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2,045 results for “planning”
PM_024568_E_Els_Plans_de_Sio
<u>File Name</u>: PM_024568_E_Els_Plans_de_Sio.jpg <br><u>Sublocation</u>: Església de Pelagalls <br><u>Location</u>: Els Plans de Sió <br><u>Province</u>: Catalunya, Lleida <br><u>Country</u>: Spain <br><u>Header</u>: Exterior; Façana oest; Romànic; 1180; <br><u>Description</u>: Pelagalls Parish church (Església de Pelagalls) Exterior West facade Romanesque 1180 <br><u>Keywords</u>: Catalunya, Cultural heritage, Els Plans de Sió (Segarra), Europe, Lleida, Monuments, Romanesque, Romanesque Catalonia, Spain, Styles <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans
<p>This dataset is published in 2017 by the OpenAIRE project and the FAIR Data Expert Group.</p> <p>It contains two survey data files, two pdf-files summarising the results in a report and an infographic, and a Readme.txt file.</p> <p>The OpenAIRE project supports the open science ambitions of the European Commission. The project and in particular the Research Data Management team provide support, training and information on the Open Research Data Pilot. In this context, a survey was carried out to collect feedback on the Horizon 2020 template for Data Management Plans (DMPs). The team collaborated with the FAIR data expert group, which is providing recommendations to the European Commission on turning FAIR data into reality. One of the specific tasks of the Expert Group is contributing to an evaluation of the Horizon 2020 approach to DMPs, including future revisions of the template and the development of additional sector/ discipline-specific guidance. The aim of the survey was to collect experiences of researchers and DMP reviewers with the DMP template and guidelines on FAIR data management in Horizon 2020. The survey assesses the usefulness of the guidelines and any aspects that are confusing and unclear to determine what improvements can be made.</p> <p>Feedback was sought from both researchers and research support staff. The survey was initially scheduled to run from 22 May to 21 June 2017. Several organisations were asked to help announce the survey, including OpenAIRE’s National Open Access Desks, the FAIR data expert group, FOSTER, LIBER, and the RDA Interest Group on Active DMPs. When the first survey responses showed only a small share of researchers, more stakeholders were contacted to specifically target this community. The European Research Area was approached, whose project officers circulated the survey call among award holders of EC projects. Early-career researchers were also informed through the YEAR network and EURODOC. This resulted in an extension of the survey to 21 July 2017.</p> <p>At the close of the survey on 21 July 2017, a total number of 289 responses were reached. 50% of the respondents indicated that they were researchers, and 60% that they were (also) research support staff. OpenAIRE and the FAIR data expert group are very pleased with this balanced outcome and would like to thank all colleagues and organisations who promoted the survey, as well as everyone who took part in it.</p> <p> </p>
Dataset: Open access potential and uptake in the context of Plan S - a partial gap analysis
<p>Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.3543000">Open access potential and uptake in the context of Plan S - a partial gap analysis</a></p> <p> </p> <p>On the report: </p> <p>The analysis presented in the report, carried out by Utrecht University Library, aims to provide cOAlition S, an international group of research funding organizations, with initial quantitative and descriptive data on the availability and usage of various open access options in different fields and subdisciplines, and, as far as possible, their compliance with Plan S requirements.</p> <p>Plan S, launched in September 2018, aims to accelerate a transition to full and immediate Open Access. In the guidance to implementation, released in November 2018 and updated in May 2019, a gap analysis of Open Access journals/platforms was announced. Its goal was to inform Coalition S funders on the Open Access options per field and identify fields where there is a need to increase the share of Open Access journals/platforms. </p> <p>The report should be seen as a first step: an exploration in methodology as much as in results. Subsequent interpretation (e.g. on fields where funder investment/action is needed) and decisions on next steps (e.g. on more complete and longitudinal monitoring of Plan S-compliant venues) is intentionally left to cOAlition S and its members. </p> <p> </p> <p><em>This work was commissioned on behalf of cOAlition S by the Dutch Research Council (NWO), a member of cOAlition S. Bianca Kramer and Jeroen Bosman of Utrecht University Library were appointed to lead the project.</em></p>
Reviewed literature on the state of conservation planning in Europe
<p><strong>Literature review of European conservation planning studies</strong></p> <p>The data table uploaded here contains reviewed literature as part of the manuscript "An assessment of the state of conservation planning in Europe” by Jung et al. that is currently in Review. In this work we reviewed all available scientific literature broadly dealing with conservation planning in various facets and across realms (terrestrial, freshwater, marine). The database provided here thus provides a comprehensive starting point of all scientific conservation planning studies conducted in Europe up until mid 2023.</p> <p>The dataset is derived from a Scopus literature query conducted on the 23th of September 2022, which resulted in an initial 1459 studies which were further refined and supplemented by evidence known to the authors.</p> <p>---</p> <p># <strong>Description of table columns</strong>:</p> <ul> <li>"ID" = Numeric Identifier of the study</li> <li>"Extent" = Scale the study was conducted, from local, regional, national to European wide</li> <li>"Region" = The broad region with regards to European country</li> <li>"Locality" = Additional detail on the locality of the study if easily available</li> <li>"Realm" = Which realm does the study cover (e.g. Terrestrial, Marine, ...)</li> <li>"Ecosystem.specificity" = Was the study conducted only for specific ecosystems (e.g. Forests)?</li> <li>"Period" = Over which period was the study conducted (Present only, future conditions, both)</li> <li>"Planning.purpose" = What was the purpose of the study?</li> <li>"Policy.relevance" = Specific policy directives or legal documents referred to in the study introduction.</li> <li>"Method" = Which method was used for the planning purpose (i.e., Zonation, Marxan, ...)</li> <li>"Biodiversity.type; = What type of Biodiversity data was included in the study?</li> <li>"Number.of.features" = How many number of features?</li> <li>"Multiple.objectives.or.constraints" = Did the study somehow account for multiple objectives or constraints?</li> <li>"Connectivity" = Was connectivity somehow considered and if so, how?</li> <li>"Costs" = Were socio-economic costs somehow considered?</li> <li>"Stakeholder.involvement" = Were stakeholders involved in the planning exercise at any point?</li> <li>"Authors" = Authors of the study</li> <li>"Title" = The title of the study</li> <li>"Year" = The year it was published</li> <li>"Journal" = The scientific journal were it was published</li> <li>"DOI" = A Digital Object identifier link (can sometimes be missing)</li> <li>"Link" = A link to the journal website (can be missing)</li> <li>"Author.Keywords" = Keywords by the authors given to the study (can be missing)</li> <li>"Index.Keywords" = Keywords captured by SCOPUS for the study (can be missing)</li> <li>"Document.Type" = Type of article</li> <li>"Source" = Derived from SCOPUS or manually added through a snowballing approach?</li> <li>"cite_scientific_May2023" = How often cited in the scientific literature by May 2023?</li> <li>"cite_policy_May2023" = How often cited in policy literatue by May 2023?</li> </ul> <p>---</p> <p>The analysis code supporting the manuscript and analysing the dataset presented here can be found <a href="https://github.com/Martin-Jung/Review_EuropeanConservationPlanning">here</a>. A preprint of the manuscript can be found <a href="http://dx.doi.org/10.31219/osf.io/8x2ug">here</a>.</p>
Relative Abundance of Soft Algae From the Comprehensive Everglades Restoration Plan (CERP) Study (FCE), Florida, USA, September 2005 to November 2011
Relative soft algae data collected between September 2005 and November 2011 "The Comprehensive Everglades Restoration Plan (CERP) focuses on “getting the water right” in the south Florida ecosystem—getting the right amount of water of the right quality to the right places at the right time" (USACE & DoI, 2015. Central and Southern Florida Project Comprehensive Everglades Restoration Plan) in the Everglades ecosystems. To inform CERP, since February 2005 we have been investigating the spatio-temporal variations of distribution, biomass and diversity of algae (key aquatic primary producers) in periphyton mats in relation to hydrology, nutrients and pH, and other environmental conditions.
Analysis of the planning process for a new park in Minneapolis, Minnesota, at the Upper Harbor Terminal site with planners and community members focusing on redevelopment that addresses green gentrification concerns, 2019 to 2021
This dataset is from a study focuses on the Minneapolis Park and Recreation Board’s planning process for a new park at the Upper Harbor Terminal (UHT) site - a defunct barge-to-rail terminal on the Mississippi River being redeveloped with a mix of housing, commercial uses, and park space (see "UHT_location_map in Other Entities). While the redevelopment affords an opportunity to remediate pollution and provide new greenspace, a high portion of nearby residents are low-income and housing-cost burdened, raising concerns among community members about gentrification. As a counter to green gentrification, the concept of “Just Green Enough” (JGE) became a common theme around the UHT development process. JGE aims to center community-centered greening efforts with broader community development goals in mind (i.e. living-wage jobs and affordable housing). Amidst growing community pressure, the Minneapolis Park and Recreation Board (MPRB) appointed a Community Advisory Committee (CAC). The CAC was tasked with meeting monthly for in-depth deliberations (facilitated by planning staff and external consultants) with the goal of making final park design and programming recommendations, which planning staff would present to the MPRB Board of Commissioners. The Upper Harbor Terminal CAC consisted of 16 members, mostly residents of North Minneapolis and Northeast, and many with a background in nonprofit, environmental, or community organizing work. Meetings began in July 2019 and lasted nearly two years until in May 2021. This dataset includes the qualitative codebooks for the CAC meetings, accompanied by the meeting minutes and other documents related to the UHT planning process. The transcripts of semi-structured interviews with stakeholders in the UHT planning process were also analyzed and coded, but specific quotations are omitted from this dataset to protect the privacy the participants.
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
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>
Plan of Bhopāl, Madhya Pradesh, India
<p>Plan of Bhopāl, Madhya Pradesh, India, showing this disposition of dams, location of Fatehgarh and other key monuments, and the layout of the medieval Paramāra city based on the configuration of streets in the old town.</p>
Udayagiri, Madhya Pradesh. Plan of the central ridge.
<p>Udayagiri, Madhya Pradesh. Plan of the central ridge, showing location of caves 3, 4, 5, 6, 8 and 13 as well as water features and archaeological features: A) astronomical platform; B) mound marking location of early pillar and lion capital, C) temple mound.</p>
Udayagiri, Madhya Pradesh. General plan of the site.
<p>Udayagiri, Madhya Pradesh. General plan of the site, showing configuration of the hill, the location of principal caves, temples and main tanks.</p>
plan view TEM stereo pair of microcracks
<p><a name="_Toc162747595"></a><strong><em><span>Figure SI1.</span></em></strong><span><strong><em><span><span>2</span></span></em></strong></span><span><em><span>: The cross-eye stereo pair can be difficult to visualise.<span> </span>This animation of the same images allows the three-dimensional structure to be seen using motion parallax.</span></em></span></p>
EUGAIN Policy Influence Plan Template
<p>This is the EUGAIN Policy Influence Plan Template.</p> <p>It was created within the European Union COST Action CA-19122 in 2024, published as figure in Deliverable 8, the Handbook.</p>
Systematic review data on the role of urban planning in the context of sustainability transformations and human-nature connections
<p>This data publication belongs to the following research paper:<br>Harms, P., Hofer, M. & Artmann, M. Planning cities with nature for sustainability transformations — a systematic review. Urban Transform 6, 9 (2024). <br>https://doi.org/10.1186/s42854-024-00066-2 </p> <p>We conducted a systematic literature review according to the PRISMA Statement 2020 (Page et al. 2021). The list shows the steps performed and the names of the corresponding datasets available here:</p> <p>Step A - Identification of Records<br>A_01_PRISMA-protocoll.pdf<br>A_02_searchstring.txt<br>A_03_recordsidentified.ris</p> <p>Step B - Screening of Records<br>B_01_recordsscreened-title-keywords.ris<br>B_02_recordsscreened-abstract.ris<br>B_03_recordsscreened-fulltext.ris<br>B_04_studiesincluded.ris<br>B_05_screeningdecisions-overview.xlsx</p> <p>Step C - Qualitative Analysis<br>C_01_codingscheme.xlsx</p> <p> </p>
Structure Annotations of Assessment and Plan Sections from MIMIC-III
<p>Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the "assessment and plan". This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. To structure these sections we collected a dataset of annotations over assessment and plan sections from the publicly available and de-identified MIMIC-III dataset, and developed deep-learning based models to perform this task, described in the associated paper available as a pre-print at: <a href="https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1">https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1</a></p> <p>When using this data please cite our paper:</p> <pre><code>@article {Stupp2022.04.13.22273438, author = {Stupp, Doron and Barequet, Ronnie and Lee, I-Ching and Oren, Eyal and Feder, Amir and Benjamini, Ayelet and Hassidim, Avinatan and Matias, Yossi and Ofek, Eran and Rajkomar, Alvin}, title = {Structured Understanding of Assessment and Plans in Clinical Documentation}, year = {2022}, doi = {10.1101/2022.04.13.22273438}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2022/04/17/2022.04.13.22273438}, journal = {medRxiv} }</code></pre> <p>The dataset, presented here, contains annotations of assessment and plan sections of notes from the publicly available and de-identified MIMIC-III dataset, marking the active problems, their assessment description, and plan action items. Action items are additionally marked as one of 8 categories (listed below). The dataset contains over 30,000 annotations of 579 notes from distinct patients, annotated by 6 medical residents and students. </p> <p>The dataset is divided into 4 partitions - a training set (481 notes), validation set (50 notes), test set (48 notes) and an inter-rater set. The inter-rater set contains the annotations of each of the raters over the test set. Rater 1 in the inter-rater set should be regarded as an intra-rater comparison (details in the paper). The labels underwent automatic normalization to capture entire word boundaries and remove flanking non-alphanumeric characters.</p> <p>Code for transforming labels into TensorFlow examples and training models as described in the paper will be made available at GitHub: <a href="https://github.com/google-research/google-research/tree/master/assessment_plan_modeling">https://github.com/google-research/google-research/tree/master/assessment_plan_modeling</a></p> <p>In order to use these annotations, the user additionally needs to obtain the text of the notes which is found in the NOTE_EVENTS table from MIMIC-III, access to which is to be acquired independently (<a href="http://mimic.mit.edu">https://mimic.mit.edu/</a>)</p> <p>Annotations are given as character spans in a CSV file with the following schema:</p> <table> <tbody> <tr> <td>Field</td> <td>Type</td> <td>Semantics</td> </tr> <tr> <td>partition</td> <td>categorical (one of [train, val, test, interrater]</td> <td>The set of ratings the span belongs to.</td> </tr> <tr> <td>rater_id</td> <td>int</td> <td>Unique id for each the raters</td> </tr> <tr> <td>note_id</td> <td>int</td> <td>The note’s unique note_id, links to the MIMIC-III notes table (as ROW-ID).</td> </tr> <tr> <td>span_type</td> <td>categorical (one of [PROBLEM_TITLE,<br> PROBLEM_DESCRIPTION, ACTION_ITEM]</td> <td>Type of the span as annotated by raters.</td> </tr> <tr> <td>char_start</td> <td>int</td> <td>Character offsets from note start</td> </tr> <tr> <td>char_end</td> <td>int</td> </tr> <tr> <td>action_item_type</td> <td>categorical (one of [MEDICATIONS, IMAGING, OBSERVATIONS_LABS, CONSULTS, NUTRITION, THERAPEUTIC_PROCEDURES, OTHER_DIAGNOSTIC_PROCEDURES, OTHER])</td> <td>Type of action item if the span is an action item (empty otherwise) as annotated by raters.</td> </tr> </tbody> </table>
Dataset: Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya
<p>This is a set of openly-available data pre-processed to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. It provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis. The work to identify these data is submitted for publication - publication details will be added here as soon as possible upon release. These data are contained in spatial data files used to create the input for OnSSET in Kitui, and a prepared CSV data input for OnSSET in Kitui (<em>kitui_OnSSET_data</em>). The following spatial data files are included in the dataset:</p> <ul> <li>kitui_admin: A vector (.geojson) file containing the administrative boundaries of Kitui county. </li> <li>kitui_clusters: A vector (.geojson) file locating population clusters generated in data processing for OnSSET.</li> <li>kitui_demand: A raster (.tif) file containing merged health, agriculture, commercial, and residential demands for Kitui county in kWh.</li> <li>kitui_elevation: A raster (.tif) containing elevation information.</li> <li>kitui_GHI: A raster (.tif) file containing global horizontal irradiance data for Kitui county.</li> <li>kitui_hydro: A vector (.geojson) file containing the locations of hydropower stations in Kitui county. Note that there are none, and that this is expected.</li> <li>kitui_night_lights: A raster (.tif) file capturing the light emitted from Kitui county at night.</li> <li>kitui_power_stations: A vector (.geojson) file showing the locations of power stations in Kitui county.</li> <li>kitui_roads: A vector (.geojson) file showing the main roadways in Kitui county.</li> <li>kitui_transformers: A vector (.geojson) file showing transformer locations in Kitui county.</li> <li>kitui_transmission_lines: A vector (.geojson) file locating transmission lines in Kitui county. </li> <li>kitui_travel_hours: A raster (.tif) file showing travel time to the nearest market center in Kitui county.</li> <li>kitui_wind_100: A raster (.tif) file of wind speeds at 100 m in Kitui county.</li> </ul> <p>This dataset has been produced through work undertaken in the Climate Compatible Growth Programme.</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>
Kanheri (Bombay, Maharashtra, India). Plan of Kanheri caves complex
<p>Kanheri (Bombay, Maharashtra, India). Plan of Kanheri caves complex, dated 1881 and published in Campbell, James M. <em>Gazetteer of the Bombay Presidency</em>. Bombay: Government Central Press, 1896.</p>
Data for: Generation of sanitation system options for urban planning considering novel technologies
<p>This data has been used (1) to quantify the appropriateness of a set of sanitation technologies for a small town (Katarnyia) in Nepal and (2) to generate sanitation system options from the appropriate technologies as an input into strategic sanitation planning using a structured decision making process. For (1), the appropriateness is quantified based on a set of criteria, also called screening criteria. These criteria include technical, socio-demographic, climatic, and institutional aspects and are quantified using uncertainty functions in order to account for the quality and quantity of available input information.</p> <p>The data contains raw data as well as modelling results. The raw data is a compilation of information collected from literature, information collected through a household survey in the small town, field observations. They are all used to describe the screening criteria for the studied sanitation technologies and the small town. Results include: (1) the outcome of the technology appropriateness assessment (technology appropriateness scores); and (2) the sanitation system options (all possible sanitation systems built from the appropriate technologies, and a smaller set of divers and highly appropriate sanitation system options as an input into decision-making).</p>
Data Management Plan
<p>Video of a presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.