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13,276 results for “IT management”
Data for "Water (or the Lack Thereof), Management, and Conservation of an Endangered Desert Wetland Obligate, Lilaeopsis schaffneriana var. recurva"
<p>Raw and RData forms of data for "Water (or the Lack Thereof), Management, and Conservation of an Endangered Desert Wetland Obligate, <em>Lilaeopsis schaffneriana </em>var. <em>recurva". </em>Consists of six Excel files, with names corresponding to the type of data.</p> <ol> <li>field_ecology_data.xlsx </li> <li>experiment_randomization.xlsx </li> <li>experiment_entered_data.xlsx </li> <li>resilience_days_to_critical.xlsx </li> <li>resilience_experiment_data.xlsx </li> <li>resilience_leaf_density_data.xlsx </li> </ol> <p>Four RData files of the loaded Excel data, and one text file to explain the coding of the drought experiment data.</p>
Interview guideline for manager of maker space
<p>In the framework of the EU funded project Make-IT, 10 case studies of maker spaces in different European countries have been compiled based on four interviews each (amongst other data). One interview was conducted with the manager of the maker space and three interviews with makers who regularly made use of the maker space. The results are publised in aggregated form in D3.1 and D3.2 on the project's website: http://make-it.io/</p>
Dataset for: Research data management in academic institutions: a scoping review
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Blondal E, Ayala AP, Dearborn D, Kenny T, Lightfoot D, Reka R, Thuna M, Trimble L, MacDonald H. Research data management in academic institutions: A scoping review. PLOS One. 2017 May 23;12(5):e0178261. doi: 10.1371/journal.pone.0178261.</p> <p>Full-text available at: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178261 ">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178261 </a></p> <p><strong>Data and Documentation Files</strong> </p> <p>Five files make up the dataset: </p> <ol> <li>Data Dictionary: RDMScopingReview_DataDictionary.pdf</li> <li>Data Abstraction Sheet: RDMScopingReview_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMScopingReview_Setting.csv</li> <li>Data Abstraction Sheet: RDMScopingReview_DataCollectionTools.csv</li> <li>Data Abstraction Sheet: RDMScopingReview_Outcomes.csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Supplementary Material: Evaluation of Cyanea capillata Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models
<p>Supplementary files for Doyle, T.K.; Headlam, J.L.; Wilcox, C.L.; MacLoughlin, E.; Yanagihara, A.A. Evaluation of <em>Cyanea capillata</em>Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models. <em>Toxins</em> <strong>2017</strong>, <em>9</em>, 215. Video S1: Vinegar Application to Gelatin-Adherent Cnidae</p>
NeuroGentoo Presentation - Bringing the Power of Gentoo Package Management to (Neuro)Scientific Software Environments
<p>Neuroscience, one of the most computation-reliant fields in the natural sciences, is dependent upon dozens of highly complex software suites, which scientists are often forced to manage manually. Upstream developers often ship bundled dependencies to better support this flawed workflow, and in the resulting mess documenting and reproducing analysis pipelines is neigh-impossible. We seek to correct these shortcoming of both modern software distribution as well as modern data science, by integrating high-quality ebuilds for neuroscientific software into the Gentoo Science Overlay. We also seek to publish a simple NuroGentoo world file (along with appropriate usage instructions) to allow scientists with access to OpenStack, Amazon Elastic Computing, or Docker to launch up and build a system fit for reproducing data analysis run on other NeuroGentoo systems with minimum effort.</p>
TalentCLEF 2025 corpus: Skill and Job Title Intelligence for Human Capital Management
<blockquote> <p><strong>🚨 Current Status: Submission of Working Notes</strong></p> </blockquote> <p><strong>If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo: </strong></p> <pre><code>@inproceedings{gasco2025overview, title={{Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management}}, author={Gasco, Luis and Fabregat, Hermenegildo and Garc\'{\i}a-Sardi{\~n}a, Laura and Estrella, Paula and Deniz, Daniel and Rodrigo, \'{A}lvaro and Zbib, Rabih}, booktitle={{International Conference of the Cross-Language Evaluation Forum for European Languages}}, year={2025}, publisher={Springer} }</code></pre> <p> </p> <h2><strong><a href="https://talentclef.github.io/talentclef/" target="_blank" rel="noopener">TalentCLEF2025</a> corpus - </strong>Task B Test set release</h2> <h3><strong>Introduction:</strong></h3> <p>The first edition of TalentCLEF aims to develop and evaluate models designed to facilitate three essential tasks:</p> <ol> <li>Finding/ranking candidates for job positions based on their experience and professional skills.</li> <li>Implementing upskilling and reskilling strategies that promote the coninuous development of workers</li> <li>Detecting emerging skills and skills gaps of importance in organizations.</li> </ol> <p>With that aim, the task is divided into two tasks: </p> <ul> <li><strong>Task A - Multilingual Job Title Matching</strong>. This task involves developing systems to identify and rank the job titles most similar to a given one by generating a ranked list of similar titles from a specified knowledge base for each job title in a provided test set.</li> <li><strong>Task B - Job Title-Based Skill Prediction. </strong>Task B requires developing systems that can retrieve relevant skills associated with a specified job title.</li> </ul> <div> <p>This data repository contains the data for these two tasks. The data is being released progressively according to the<a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/schedule/"> task schedule</a>.</p> <p>The task evaluation takes place on Codabench (<a href="https://www.codabench.org/competitions/5842/">Task A</a> and <a href="https://www.codabench.org/competitions/7059/">Task B</a>). Participants must register for the competition through <a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a> to be part of the evaluation campaign.</p> <p> </p> </div> <h3><strong>File structure: </strong></h3> <div> <div> <div> <div> <blockquote> <p>For a detailed description of the data structure, you can refer to the <a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/description_corpus/">TalentCLEF2025 data description page,</a> where it is thoroughly explained.</p> </blockquote> </div> </div> </div> </div> <p>The files is organized into two <code>*.zip</code> files, <code>TaskA.zip</code> and <code>TaskB.zip</code>, each containing training, validation and test folders to support different stages of model development. So far, only the training set for both tasks has been released, but in future releases, as the tasks progress, additional data will be added to the different subfolders for each task.</p> <p><strong>TaskA</strong> includes language-specific subfolders within the training and validation directories, covering English, Spanish, German, and Chinese job title data. The training folders for TaskA contain language-specific .tsv files for each respective language. Validation folders include three essential files—queries, corpus_elements, and q_rels—for evaluating model relevance to search queries. TaskA’s test folder has queries and corpus_elements files for testing retrieval.<br><br></p> <pre><code>TaskA/ │ ├── training/ │ ├── english/ │ │ └── taskA_training_en.tsv │ ├── spanish/ │ │ └── taskA_training_es.tsv │ └── german/ │ └── taskA_training_de.tsv │ ├── validation/ │ ├── english/ │ │ ├── queries │ │ ├── corpus_elements │ │ └── qrels │ ├── spanish/ │ ├── german/ │ └── chinese/ │ └── test/ ├── english/ │ ├── queries │ └── corpus_elements ├── spanish/ ├── german/ └── chinese/ </code></pre> <p><strong>TaskB</strong> follows a similar structure but without language-specific subfolders, providing general .tsv files for training, validation, and testing. This consistent file organization enables efficient data access and structured updates as new data versions are published.</p> <pre><code>TaskB/ │ ├── training/ │ ├── job2skill.tsv │ ├── jobid2terms.json │ └── skillid2terms.json<br>│ ├── validation/ │ ├── queries │ ├── corpus_elements │ └── qrels │ └── test/ ├── queries └── corpus_elements </code></pre> <p><strong>Tutorials:</strong></p> <table> <tbody> <tr> <td><strong>Notebook</strong></td> <td>Link</td> </tr> <tr> <td>Data Download and Load using Python </td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_data_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Development set Baseline generation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskA_DevSet_Baseline.ipynb">Link to Colab</a></td> </tr> <tr> <td>Task B - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskB_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> </tbody> </table> <p><strong>Resources: </strong></p> <ul> <li><a href="https://talentclef.github.io/talentclef/docs/" target="_blank" rel="noopener">Web</a></li> <li><a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a></li> <li><a href="https://www.codabench.org/competitions/5842/">Codabench Task A</a></li> <li><a href="https://www.codabench.org/competitions/7059/">Codabench Task B</a></li> <li><a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/additional_resources/">Additional Resources</a></li> </ul>
CESM2 Atmospheric CO2 without agricultural management
This dataset was created to understand the impacts of agriculture on CO2 concentrations. Two 1-degree simulations were branched from the CMIP6 "CESM2-esm-hist" simulation in 1970. The first of these turned off the explicit representation of agriculture so that all crop areas are represented as "generic" C3 crops, where crop phenology is simulated as C3 grasses and do not include irrigation or fertilization (referred to as "generic crop"). The second uses the explicit representation of agriculture but removes industrial N fertilization (referred to as "no fertilization"). To ensure that changes in CO2 fluxes were minimally impacted by model drift, each simulation equilibrated carbon fluxes in 1970 by cycling over a single year of forcing for ten years. The CESM2 simulated these alternative representations of agriculture in a CO2 emissions-forced historical scenario following the "esm-hist" experimental protocol.
Dataset on light measuments in the understory of a Tropical forest restoration submitted to four thinning intensities through chemical management
<p>The lack of information on the management of light in tropical forests causes a technical constraint for timber production in restoration sites, especially given the light restrictions for timber production. This issue could be amended with the development of methods to easily manage and estimate light availability, targeting practices that balance restoration success and productivity. We conducted the study that gathered this data in an area within the Atlantic Forest, Brazil, where we tested the efficiency of chemical thinning fast-growing species to increase light availability in the understory of a five-year-old restoration planting. Our goal was to increase the growth rates of desirable timber species in the understory of the restoration site. <br>Moreover, we tested the viability of using hemispherical photography taken with a smartphone to assess light incidence and assist restoration management practices. We calculated the percentage of photosynthetically active radiation (PAR) using a ceptometer in four different thinning intensities and compared them to the smartphone measures using correlation analysis and generalized mixed models. Chemical thinning increased light incidence in the understory Light management through PAR and canopy opening were highly correlated overall, especially after three months of management and above 60% of the basal area thinned. Data demonstrates the potential of chemical thinning as a management practice to enhance light availability in the understory of tropical forest restoration sites and highlights the value of using smartphones and fisheye clips for the indirect assessment of light conditions. </p>
Information flows around agricultural best management practices in central Pennsylvania
<p>This dataset was collected between February and April 2019, to assess the information network of agricultural Best-Management Practices (BMPs) in central Pennsylvania, a sub-region of the Chesapeake Bay watershed.</p> <p>It contains information flows (or "messages") relating to 16 specific BMPs, including:</p> <ul> <li>the BMP it relates to (e.g. riparian buffers, manure management planning, no-till, cover-cropping, etc.);</li> <li>the source and target of the information (actors);</li> <li>the kind of message (e.g. funding, regulation, technical assistance, etc.);</li> <li>the weight (strength) of messages (only for those received by farmers directly).</li> </ul> <p>Over 3900 messages/information flows were recorded, involving 57 actors.</p> <p>This data was used to conduct the study "Navigating agricultural nonpoint source pollution governance: A social network analysis of best management practices in central Pennsylvania".</p>
Research Data Management in Selected Health Research Institutions in Uganda
<p>This data set was collected from Researchers in three purposively selected health reseach Institutions in Uganda. The purpose of the study was to explore compliance to FAIR data princiles and Open science initiative given the increasing dependence on donor funding and need to fulfill the requirement for good research practices. </p>
Figure 6 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 6. Moth (A); coffee leaf damage (B and C); caterpillar at the beginning of pupal stage (D); and the characteristic X-shaped cocoon of coffee tree miner (E).
Figure 1 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 1. Rosette with coffee berry borer attack symptom (A); and detail of the pest hole in the crown region of the fruit (B).
Figure 9 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 9. Orthezia colony (Praelongorthezia praelonga) on coffee leaves (A); leaf covered with dark- colored fungus, commonly referred to as sooty mold (B); and coffee plant with high defoliation caused by the pest (C).
Figure 7 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 7. Flower bud (A), branches (B) and rosette (C) of the coffee tree infested with citrus mealybug.
Figure 3 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 3. Prorops nasuta - Uganda wasp (A); C. stephanoderis - Ivory Coast Wasp (B); and C. hyalinipennis (C).
Figure 12 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 12. Young form and pupa of the honeydew moth caterpillar and damages the coffee tree rosette.
Figure 4 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE
Figure 4. Parasitism stages of the Ivory Coast Wasp, Cephalonomia stephanoderis from the coffee berry borer larva (top of the figure) and the coffee berry borer pupa (bottom). Source: Benassi (1996).
Ecological forecasts for marine resource management during climate extremes
<p><span>Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. <span>We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members.</span> Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.</span></p>
Strategic crisis management in the EU: reference list
<p>This dataset includes all the bibliographic references from the SAPEA evidence review report 'Strategic crisis management in the EU'.</p>
Characterizing juvenile dispersal dynamics of invasive barred owls: implications for management
<p>Characterizing natal dispersal can help manage the spread of invasive species expanding their ranges in response to land use and climate change. The Barred Owl (<em>Strix varia</em>) is a prominent example of an apex predator undergoing a rapid range expansion, having spread from eastern to western North America where it is now hyperabundant—threatening the Northern Spotted Owl (<em>S. occidentalis caurina</em>) with extinction and potentially endangering many other native species. We attached satellite tags to 31 Barred Owl juveniles at the southern leading edge of the Barred Owl's expanding range in California to characterize natal dispersal patterns and inform management. Juveniles traveled up to 100km from natal territories and experienced high mortality (annual survival = 0.204). At landscape scales, juveniles preferentially used forests, shrublands, and lower elevations during dispersal and avoided grasslands and burned areas. At finer scales, juveniles preferred shorter (younger) forests, lower elevations, and drainages, and avoided unforested areas. Our results suggest the Barred Owl range expansion is being driven primarily by high reproductive rates and densities despite low juvenile survival rates and dispersal through putatively suboptimal younger forests as a result of exclusion from high-quality habitat by territorial individuals. These findings also point to several strategies for conserving Spotted Owls and other native species in the Barred Owl's expanded range, including: (1) creating and maintaining Barred Owl-free reserves bounded by open or high-elevation areas; (2) creating reserves large enough to reduce immigration by long distance dispersers; and (3) removing Barred Owls from large riparian corridors. </p>
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.