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13,276 results for “IT management”
Data Evolution of Blended Learning and its Prospects in Management Education
<p>Data Scopus - Article "Evolution of Blended Learning and its Prospects in Management Education"</p>
Database for Perceived Inclusivity and Trust in Protected Area Management Decisions among Stakeholders in Alaska
<p>This database is part of a state-wide survey in Alaska, USA. An online Qualtrics interface was used to administer the survey to a panel of Alaskan residents from June to August 2020.</p>
Dataset of crop yield and management in CS02 from Diverfarming project
<p>Auxiliary data related to crop management and data of different crop yields in mandarin monoculture and mandarin diversified treatments during three crop cycles in case study 02 from Diverfarming project</p>
GERONTE H2020 project - GERDAT004 - Dataset of self-management recommendations
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of self-management recommendations that can be used in the care for older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>Patient empowerment by supporting self-management is an important component of the Geronte care pathway. As patients will be monitoring themselves at home, to register side-effects of treatment, decompensation of comorbidities and signs of functional decline, they will also be faced with questions about how to deal with the issues that they are having. While one important component of the care pathway is early signalling of complications to allow for early intervention by health care professionals, there is also a lot that patients can do for themselves at home to enhance their life-style, decrease burden of signs and symptoms or to improve outcomes.</p> <p>This led to the composition of a dataset to be included in the GERONTE care pathway, which is presented here.</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Rapid Landslide Risk Zoning toward Multi-Slope Units of the Neikuihui Tribe for Preliminary Disaster Management repository
<p> Taiwan features steep terrain and a fragile geology environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to the Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan evolves landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of Risk, which comprises Hazard, Exposure, and Vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.</p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries
<p>This document provides extended, supplementary data and information to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- The raw dataset of survey entries, anonymized (IP addresses and personal comments deleted)</p>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
Research Data Management Lifecycle
<p>The Research Data Management (RDM) lifecycle describes the various phases of a research project from a data management perspective. The Cycle diagram illustrates all the steps with multiple levels of granularity and details. The text free images can be used for other purposes where a cycle diagram is needed.</p>
H2020 773782-COASTAL MAL03 Management set for the Norrström-Baltic region
<p>The sets of measures relate to different Business Road Map (BRM) alternatives prioritized by stakeholders in the Norrström-Baltic (MAL3) case:</p> <ul> <li><strong>Current management</strong>: Base case with no change in nutrient concentrations for nitrogen (N) and phosphorous (P) (same results as for the base case scenario in D19).</li> <li><strong>Agricultural set of measures</strong>: Considers the example of 25% reduction in nutrient concentrations leaching from currently active agriculture with associated reduction in agricultural nutrient contributions to subsurface water (SSW) and surface water (SW) nutrient concentrations. Such reductions may result, e.g., from improved/optimized agricultural fertilization practices and drainage facilities, and restoration/construction of wetlands that can capture local nutrient leakage. This set of measures relates to the stakeholder-prioritized BRM alternative “Integrated risk assessment of nutrient losses from agricultural soils to surface waters”.</li> <li><strong>WWTP set of measures</strong>: Considers the example of 25% reduction of nutrient concentrations in discharges from currently active municipal and industrial wastewater treatment plants (WWTPs) and unconnected wastewater facilities with associated reduction in their contributions to SW nutrient concentrations. Such reductions may result, e.g., from improved nutrient removal in WWTPs and recovery in smart water and sanitation systems, related to technological advancements and, more widely, e.g., creation of a nutrient market that makes such capture and reuse worthwhile. This set of measures relates to the stakeholder-prioritized BRM alternatives “Nutrient recovery in wastewater treatment plants” and “Smart water and sanitation systems”.</li> <li><strong>Legacy set of measures</strong>: Considers the example of 25% reduction in total SSW and SW nutrient concentrations. Such reductions may result from catchment-wide mitigation (removal/capture and possible reuse) of nutrients released from diffuse wide-spread legacy sources that still remain in soil, groundwater and sediments from different types of earlier nutrient inputs (past agricultural leakage, municipal and industrial wastewater discharges). Such mitigation may be achieved, e.g., by restoration/construction of wetlands and construction of reactive barriers that are well-placed and distributed to effectively capture considerable parts of the overall legacy nutrient releases throughout each hydrological catchment. This set of measures also relates to possible changes in socio-economic drivers, such as creation of a nutrient market that can make capture and reuse of nutrients worthwhile, along with improved knowledge transfer between sectors and some shift in the municipal water (quality) management monopoly that current applies in Sweden. With regard to the stakeholder-prioritized BRM alternatives, it relates to: (i) “Improved knowledge transfer between sectors” that may drive better system understanding with more efficient nutrient mitigation measures taken and well/optimally placed in each hydrological catchment for targeting and mitigating diffuse legacy sources; and (ii) “Change of municipal monopoly” that may enhance collaboration and communication between different municipalities within the same hydrological catchment toward more overarching efficiency and circular principles on whole catchment-scale.</li> </ul> <p>The .xslx data are organized as follows.</p> <p>One sheet per set of measure: </p> <ul> <li>Base case (current management)</li> <li>Agricultural set of measures</li> <li>Wastewater treatment plants set of measures</li> <li>Legacy set of measures</li> </ul> <p>For each sheet, the following data are included. </p> <p>Columns:</p> <ul> <li><strong>A: Variable name</strong></li> <li><strong>B: Input variables</strong> - full name and units of the variable</li> <li><strong>C: Initial</strong></li> <li><strong>D: current climate and socio-economy</strong></li> <li><strong>E: scenarios</strong>: RCP4.5+SSP1, RCP4.5+SSP2, RCP4.5+SSP4, RCP4.5+SSP5</li> </ul> <p>For each column, the values are organized with the following variables (rows 2-16): “Concentration of N in SSW”, “Concentration of N in SW”, "Concentration of N in WWTP-input", "Concentration of N in WWTP-output", “Concentration of P in SSW”, “Concentration of P in SW”, "Concentration of P in WWTP-input", "Concentration of P in WWTP-output", "Concentration of P-SW to agriculture", "Concentration of N-SW to agriculture", "Concentration of P-agriculture to SW", "Concentration of N-agriculture to SW", "Concentration of N-agriculture to SSW", "Concentration of P-agriculture to SSW"</p> <ul> </ul> <p> </p>
Custom Dataset Collected for Energy Manager
<ul> <li>Change directory to inside the dataset</li> <li>Create virtual python environment and install the contents in requirements.txt</li> <li>Run the commands in sample_commands.txt file to get an idea on how the sample_pvm_to_csv.py script works</li> </ul> <p><strong>Data description:</strong><br> Light sensor (APDS9960) - Solar irradiance; Power sensor (INA226) - solar panel open circuit voltage (OCV), solar panel closed circuit current (CCC), solar panel closed circuit shunt voltage (CCSV); DHT22 - temperature and humidity data are collected using a custom setup (check the custom_setup.pdf file), placed indoors, next to a curtain less glass window (the plane of the sensing surfaces were almost 45 degree to the window glass. The spectral distortions made by the glass on the solar irradiance observed is unknown - sorry! The sensors used are INA226, Solar panel (1.5W, 137x81x2.5 mm, 16% efficiency, V & I at peak power :5.5V & 270ma), APDS9960 and DHT22.</p> <p>The custom setup transmits collected data to the server. The server creates a new log file everyday with '.txt' file extension. When the server restarts and begins logging, the first file created has the time information of when the log file was created, suffixed with number '0' before the file extension. This suffix is incremented day by day, until the next interruption to the server, where it create a new log file with the current time and suffix '0', and the whole cycle continues. The log files provided here are raw and unfiltered.<br> <br> A header with 'UTC' time was transmitted by the custom setup to the server whenever it restarts. It contains column information. The sampling period is 0.2s and only the time stamp for the first data is found in the header. The timestamp for the rest is calculated using the index / serial number and the sample period. The index and the data from each sensor are separated by '#'. If a sensor provides multiple data, then those data are separated by ','.</p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
GERDAT011 Literature search for publication - Geriatric assessment in the management of older patients with cancer – a systematic review (update).xlsx
<p>Search data belonging to the publication Geriatric assessment in the management of older patients with cancer – a systematic review (update)</p>
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Dataset of the paper "Vermicomposting as a sustainable option for the management of the biomass of the invasive tree Acacia dealbata Link."
<p>Data generated during an experiment of vermicomposting of <em>Acacia dealbata</em> fresh biomass. Four files are included: "<strong>vermicompost_and_earthworm_data.csv</strong>" and "<strong>readme.csv</strong>" are the raw data of different parameters measured in vermicompost samples during the vermicomposting of <em>Acacia dealbata</em> by the earthworm <em>Eisenia andrei</em> and an explanation of each parameter and the unit in which the parameter is expressed. </p> <p>"<strong>germination test.csv</strong>" and "<strong>radicle_length.csv</strong>" are the results of an ecotoxicological test on the effect of <em>A. dealbata</em> biomass and vermicompost on the germination and radicle elongation in <em>Lepidium sativum</em>.</p>
Research data management in the German-speaking Sports Sciences - Survey on the Status Quo
<p>The data set contains survey data on the status quo of research data management within the German-speaking sports science community. The survey was conducted as an online survey in the period from August 16<sup>th</sup> to September 30<sup>th</sup>, 2023.</p>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
Raw (main) dataset for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Raw dataset for farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</p>
Mapping practices of online community management
<p>Results of an online survey conducted during the period 13-31st March 2018. Responses were collected through a Google form; instructions and context were made available on a <a href="http://www.cottica.net/2018/03/13/mapping-online-community-management-practices-can-i-have-a-little-help-with-my-thesis/">web page</a>, the link of which was disseminated through Facebook, Twitter and on the e-mint mailing list (dedicated to professional online community managers on Yahoo. </p> <p>Each row of the file represents one questionnaire; each column represents one question.</p> <ul> <li>The first 9 questions are all of the format "To manage your online community, which of these courses of actions do you take, and how often?". The answers were given on a Likert-4 scale.</li> <li>The 10th question was "Do you want to add any other activity that uses up significant chunks of your community management time?". The answers were given in free form text.</li> <li>The 11th question was "How old is the community you manage? If you manage more than one, refer to the oldest." The answers were given as multiple choice, with three possible choices.</li> <li>The 12th question was "How large is the community you manage? If you manage more than one, refer to the largest.". The answers were given as multiple choice, with five possible choices.</li> </ul> <p>This work is part of my PhD Thesis.</p>
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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.