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148 results for “Change Management”
Figure 1 from: Neylon C (2017) Compliance Culture or Culture Change? The role of funders in improving data management and sharing practice amongst researchers. Research Ideas and Outcomes 3: e21705. https://doi.org/10.3897/rio.3.e21705
Figure 1 - Illustration of the categories through which many research data management and sharing policies develop, with examples of the language used.
SCIENTIFIC AND THEORETICAL BASIS OF COMPETITIVENESS MANAGEMENT IN TEXTILE ENTERPRISES IN THE CONDITIONS OF INNOVATIVE CHANGES IN THE ECONOMY
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Impacts of anthropogenic management legacies on forest dynamics of the Tibetan Plateau transition region under changing climates
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SECO-RCR: A Tool to Manage Requirements Change in Software Ecosystems
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Supplementary material 1 from: Tájek P, Tenčík A, Konvička M, John V (2023) Vegetation changes at oligotrophic grasslands managed for a declining butterfly. Nature Conservation 52: 23-46. https://doi.org/10.3897/natureconservation.52.90452
Ordination scores from the indirect DCA analysis species
Effectiveness of Florbetapir (18F) PET Imaging in Changing Patient Management and the Relationship Between Scan Status and Cognitive Decline
ClinicalTrials.gov study NCT01703702. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Instilled Lidocaine vs Placebo for Pain Management During Vacuum Assisted Closure (VAC) Dressing Changes
ClinicalTrials.gov study NCT00585325. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Continuous Glucose Monitoring to Assess Glycemia in Chronic Kidney Disease - Changing Glucose Management
ClinicalTrials.gov study NCT02608177. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Contrast-Enhanced Digital Mammography (CEDM) for Identifying a Change in Management of Women With Newly Diagnosed Breast Cancer
ClinicalTrials.gov study NCT01303419. IPD Sharing: Not stated. Countries: 5. Publications: 0.
SomaSignal Tests on Medical Management and Change in Risk in Patients With Diabetes
ClinicalTrials.gov study NCT05256706. IPD Sharing: YES. Countries: 1. Publications: 0.
Mission is Remission®: How Can a Disease Self-management Website Change Care?
ClinicalTrials.gov study NCT02694042. IPD Sharing: NO. Countries: 0. Publications: 52.
Data from: Changes in ecosystem properties after postfire management strategies in wildfire affected areas
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Data from: Strategies for sustainable management of renewable resources during environmental change
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Using a custom mobile application for change management in an electronic health record implementation
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Data from: Management of behavioural change in patients presenting with a diagnosis of dementia: a video vignette study with Australian general practitioners
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Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"
<p># Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"<br> This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation.</p> <p>### REFERENCES<br> Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be<br> Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version<br> 1.86.0, <URL: https://github.com/pik-piam/madrat>.</p> <p>rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL:<br> https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>.</p> <p>Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov<br> R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E,<br> Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL:<br> https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>.</p> <p>Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>.</p> <p>Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder<br> F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL:<br> https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>.</p> <p>## LICENSE<br> This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode.</p> <p>## CONTACT<br> karstens@pik-potsdam.de</p>
Data for "Response of global forest management to changes in future wood demand"
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Data from: Subalpine vegetation changes in the Eastern Sudetes (1973–2021): Effects of abandonment, conservation management and avalanches
<p>This dataset contains the original data used in the article:</p> <p>Klinkovská K., Kučerová A., Pustková Š., Rohel J., Slachová K., Sobotka V., Szokala D., Danihelka J., Kočí M., Šmerdová E. & Chytrý M. (2023) Subalpine vegetation changes in the Eastern Sudetes (1973–2021): effects of abandonment, conservation management and avalanches. <em>Applied Vegetation Science</em>, 26, e12711. <a href="https://doi.org/10.1111/avsc.12711">https://doi.org/10.1111/avsc.12711</a></p> <p>The data contain plant species composition from vegetation plots repeatedly surveyed in the Hrubý Jeseník Mountains (Eastern Sudetes, Czech Republic). Vegetation plots surveyed by Leoš Bureš and Zuzana Burešová in 1973–1978 were resurveyed in 2004–2010 by Martin Kočí and Leo Bureš and resurveyed again in 2021 by the authors of this dataset. Several new plots were also surveyed in 2004–2010 and resurveyed in 2021. In the 1970s, plot locations were related to patches of a detailed vegetation map. In the 2000s, plot locations were measured using GPS with an uncertainty of less than 10 m. In 2021, plots were localized using differential GPS with ~5 cm accuracy (Topcon HiPer SR with Getac PS336 Data Collector) in each corner of the plot. These coordinates are stored in the fields Longitude, Longitude2, Longitude3, Longitude4, Latitude, Latitude2, Latitude3 and Latitude4 in the dataset.</p> <p>The dataset contains records of 148 vegetation plots, of which 66 were sampled three times (1970s, 2000s, 2021) and 82 were sampled twice (2000s, 2021), i.e. 362 vegetation-plot records in total. Their locations are shown at <a href="https://arcg.is/0uSrz9">https://arcg.is/0uSrz9</a>.</p> <p>Plot size and shape varied by vegetation type. Repeated sampling always used the same plot size as the previous sampling. Most plots were squares of 100 m<sup>2</sup> in woodlands and 16 m<sup>2</sup> in grasslands or rectangles of 10 m<sup>2</sup> in springs. All species of vascular plants were recorded in each plot, and their cover was estimated using the nine-grade Braun-Blanquet scale (Westhoff & van der Maarel, 1978). Bryophytes and lichens were recorded in only some plots, focusing on the dominant species.</p> <p>In 2021, soil samples were collected from the mineral soil horizon at 5–10 cm depth from four places within each plot. We mixed these samples for each plot and measured the soil pH and electrical conductivity of a mixed sample in a soil-water suspension (weight ratio 1:2.5) using the HQ40D digital multimeter. These data are found in the fields Soil_ph and Conduct.</p> <p>Plots were divided into managed and unmanaged based on the overlay of plot coordinates with GIS layers indicating the areas managed for conservation purposes in the last ten years provided by the Administration of the Landscape Protected Area Jeseníky (field Mown). In addition, plots were divided into affected and unaffected by the 2019 avalanche based on the positions of damaged trees observed in the field (field Avalanch).</p> <p>The structure of header data follows the structure of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, the vegetation type of each plot record in the 1970s, 2000s and 2021 is given in the fields Class_2021, Class_2000 and Class_1970, respectively</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_Jeseniky_resurvey.zip</strong>. To use this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) have to be installed.</li> <li>Two TXT files with columns separated by tabs: <ul> <li><strong>Jeseniky_resurvey_species.txt</strong> contains the percentage covers of plant species in plots. Plant nomenclature harmonized according to Kaplan et al. (2019). Bryophytes, vernal species <em>Anemone nemorosa </em>and <em>Cardamine pratensis</em>, hybrids and species that could not be accurately identified were excluded. Woody species occurring in different layers were merged within each plot.</li> <li><strong>Jeseniky_resurvey_head.txt</strong> includes information about the number of species in each plot (spe.nr), the number and proportion of threatened species (IUCN categories CR, EN, VU, columns end.nr and perc.end) and unweighted means of Ellenberg-type indicator values for light (Light_IV), temperature (Temp_IV), moisture (Moist_IV), soil reaction (React_IV) and nutrients (Nutr_IV) used to test changes in these variables through time.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
The SEA CHANGE Study: A Self Management Intervention for Head and Neck Cancer Survivors
ClinicalTrials.gov study NCT04051697. IPD Sharing: NO. Countries: 1. Publications: 0.
Role of rhPSMA-7.3 PET/CT Imaging in Men With High-Risk Prostate Cancer Following Conventional Imaging and Associated Changes in Medical Management
ClinicalTrials.gov study NCT05799248. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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)
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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.