensive analysis of eclipsing time variations (ETVs) using modern and sophisticated optimization methods that comprise a set of tools to investigate period variability mechanisms in eclipsing binary stars such as the light-time effect, the Applegate mechanism, and mass transfer. We implement these methods for the first time assuming that the above mechanisms can act simultaneously in the puzzling W UMa–type binary star TZ Bootis by using archival and new TESS data spanning 75 yr and reexamining the up-to-date ETVs. Preliminary analysis of the TESS data revealed for the first time the presence of a second binary in agreement with previous spectroscopic data and astrometric results from Gaia DR3. We consider the most credible scenario for the ETV: two stellar circumbinary companions of minimum masses M 3 = 0.5 M ☉ and M 4 = 0.14 M ☉ in highly eccentric orbits e 3 = 0.70 and e 4 = 0.82 with periods P 3 = 38 yr and P 4 = 20 yr along with a 24 yr magnetic activity of the secondary component and a long-term period increase (dP/dt = 1.2 × 10−8 days yr−1), interpreted as a conservative mass transfer from the secondary to the primary component at a rate of dM 1/dt = 3.7 × 10−9 days yr−1. Further spectroscopic observations, analytical modeling of the second pair, and ETV analysis of both pairs are needed to investigate the quadruple nature of the system.

Brain texture as a marker of transdiagnostic clinical profiles in patients with recent-onset psychosis and depression

The inter-relationships of voxels can be captured by the radiomics texture features across multiple spatial scales. Prediction models of brain texture changes captured by the contrast texture feature in recent-onset psychosis (ROP) and recent-onset depression (ROD) have recently been proposed, although the validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are lacking. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. Here we aimed to investigate the cross-sectional utility of brain texture changes for (1) identification of the psychopathological state (ROP and ROD) and (2) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural magnetic resonance imaging data for 116 patients with ROD, 122 patients with ROP and 197 healthy control participants from the PRONIA (Personalized pROgNostic tools for early psychosIs mAnagement) study by applying a set of tools and frameworks to explain the classification decisions of the deep-learning algorithm (named explainable artificial intelligence) and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was >72% and was validated in a second independent age- and sex-matched sample of 137 ROP, 94 ROD and 159 healthy control participants. Clustering analysis was implemented to map the changes in texture brain produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into eight homogeneous clusters. In the clinical group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, that is, age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels that contributed significantly to the classification decision predicted accurately the PANSS (positive and negative symptom scale) scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which predict the clinical severity and outcome profile in ROP and ROD patients.

ЗЕЛЕНА ТРАНСФОРМАЦІЯ ЕКОНОМІКИ НІМЕЧЧИНИ В СУЧАСНОМУ КОНТЕКСТІ ГЛОБАЛЬНИХ ВИКЛИКІВ

The green transformation represents one of the most ambitious socio-economic and ecological endeavors in the modern world. Germany is a key nation undertaking this economic green transformation. The article’s objective is to analyze the main trends of Germany’s green transformation and to develop a management toolkit for this project. It focuses on the "Energiewende" project, executed by the German government, which encompasses Germany’s comprehensive efforts to shift from fossil fuels to an economy based on renewable energy sources. The article examines socio-political and socio-economic decisions, as well as technological innovations underpinning this movement, including the development of wind and solar power, the integration of energy systems, the modernization of electric transport infrastructure, and the enhancement of Germany’s energy efficiency. It highlights the importance of government policy and legislative initiatives in supporting green transformation and the role of public influence on the project’s implementation. The challenges Germany faces in this green transformation process, such as the need for further investments, network load management, and maintaining energy security, are discussed. Particular emphasis is placed on analyzing the socio-economic impacts of the green transformation, including job creation in the renewable energy sector and effects on traditional energy industries. Recommendations are provided on how this strategy could be adapted in Ukraine, along with potential pathways for international cooperation in this field. To support Germany’s green transformation, an analytical platform for renewable energy management and energy efficiency is being offered. This comprehensive set of tools includes modules such as environmental impact monitoring, energy management, public engagement and education, regulatory compliance, and market trend analysis. The proposed research will be beneficial for the academic and analytical community, international organizations, government and local authorities, and business. Future research prospects include exploring opportunities for adapting Germany’s experience in stimulating state and business initiatives for sustainable development in Ukraine.

Evaluation of the efficiency of parallel algorithms using intel parallel studio

The paper analyzes the means of evaluating the effectiveness of parallel programs. The research focuses on im- proving the performance of basic linear algebra operations performed on parallel computing systems with shared memory. The Intel Parallel Studio environment is used, which provides a powerful set of tools and resources for analyzing and optimizing parallel programs. The results of the study make it possible to solve performance-related problems and improve the operation of parallel programs.

Humans in the loop: Community science and machine learning synergies for overcoming herbarium digitization bottlenecks.

Among the slowest steps in the digitization of natural history collections is converting imaged labels into digital text. We present here a working solution to overcome this long-recognized efficiency bottleneck that leverages synergies between community science efforts and machine learning approaches. We present two new semi-automated services. The first detects and classifies typewritten, handwritten, or mixed labels from herbarium sheets. The second uses a workflow tuned for specimen labels to label text using optical character recognition (OCR). The label finder and classifier was built via humans-in-the-loop processes that utilize the community science Notes from Nature platform to develop training and validation data sets to feed into a machine learning pipeline. Our results showcase a >93% success rate for finding and classifying main labels. The OCR pipeline optimizes pre-processing, multiple OCR engines, and post-processing steps, including an alignment approach borrowed from molecular systematics. This pipeline yields >4-fold reductions in errors compared to off-the-shelf open-source solutions. The OCR workflow also allows human validation using a custom Notes from Nature tool. Our work showcases a usable set of tools for herbarium digitization including a custom-built web application that is freely accessible. Further work to better integrate these services into existing toolkits can support broad community use.