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Table 8. Calculation of regression equation of hydroxyproline standard solution At λ = 559 nm
<p>Table 8. Calculation of regression equation of hydroxyproline standard solution At λ = 559 nm</p>
Table 9. Result of absorbance measurement of hydroxyproline standard solution at = 559 nm
<p>Table 9. Result of absorbance measurement of hydroxyproline standard solution at = 559 nm</p>
Using standardized fish-specific autonomous reef monitoring structures (FARMS) to quantify cryptobenthic fish communities
<p>1. Biodiversity inventories and monitoring techniques for marine fishes often overlook small (<5cm), bottom-associated ('cryptobenthic') fishes, and few standardized, comparative assessments of cryptobenthic fish communities exist. We sought to develop a standardized, quantitative survey method for cryptobenthic fishes that permits their sampling across a variety of habitats and conditions.</p> <p>2. Fish-specific autonomous reef monitoring structures (FARMS) are designed to sample cryptobenthic fishes using a suite of accessible and affordable materials. To generate a variety of microhabitats, FARMS consist of three layers of stacked PVC pipes in three different sizes, as well as a bottom and top level of loose PVC-pipe fragments in a mesh basket. We deployed FARMS across a variety of habitats, including coral reefs, seagrass beds, oyster reefs, mangroves, and soft-bottom habitats across six locations (Hawai'i, Texas, Panama, Saudi Arabia, Brazil, and Curaçao).</p> <p>3. From shallow estuaries to coral reefs beyond 100 m depth, FARMS attracted distinct communities of native cryptobenthic fishes with strong site or habitat specificity. Comparing the FARMS to communities sampled with alternative methods (enclosed clove-oil stations on coral reefs in Panama and oyster sampling units on oyster reefs in Texas) suggests that FARMS yield a subset of cryptobenthic species representative of those present on local coral and oyster reefs. While FARMS yield fewer individuals per sample and are restricted to cryptobenthic species, they are efficient sampling devices relative to the sampled area.</p> <p>4. We demonstrate that FARMS represent a useful tool for standardized collections of cryptobenthic fishes. While natural substrata are bound to yield more mature communities with a larger number of individuals and wider range of specialist species, the potential to deploy and retrieve FARMS in turbid environments, beyond regular SCUBA depth, and where fish collections using anesthetics or ichthyocides are forbidden suggests that they are a valuable complementary technique to survey fishes in aquatic ecosystems. Deploying FARMS in locations and habitats where cryptobenthic fish communities have not been studied in detail may yield many valuable specimens of unknown or poorly known species.</p>
Accounting for forest condition in Europe based on an international statistical standard
<p>The datasets describe a European forest ecosystem condition account compliant with the System of Environmental-Economic Accounting. Four datasets are provided:</p> <p>1. An excel file with a forest extent and condition account at European level for 44 forest types</p> <p>2. A raster map with the forest typology</p> <p>3. A raster map with the forest condition index (0-1) at 100 m resolution for the years 2000 and 2018</p> <p>4. A map quantifying the uncertainty of the condition estimates using a semi-quantitative scale (low uncertainty, low to medium uncertainty, medium to high uncertainty, high uncertainty)</p>
10 - Falsifiability as a standard of evaluation of scientific theories
<p> </p> <p>Session #10 of the REINFORCE Critical & Scientific Thinking course, entitled, 'Falsifiability as a standard of evaluation of scientific theories', is given by Stefano Gattei, Professor of Science in Society, at the University of Trento, Italy.</p> <p>During the session, Prof. Gattei continues the examination of Popper's work and looks in detail at the ideas of conjecture and refutation.</p>
Nino-cunei/uruk: Moved tf files to standard location
<p>The tf files now reside under tf/1.0 instead of tf/uruk/1.0.</p> <p>A single file complete.zip is attached to the release.</p>
PubChem compounds before and after standardization
<p>This repository contains a subset of 200k PubChem compounds before and after standardization.<br> This dataset is the basis of the PubChem-pretrained model presented in "Standardizing chemical compounds with language models" (available on <a href="https://doi.org/10.26434/chemrxiv-2022-14ztf-v2">ChemRxiv</a>, see also the associated <a href="https://github.com/rxn4chemistry/rxn-standardization">GitHub repository</a>).</p> <p>The associated pretrained model is also provided here, along with the splits used for training.</p> <p>The data is provided under the <a href="https://cdla.dev/sharing-1-0/">CDLA-Sharing-1.0</a> license.</p> <p>Provided files:</p> <ul> <li>README.md: General README.</li> <li>src_and_tgt_all.csv: All the compounds before and after standardization, in CSV format.</li> <li>src-train.txt: The tokenized compounds before standardization in the train split.</li> <li>tgt-train.txt: The tokenized compounds after standardization in the train split.</li> <li>src-valid.txt: The tokenized compounds before standardization in the validation split.</li> <li>tgt-valid.txt: The tokenized compounds after standardization in the validation split.</li> <li>src-test.txt: The tokenized compounds before standardization in the test split.</li> <li>tgt-test.txt: The tokenized compounds after standardization in the test split.</li> <li>LICENSE.md: The details of the CDLA-Sharing-1.0 license.</li> <li>pretrained_pubchem_step_120000.pt: the pretrained model.</li> </ul>
Supplementary Data: Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider
<p><strong>Supplementary Data</strong></p> <p><em>Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider</em></p> <p>This record contains the full dataset generated for the study "<em>Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider</em>". It contains the following files:</p> <ul> <li>data_cross_sections_full_exact.csv - a CSV file with the soft breaking parameters M1, M2, mu and tanb, the neutralino/chargino masses, the cross sections for neutralino-neutralino, chargino-neutralino and chargino-chargino production at the LHC operating at a CM energy of 13 TeV, and the branching ratios of the unstable charginos/neutralinos. </li> <li>benchmark_points.zip - this zip file contains the SLHA files for the four benchmark points as shown in the paper, together with the prospino output for the cross section. </li> </ul> <p> </p>
Ring Width Index (RWI) and Standard Precipitation Index (SPI) data for Atmospheric River reconstruction
<p>These are the two datasets used for reconstructing atmospheric river activity along the U.S. west coast (under review at the Journal of Geophysical Research: Atmosphere). The first file rwi.mat contains the ring-width-index for the chronologies used to develop the second dataset (SPI_recon.nc). This file is used as the covariate to develop the AR reconstructions (with Poisson regression) and goes back to 1400 CE. </p>
MEDDOPROF Silver Standard: Predictions for Occupations, Working Statuses and Activities in Clinical Case Reports
<p><strong>MEDDOPROF Silver Standard</strong></p> <p>This repository contains the <strong>MEDDOPROF Silver Standard</strong>, a collection of clinical case reports of multiple specialties that have been automatically annotated with occupations, working statuses and activities using named entity recognition models trained on the MEDDOPROF corpus. MEDDOPROF and this Silver Standard have been created by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis.</p> <p><strong>About the MEDDOPROF Corpus</strong></p> <p><a href="https://temu.bsc.es/meddoprof/">MEDDOPROF</a> is a corpus and shared task focused on the detection and normalization of occupations and related information such as working statuses and non-paid activities in clinical texts in Spanish. All mentions in the corpus have been normalized to SNOMED CT, with occupations also being normalized to the European Skills, Competences, and Occupations classification ESCO. MEDDOPLACE was released as part of the IberLEF 2021 workshop, which in turn was held within the SEPLN 2021 conference.</p> <p><strong>About this Silver Standard</strong></p> <p>The Silver Standard includes 13,357 documents and a total of 5,568,844 tokens. The documents belong to over 10 different medical specialties, including occupational health, oncology, radiology, infectology, dermatology, psichiatry and more.</p> <p>The corpus includes three different labels: PROFESION (occupation), SITUACION-LABORAL (working status) and ACTIVIDAD (activity/hobby). There is a total of 4,332 predictions (1,325 unique), with the most common label being PROFESION with 3,599 predictions (1,181 unique), followed by SITUACION-LABORAL with 682 (180 unique) and ACTIVIDAD with 51 (44 unique).</p> <p>The annotations are presented in .ann format, which is the format used by the annotation tool brat.</p> <p>For more information, please check the attached README file.</p> <p><strong>Citation</strong></p> <p>If you use this Silver Standard or any MEDDOPROF materials, please cite the task's overview paper:<br> Lima-López, Salvador, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, & Martin Krallinger. “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts.” Procesamiento del Lenguaje Natural [Online], 67 (2021): 243-256.</p> <p><strong>License</strong><br> This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p><strong>Contact</strong><br> If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br> - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Acknowledgements</strong><br> MEDDOPROF was promoted by the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4ProfHealth - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).</p> <p><strong>Related Links</strong></p> <p>- MEDDOPROF website: <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a></p> <p>- MEDDOPROF Gold Standard: <a href="https://doi.org/10.5281/zenodo.5070540">https://doi.org/10.5281/zenodo.5070540</a></p> <p>- MEDDOPROF Annotation Guidelines: <a href="https://doi.org/10.5281/zenodo.4694675">https://doi.org/10.5281/zenodo.4694675</a></p>
A Standardized Precipitation Index (SPI) Climatology for the Continental U.S.
<p>The standardized precipitation index SPI (McKee et al. 1993) was designed to standardize precipitation timeseries across an observational record in order to compute precipitation anomalies in both time and space. The SPI is a widely used drought index that represents the number of standard deviations that observed precipitation deviates from the climatological average precipitation measured at the same location and time period. We calculated the SPI using monthly 4 kilometer resolution precipitation data from the Parameter-elevation with independent slopes model (PRISM; Daly et al. 1994) for the conterminous United States. Monthly PRISM data from 1948-2022 were aggregated to warm season (May-September) and annual (calendar year) totals. SPI anomalies were calculated for each seasonal time window relative to the 1948-2022 timeperiod.</p> <p>Each timeseries of annual precipitation totals was fit to a Gamma probability distribution based on the L-moments of the data. We computed the cumulative distribution function (CDF) associated with the observations using the parameters from the aforementioned Gamma distribution. The CDF values were then evaluated within an inverse Gaussian function with a mean of zero and a standard deviation of one to obtain the final SPI value</p> <p>Coordinate Reference System (CRS) = EPSG:4326</p> <p>McKee, T.B., N.J. Doesken and J. Kleist, 1993: The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January 1993. Boston, American Meteorological Society, 179–184.</p> <p>Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158.</p>
Living Standards Capabilities, Health-Related Quality of Life and Life Satisfaction among Older Adults
<p>This study attempts to analyse the Structural Relationship between Living Standards Capabilities, Health-Related Quality of Life and Life Satisfaction among Older Adults</p>
MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction
<p><strong>MedProcNER</strong> stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the <strong>detection, normalization and indexing of clinical procedures in medical documents</strong> in <strong>Spanish</strong>. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection.</p><p><strong>Please cite if you use this dataset:</strong></p><p>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</p><p>@article{lima2023overview, title={Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023}, author={Lima-L{\'o}pez, Salvador and Farr{\'e}-Maduell, Eul{\`a}lia and Gasc{\'o}, Luis and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, journal={Working Notes of CLEF}, year={2023} }</p><p>This repository includes the Train Set of the task, which includes a total of 750 documents, plus the annotated Test Set's 250 documents. A gazetteer of possible <strong>SNOMED CT codes</strong> for the normalization and indexing tasks is also part of the bundle as a lexical resource. </p><p>In addition, a cross-mapping file of all SNOMED CT codes to <strong>MeSH</strong> is also included.</p><p>Finally, we release an experimental <strong>multilingual Silver Standard</strong> version derived from the Spanish Gold Standard in <strong>9 languages</strong>: <i><strong>English</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Romanian</strong></i>, <i><strong>Czech</strong></i>, <i><strong>Dutch</strong></i> and <i><strong>Swedish</strong></i>. </p><p>These documents have been generated using an automatic annotation transfer process that works as follows:</p><ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using a lexical approach and custom dictionaries.</li></ol><p>MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/medprocner">https://temu.bsc.es/medprocner</a>.</p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/medprocner"><strong>Web</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Citation</strong></a><strong>: </strong>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</li><li><a href="https://doi.org/10.5281/zenodo.7817666"><strong>Annotation guidelines</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/"><strong>Proceedings and participant papers</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Overview paper</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/medprocnerproctemist-shared-task-on-clinical-procedure-detection-and-normalization-task-overview-presentation-at-bioasq-workshop-of-clef-2023-conference"><strong>Overview talk slides at BioASQ/CLEF</strong></a></li></ul><p> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in MedProcNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST </a>(Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
Cipher Machine Kryha Standard (Inv. 62797)
<p>This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine.<br> <br> object details:<br> name: Kryha Standard<br> Inv.-No. of the Deutsches Museum: 62797</p> <p>file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.181<br> Y pixel spacing: 0.181<br> Z pixel spacing: 0.181<br> grid size x: 1509<br> grid size y: 2040<br> grid size z: 681<br> color depth: 16Bit</p> <p>ownership & image acquisition:<br> project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a><br> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a><br> IP holder: Deutsches Museum<br> license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a><br> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a><br> scanning device: High Energy CT XXL-CT<br> Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a><br> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)</p>
Cipher Machine Kryha Standard (Inv. 2017-384)
<p>This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine.</p> <p>object details:<br> name: Kryha Standard<br> Inv.-No. of the Deutsches Museum: 2017-384</p> <p>file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.181<br> Y pixel spacing: 0.181<br> Z pixel spacing: 0.181<br> grid size x: 1431<br> grid size y: 854<br> grid size z: 1429<br> color depth: 16Bit</p> <p>ownership & image acquisition:<br> project: <a href="https://digital.deutsches-museum.de/en/projects/3d-cipher">3D-Cipher</a><br> collection: <a href="https://digital.deutsches-museum.de">Deutsches Museum</a><br> IP holder: Deutsches Museum<br> license: <a href="https://creativecommons.org/licenses/by/4.0/deed.en">Creative Commons BY-SA 4.0</a><br> scanning facility: <a href="https://www.iis.fraunhofer.de/en/ff/zfp.html">Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS</a><br> scanning device: High Energy CT XXL-CT<br> Funding attribution: <a href="https://www.bmbf.de/bmbf/en">German Federal Ministry of Education and Research</a><br> acknowledgement: <a href="https://www.cryptomuseum.com">CryptoMuseum</a> (as main source for informations about the cipher machines)</p>
Standard Error Estimates for ARRI Ensemble LM model outputs
<p>Summary</p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p>Description</p> <p>The LM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble linear regression models (LM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble LM model was created using a custom R script that was based off the work detailed in Hogland, 2021. The ensemble LM script was modified to use the lm() function in place of the GAM modelling functions. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble LM models for each cell were averaged (mean) to produce the LM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p>The 246 Restore field plots used to train the LM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p>References:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. <a href="https://doi.org/10.5281/zenodo.8222114">https://doi.org/10.5281/zenodo.8222114</a></p> <p>Credits</p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p>Use Limitations</p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
Incorporation of colonization pressure into the propagule pressure-based global ballast water standard
<p>In 2024 cargo vessels must meet the International Maritime Organization's global ballast water discharge standards (IMO D-2) that limit the concentration of living organisms. D-2 focuses on reducing invasion risk by reducing 'community propagule pressure (CPP)', though it does not consider colonization pressure (CP). We modeled risk differences in IMO D-2-compliant discharges (10 ind. m-3) for communities that had inverse patterns of CP and species' individual propagule pressures (IPP). Secondly, we determined the effect on risk of varying CPP and CP. As part of this we tested whether the IMO D-2 standard for zooplankton-sized organisms of <10 individuals m-3 was an optimal choice. Risk was defined as probability of at least one species invading using four risk-release models. Risk differed strongly at the D-2 limit based on community composition. At low CPP (<25 ind. m-3), risk was strongly affected by CP for hyperbolic and linear risk-release models and weakly for exponential and logistic models, while CPP affected only the former two model types. Across a much wider range of CPP values, risk was affected by CP, CPP, and by their interaction for all models. The IMO D-2 standard for zooplankton-sized organisms requires very low CPP and even lower IPPs in mixed-species releases, which will impede successful colonization. Species-abundance theory predicts that discharges meeting the D-2 standard (low CPP) will also have low CP. Much more empirical data is required to determine whether vessels can consistently reduce CP as it lower CPP in order to meet these requirements.</p>
Establishing a new standard of care for calculus using trials with randomized student allocation
<p>Calculus, the study of change in processes and systems, serves as the foundation of many STEM disciplines. Traditional, lecture-based calculus instruction presents a persistent barrier for students seeking STEM degrees, limits access to STEM professions, and blocks their potential to address society's challenges. A large-scale pragmatic trial with randomized student allocation was conducted to compare two calculus instruction styles: active student engagement (treatment condition) versus traditional, lecture-based instruction (control condition). A sample of 811 U.S. university students was studied across 32 sections taught by 19 instructors over three semesters at a large U.S. Hispanic-Serving Institution. Large effect sizes were consistently measured for student learning outcomes in the treatment condition, which demonstrated a new standard for calculus instruction and increased opportunities for completion of STEM degrees.</p>
Education in the Anthropocene: Assessing planetary health science standards in the US
The environmental crises defining the Anthropocene demand ubiquitous mitigation efforts, met with collective support. Yet, disengagement and disbelief surrounding planetary health threats are pervasive, especially in the United States (US). This skepticism may be influenced by inadequate education addressing the scope and urgency of the planetary health crisis. We analyzed current K-12 science standards related to planetary health throughout the US, assessing their quality and potential predictors of variation. While planetary health education varies widely across the US with respect to the presence and depth of terms, most science standards neglected to convey these concepts with a sense of urgency. Furthermore, state/territory political affiliation and primary GDP contributor were each predictive of the quality of planetary health education. We propose that a nation-wide science standard could fully address the urgency of the planetary health crisis and prevent political bias from influencing the breadth and depth of concepts covered.
Results of integrating asynchronous data provenance methods, metadata extraction, and similarity detection into a standards-based RDMS
<p>Related to the thesis: "Asynchronous Tracking and Description of Research Data Changes in Distributed Systems with Interoperable Metadata"</p>
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)
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.