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7 results for “Product Metrics”
Country-wide data products for the ecosystem structure metrics derived from ALS data across the Netherlands (AHN3)
<p>This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN3). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from AHN3 dataset (<a href="https://downloads.pdok.nl/ahn3-downloadpage/">https://downloads.pdok.nl/ahn3-downloadpage/</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="../record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>). The Jupyter Notebooks for the processing of the AHN3 dataset are available on GitHub (<a href="https://github.com/eEcoLiDAR/AHN/tree/main/AHN3">https://github.com/eEcoLiDAR/AHN/tree/main/AHN3</a>).</p> <p>The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity), and a layer of point density and a layer of building/road/water mask are also provided. Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn3_10m_feature_name.tiff").</p> <p>An overview of all the listed metrics (maps) is also provided in the PDF version (AHN3.pdf).</p> <p>A detailed description of the dataset is available from the following data publication:<br>Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2022. Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data in Brief: 108798.<br><a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.dib.2022.108798&data=05%7C01%7Cy.shi%40uva.nl%7C177a19a4359a422b0ef808dad9d30ef8%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C638061797757145956%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2R7NSGli4Mw6Pp5FAIyOzBu4USPZXigng46EFVT4X68%3D&reserved=0">https://doi.org/10.1016/j.dib.2022.108798</a></p> <p>A detailed description of all the metrics can be found in the README file (README.docx). </p> <p>A .zip file is also provided containing all the data for the validation of the AHN3 data products (AHN3_validation.zip). </p>
Repository Analytics and Metrics Portal (RAMP) Production Snapshot Dataset, 2018-11-01
<p>The data are publicly available via Globus: <a href="https://app.globus.org/file-manager?origin_id=a40be90c-f8c2-11e8-9340-0e3d676669f4&origin_path=%2F">https://app.globus.org/file-manager?origin_id=a40be90c-f8c2-11e8-9340-0e3d676669f4&origin_path=%2F</a></p> <p>The data consist of a snapshot of the production RAMP Elasticsearch instance [http://ramp.montana.edu/](http://ramp.montana.edu/). The snapshot was taken on November 1, 2018, and consists of 51 indices (one index each for 50 participating institutional repositories (IR) plus one master index or alias that provides computational access to all indices at once). In addition to the snapshot itself, the published dataset includes documentation describing data collection and processing, separate documentation of the requirements and steps to restore the snapshot to a working instance of Elasticsearch, a CSV file listing participating IR and their corresponding Elasticsearch index names, and a Jupyter Notebook with sample Python code for accessing Elasticsearch.</p> <p>The snapshot ID needed to restore the snapshot to a working index is '2018-11-01.' Please see the included file, 'restore_RAMP_snapshots.pdf' for more info.</p> <p>Because of the large file size, download via high speed network is recommended.</p> <p>RAMP development was funded by the Institute of Museum and Library Services (IMLS) as part of the "Measuring Up" project: IMLS: LG-06-14-0090<br> </p>
Postural stability metrics associated to the publication: Additive manufacturing of spinal braces: evaluation of production process and postural stability in patients with scoliosis
<p>Data was collected for each condition (3D-printed brace, conventional brace, unbraced) for 60 seconds with patients in a standing posture, open eyes and both feet together.</p>
Revisiting process versus product metrics: A large scale analysis
<p>Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a handful of projects) to analytics in-the-large (dealing with hundreds of projects)? To answer this question, we recheck prior small-scale results (about process versus product metrics for defect prediction and the granularity of metrics) using 722,471 commits from 700 Github projects. We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the-large. For example, like prior work, we see that process metrics are better predictors for defects than product metrics (best process/product-based learners respectively achieve recalls of 98%/44% and AUCs of 95%/54%, median values).</p> <p>That said, we warn that it is unwise to trust metric importance results from analytics in-the-small studies since those change dramatically when moving to analytics in-the-large. Also, when reasoning in-the-large about hundreds of projects, it is better to use predictions from multiple models (since single model predictions can become confused and exhibit a high variance).</p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Evaluation metrics for eight types of gap-filled snow cover products in China and four schemes of combining multiple products
<p>This dataset contains the station-based evaluation metrics in terms of CK, R of SCD, and CWR values of SSD and SED for all the eight types of gap-filled products and the proposed schemes for combining multiple products. "TP" means the Tibetan Plateau.</p>
Shortcomings of Event-Based Metrics [Supplement to PhD "Machine-Actionable Assessment of Research Data Products"]
<p>This deposition includes a tabular overview of all publications analyzed by my PhD to identify and classify shortcomings of event-based metrics for research data products. The tabular overview is linked via its field "Key" to the bibtex file which holds the bibliographic information to replicate the results shown in the table.</p> <p>The deposition is supplementary material to the dissertation "Machine-Actionable Assessment of Research Data Products" (Tobias Weber, yet unpublished), especially chapter 3.</p>
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International Brain Laboratory public data
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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.