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50 results for “Process management”
IPBES Data Management Tutorials - Session 5.4: Processing and analysis
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on <em>processing and analysis </em>reviews common scripting languages for data analysis and processing, such as python and R. </p>
Data Management Plan (DMP) Process Example
<p>This diagram is an example of a funding program solicitation mapped to the select key components of a data management plan, major data lifecycle process, infrastructure and resources, and proposed elements for sustainability of a funded research project. This diagram was developed out of a need to illustrate introductory DMP processes workflows for education, teaching, and training purposes. The DCC Checklist for a Data Management Plan (2013) and the USGS Data Lifecycle Model (2013) were adapted in this diagram.</p>
Survey: Qualitative FGI research on the processing, sourcing, utilisation and management of wood biomass (NCN) DEC-2020/39/I/HS4/03533
<p>The dataset contains the proceedings of a qualitative FGI of representatives of wood biomass processing and harvesting companies. The research was conducted from 5 July 2022 to 7 July 2022 using only the FGI method. The survey was conducted in face-to-face meetings among respondents.<br>The study was funded by National Science Centre in Poland under agreement National Center of Science (NCN) through grant DEC-2020/39/I/HS4/03533</p>
Dataset - Analytic Network Process in economics, finance and management
<p><em>This data set presents the recent use of the Analytic Network Process (ANP) in the decision process in the areas of economics, finance and management. It has 434 ANP studies for a 10-year period (2012-2021) within the Scopus database. They were identified using the keyword “Analytic Network Process” in articles indexed in the following two database categories: "Business, Management and Accounting"; and "Economics, Econometrics and Finance". </em></p> <p> </p>
Model Collection of the Business Process Management Academic Initiative
<p>This model collection contains 29,810 models. The collection originates from the BPM Academic Initiative (BPMAI). In 2012, the initial members of this initiative were the following institutions represented by the corresponding professors: Mathias Weske (HPI, University of Potsdam), Marlon Dumas (University of Tartu), Marcello La Rosa (University of Melbourne), Jan Mendling (WU Vienna), Hajo A. Reijers (Utrecht University), Michael Rosemann (Queensland University of Technology, Australia), Jan Recker (University of Cologne, Germany), Wil van der Aalst (RWTH Aachen, Germany), Michael zur Mühlen (Stevens Institute of Technology, Hoboken, NJ) and Frank Leymann (University of Stuttgart), respectively, complemented by Dr. Gero Decker (Signavio GmbH, Berlin). The vendor Signavio provides a <a href="http://www.signavio.com/en/academic.html">free workspace</a> to the members of the BPM Academic Initiative and the models being created are made available for reseach purposes on the Creative Commons licence.</p> <p>The BPM Academic Initiative collection comprises tens of thousands of models, of various process modeling languages, and size. Models of the collection are available in several revisions, which opens a new perspective in researching the way people model.<br> The BPMAI collection is shared in a JSON format. Tools have been developed at HPI, University of Potsdam for efficiently processing these files. See the <a href="https://github.com/tobiashoppe/promnicat">PromniCat project</a>. Note that this project is no longer further developed, so you have to work with the information available there. Various analysis techniques can be applied for these models using the <a href="https://code.google.com/archive/p/jbpt/">jBPT library</a> or <a href="https://code.google.com/archive/p/apromore/">Apromore</a>.</p>
Hydraulic Processes based on Managed Realignment
<p>As a part of the primary requirement for the Data Science task, it has been written a document under name of Data Management Plan. For this purpose we used a tool provided by TU Wien - DMP Tool. The DMP has been build in a way to comply with Science Europe guidlines and hence following FAIR principles.</p> <p>This is s Verison 1 of DMP and the updates will be performed through the research if that's required. </p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Process of systematic mapping
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 1. Examples for Today Well Manageable and not yet Well Manageable Processes in Automation
<p>Despite the many progresses that have been achieved in automation and AI over the last decades, applications have mainly been successful for circumscribed, well-defined tasks in environments that are relatively specific, well-structured, and characterized by a limited number of possible occurring processes, states, and ways how to react to them [59]. There, technical solutions can even exceed certain human capabilities. The situation changes however if we switch to systems that should perform a broader range of tasks in less well-structured environments. Here, the limits of technical feasibility are being stretched to the utmost [7]. This fact is probably best illustrated by<br> the following two concrete examples in Figure 1.</p>
Figure 3. Traditional Approach and Process Management System Approach – Effort Percentage Comparison-Business Process Management – A Traditional Approach versus a Knowledge Based Approach
<p>Comparing the results obtained in using the two approaches (Figure 3), it is possible to note<br> a significant reduction in terms of both effort and working hours in correspondence of design and<br> development phases.</p>
WONDERBREAD: A Benchmark + Dataset for Business Process Management (BPM) Tasks
<p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2406.13264">WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks</a></p> <h2><strong>Background</strong></h2> <p>The <em>WONDERBREAD</em> dataset contains <strong>2,928 human demonstrations</strong> of <strong>598 web navigation workflows</strong> across <strong>6 types of BPM tasks</strong>. These tasks measure the ability of a model to generate accurate documentation, assist in knowledge transfer, and improve the efficiency of workflows.</p> <p>Please see our website for more details: <a href="https://wonderbread.stanford.edu/">https://wonderbread.stanford.edu/</a></p> <h2><strong>Quick Start</strong></h2> <p>To start, download <strong>debug_demos.zip</strong> (1 GB). It contains a subset of <strong>24 demonstrations</strong> which can give you a sense of how the dataset is structured.</p> <p>To reproduce the paper, download <strong>gold_demos.zip</strong> (33 GB). It contains <strong>724 demonstrations</strong> corresponding to the 162 "Gold" tasks which were used for all the evaluations in the original paper.</p> <p>To obtain the full dataset, download <strong>demos.zip</strong> (133 GB). This contains all <strong>2,928 demonstrations</strong> and can be used for training, fine-tuning, and evaluating models.</p> <h2><strong>Dataset Structure</strong></h2> <p>The dataset contains several files, defined below.</p> <ol> <li><strong>Raw Data</strong><em> (useful for training/fine-tuning/evaluation)</em> <ol> <li><strong>debug_demos.zip </strong>(1 GB)<strong> -- </strong>a subset of only 24 demonstrations taken from the full dataset. Useful to get a sense of the dataset and for debugging.</li> <li><strong>gold_demos.zip</strong> (22 GB) -- a subset of only 724 demonstrations corresopnding to the 162 "Gold" tasks. This is the dataset that was used for all evaluations in the original <em>WONDERBREAD</em> paper.</li> <li><strong>demos.zip</strong> (133 GB) -- all 2,928 demonstrations across 598 tasks. Useful for training your own models.</li> </ol> </li> <li><strong>Modality-Specific Subsets of Raw Data </strong><em>(useful for specific types of training/fine-tuning/evaluation)</em><br> <ol> <li><strong>All Demos</strong> <ol> <li><strong>demos_sop_only.zip</strong> (4 MB)-- only the SOP <code>.txt</code> files for all 2,928 demonstrations</li> <li><strong>demos_sop_and_trace_only.zip</strong> (770 MB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for all 2,928 demonstrations</li> <li><strong>demos_sop_and_trace_and_screenshots_only.zip</strong> (22 GB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for all 2,928 demonstrations</li> </ol> </li> <li><strong>"Gold" Demos</strong> <ol> <li><strong>gold_demos_sop_only.zip</strong> (1 MB)-- only the SOP <code>.txt</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li><strong>gold_demos_sop_and_trace_only.zip</strong> (190 MB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li><strong>gold_demos_sop_and_trace_and_screenshots_only.zip </strong>(6 GB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for the 724 demonstrations in the "Gold" tasks</li> </ol> </li> </ol> </li> <li><strong>Evaluation</strong><em> (useful for evaluation)</em> <ol> <li><strong>qa_dataset.csv -- </strong>contains all 120 questions and ground truth answers used in the "Knowlege Transfer" evaluation.<strong><br></strong></li> <li><strong>df_rankings.csv -- </strong>contains the rankings of all "Gold" tasks used in the "SOP Ranking" evaluation.<strong><br></strong></li> </ol> </li> <li><strong>Metadata</strong><em> (can be safely ignored)</em> <ol> <li><strong>Process Mining Task Demonstrations.xlsx --</strong> maps human annotators to specific demonstrations; also contains "Gold" task rankings used in the "SOP Ranking" evaluation.</li> <li><strong>metadata.json -- </strong>maps Google Drive URLs to Google Drive Folder IDs to demonstration names</li> <li><strong>df_valid.csv -- </strong>tracks assets associated with each demonstration</li> </ol> </li> </ol>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Process Performance Indicators for IT Service Management: The PPI Dataset
<p>Set of process performance indicators defined in natural language, related to the IT service management processes of several public organizations in Spain as published in <em>Estrada-Torres, B., del-Río-Ortega, A., Resinas, M., & Ruiz-Cortés, A. (2021). Process Performance Indicators for IT Service Management: The PPI Dataset. In Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts (pp. 125-132). CRC Press. DOI: <a href="https://doi.org/10.1201/9781003168393-8">https://doi.org/10.1201/9781003168393-8</a></em></p>
A Scalable Data Management System Data Model facilitating the integration of any inspection system and the automated data digitalization process.
<p>In the context of the EU-funded project PILOTING (No. 871542), a scalable Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. In particular, a three-phase methodology was adopted for the creation of the DMS Data Model (DMS-DM) in order to define the architectural design of the DMS. <br> The first phase was to semantically define the entities of the PILOTING ecosystem, identifying assets and activities that were important to the data providers, and including them in the data model design. The second phase was to identify relationships between the entities, focusing on information that must pre-exist for entities to be semantically accurate, hierarchies between entities, especially physical assets, and the data needed to form the inspection plan. For the third phase, the information collected by the previous two stages of the data model construction was used to create the overall data model, taking into consideration the possible integration with third parties and the needs of the I&M Visualization portal. <br> The constructed document is attempted to present the designed Entity-Relationship-Diagram (ERD) of the DMS-DM. Additional definitions of the existing entities and the relations between them are also depicted. </p>
BIG DATA ANALYTICS IN DIGITAL HUMAN RESOURCES MANAGEMENT: IMPACT ON THE RECRUITMENT PROCESS
<p>This study investigates how HR employees experience the big data phenomenon in the recruitment function of HRM and how their perceptions of the phenomenon have evolved. This study also examines how BD will affect organizational and HRM and how it can be improved in other functions of HR. In this exploratory study, which comprehensively addresses the BD phenomenon in HRM, the phenomenological design approach, one of the qualitative research methods, was applied to test the research questions and a semi-structured interview form was used for research data. Using the snowball sampling method, in-depth interviews were conducted with 10 HR employees working in large and semi-structured organizations in Turkey and the interviews were analyzed with MAXQDA 20. The findings show that HRM employees are aware of BD. On the other hand, it is understood that BD technologies provide easy accessibility in recruitment, offer a strategic competitive advantage, and enable more effective management of information management, which saves HR employees' work in a facilitating way. They benefit from technology as a decision support assistant. Finally, the research results provide theoretical and practical implications for future researchers and practitioners for the development and effective use of BD technology in the field of HRM.</p>
Patient-Partner Stress Management Effects on Chronic Fatigue Syndrome Symptoms and Neuroimmune Process
ClinicalTrials.gov study NCT01650636. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Disentangling the impact of forest management intensity components on soil biological processes
Open the record for dataset details and reuse information.
The Hidden Midden – Geoarchaeological investigation of sedimentation processes, waste disposal practices and resource management at the La Tène settlement of Basel-Gasfabrik (Switzerland) - Dataset
<p>Data set to the paper " The Hidden Midden – Geoarchaeological investigation of sedimentation processes, waste disposal practices and resource management at the La Tène settlement of Basel-Gasfabrik (Switzerland)" (Geoarchaeology; An International Journal).</p> <p>Supplementary Information 1: Profile photos, polished sections and thin sections scans</p> <p>Supplementary Information 2: Data set of the micromorpological analysis. Most of the data are given in a semiquantitative way: absent [0%] – single [<2%] – few [2–10%] – frequent [10–25%] – numerous [25–50%] – dominant [>50%]</p> <p> </p>
Comprehensive Risk and Emergency Management Conceptual Design and Map (Response Process Big Picture)
<p>Comprehensive Risk and Emergency Management Conceptual Design and Map helps to have an insight about Risk and Emergency Planning and Management Process to better act.</p> <p>Response Process Big Picture</p>
Search Protocol for "Conversational Systems for AI-Augmented Business Process Management"
<p>Results obtained from implementing the search protocol devised for the literature survey "<em>Conversational Systems for AI-Augmented Business Process Management</em>".</p> <p>The dataset comprises four spreadsheets, each corresponding to one of the four BPM areas identified in the paper, namely:</p> <ul> <li><em>Descriptive Process Analytics</em>;</li> <li><em>Predictive Process Analytics</em>;</li> <li><em>Prescriptive Process Optimization</em>;</li> <li><em>Augmented Process Execution</em>.</li> </ul> <p>Each spreadsheet consists of multiple sheets:</p> <ul> <li>The first four sheets document the papers collected from each data source (<em>Google Scholar</em>, <em>Scopus</em>, <em>ACM Digital Library</em>, <em>IEEE Xplore</em>) by applying the search strings defined in the paper.</li> <li>"All" reports all the papers obtained in the search.</li> <li>"All(-duplicates)" lists all publications, excluding duplicates.</li> <li>"Inclusion" applies the inclusion criteria defined in the paper to select the works considered in this survey.</li> <li>"Final" comprises the selected papers, representing the outcomes of the search protocol's application.</li> <li>"Results" provides statistical insights into the application of the search protocol for the specific BPM area under analysis."</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.