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5,738 results for “Standards”
GC-MS analysis of standard mix for geological biomarkers quantification
<p>Result from GC-MS analysis of standard mix prepared for geological biomarkers quantification.</p>
Supplementary material 1 from: Zermoglio PF, Plos A, Acosta N, Amaya L, Escobar DA, Grattarola F, Mancina CA, Nuñez F, Plata CA, Quintero E, Vargas M (2020) Latin American Plea for Incorporation of Other, Non-English Languages in TDWG Standards Documentation. Biodiversity Information Science and Standards 4: e58973. https://doi.org/10.3897/biss.4.58973
Signatories to the petition for incorporation of other languages to the Biodiversity Information Standards (TDWG) standards and documentation
Impact of COVID-19 lockdown on glycemic control in adults with type 1 diabetes mellitus: information and standardized questions regarding follow-up during lockdown
<p>Aim. To examine the impact of the lockdown caused by COVID-19 pandemic on both the glycemic control and the daily habits of a group of patients with type 1 diabetes mellitus (T1DM) using flash continuous glucose monitoring devices (Flash CGM). </p> <p>Methods. Retrospective analysis based on all the information gathered in virtual consultations from a cohort of 50 adult patients with T1DM with follow-up at our site. We compared their CGM metrics during lockdown with their own previous data before the pandemic occurred, as well as the potential psychological and therapeutic changes.</p> <p>Results. We observed a reduction of the average glucose: 160.26 ± 22.55 mg/dl vs. 150 ± 20.96 mg/dl, p=0.0009, estimated HbA1c: 7.21 ± 0.78% vs. 6.83 ± 0.71%, p=000.5, glucose management indicator (GMI) 7.15 ± 0.57 % vs. 6.88 ± 0.49 %, p=0.0003, and glycemic variability (CV): 40.74 ± 6.66 vs. 36.43 ± 6.09 p<0.0001. Time in range showed an improvement: 57.46 ± 11.85% vs a 65.76 ± 12.09%, p<0.0001, without an increase in percentage of time in hypoglycaemia.</p> <p>Conclusions: COVID-19 lockdown was associated with an improvement in glycemic control in patients with T1DM using CGM.</p>
Solution properties of anionic sesame fatty acid methyl esters sulphonate surfactant and sodium dodecyl sulphate standard
<p>The relationship between specific conductivity and surfactant concentration was used to determine the solution properties of synthesized anionic sesame fatty acid methyl esters sulphonate (SEFAMESO) surfactant and sodium dodecyl sulphate (SDS) standard. The solution properties evaluated included critical micelle concentration (CMC) values, counter-ion binding degree (<em>β</em>) and thermodynamic parameters namely, Gibbs free energy (<i>∆G°</i><sub><sub><i><sub>mic</sub></i></sub></sub>), enthalpy (<i>∆H°</i><sub><sub><i><sub>mic</sub></i></sub></sub>) and entropy (<i>∆S°</i><sub><sub><i><sub>mic</sub></i></sub></sub><i>)</i> of micellization.</p>
Data from: Changes in seed predation along a 2300-m elevational gradient on a tropical mountain in Myanmar: a standardized test with 32 non-native plant species
<p>It has been hypothesized that biotic interactions are stronger towards lower latitudes and elevations. However, results vary among interaction systems and experimental protocols. Our goal was to examine the validity of this prediction by using a standardized method to investigate seed–animal interaction. We assessed removal by animals for 40960 seeds belonging to 32 non-native tree species along an elevation gradient from 600 m to 2910 m on Mount Victoria (Nat Ma Taung), western Myanmar. We analyzed the elevational trends of seed removal at both individual seed level (probability of depot encounter, proportion of seeds removed after encounter and total proportion of seeds removed) and community level (Shannon diversity and species evenness indices). The dry and wet seasons had opposite relationships between seed removal and elevation,<br> i.e. hump-shaped in the dry season and U-shaped in the wet season. Individual plant species displayed almost all possible patterns: U-shaped and hump-shaped, monotonic decrease and increase, and elevation-independent patterns. As a consequence of the hump-shaped seed removal pattern with elevation in the dry season, the diversity and evenness of surviving seeds showed U-shaped patterns. Our study shows that elevational trends in seed–animal interactions do not follow a constant rule, but differ between seasons and among species, suggesting that a one-off survey with few species might give misleading information on overall macroecological patterns. Future studies of trends in biotic interactions along gradients should bear this in mind.</p>
Implementing standardized provider documentation in a tertiary epilepsy clinic: supplemental material
<p class="CxSpFirst"><b>Objective: </b>To incorporate standardized documentation into an epilepsy clinic and use these standardized data to compare patients' perception of epilepsy diagnosis to provider documentation.</p> <p class="CxSpMiddle"><b>Methods: </b>Using quality improvement methodology, we implemented interventions to increase documentation of epilepsy diagnosis, seizure frequency and type from 49.8% to 70% of adult non-employee patients seen by six providers over five months of routine clinical care. The main intervention consisted of an interactive SmartPhrase that mirrored a documentation template developed by the Epilepsy Learning Healthcare System. We assessed the weekly proportion of complete SmartPhrases among eligible patient-encounters with a statistical process control chart. We used a subset of patients with established epilepsy care linked to existing patient-reported survey data to examine the proportion of patient-to-provider agreement on epilepsy diagnosis (yes vs no/unsure). We also examined socio-demographic and clinical characteristics of patients who disagreed vs agreed with provider's documentation of epilepsy diagnosis.</p> <p><b>Results: </b>The median SmartPhrase weekly completion rate was 78%. Established patients disagreed with providers with respect to epilepsy diagnosis in 18.5% of encounters (κ = 0.13), indicating that they did not have, or were unsure if they had, epilepsy despite having a provider-documented epilepsy diagnosis. Patients who disagreed with providers were similar to those who agreed with respect to age, sex, ethnicity, marital status, seizure frequency, type, and other quality of life measures.</p> <p class="CxSpMiddle"><b>Conclusions: </b>This project supports the feasibility of implementing standardized documentation of data relevant to epilepsy care in a tertiary epilepsy clinic and highlights an opportunity for improvement in patient-provider communication.</p>
Data from: Of 11 candidate steroids, corticosterone concentration standardized for mass is the most reliable steroid-biomarker of nutritional stress across different feather types
<p>1. Measuring corticosterone in feathers has become an informative tool in avian ecology, enabling researchers to investigate carry-over effects and responses to environmental variability. Few studies have, however, explored whether corticosterone is the only hormone expressed in feathers, and is the most indicative of environmental stress. Essential questions remain as to how to compare hormone concentrations across different types of feathers and whether preening adds steroids, applied after feather growth. 2. We used liquid chromatography coupled to tandem mass spectrometry to quantify a suite of 11 steroid hormones in back, breast, tail, and primary feathers naturally grown at overlapping time intervals by rhinoceros auklet Cerorhinca monocerata captive-reared fledglings and wild-caught juveniles. The captive-reared birds were raised on either a restricted or control diet. Measured steroids included intermediates in the adrenal steroidogenesis pathway to glucocorticoids and the sex steroids pathway to androgens and estrogens. 3. Corticosterone was detected in the majority of feathers of each type. We also detected cortisone in back feathers, androstenedione in breast feathers, and testosterone in primary feathers. Captive fledglings raised on a restricted diet had higher concentrations of corticosterone in all four feather types than captive fledglings raised on a control diet. Corticosterone concentrations were reliably repeatable across feather types when standardized for feather mass, but not for feather length. Of the seven hormones looked for in uropygial gland secretions, only corticosterone was detected in one out of 23 samples. 4. We conclude that corticosterone is the best feather-steroid biomarker for detection of developmental nutritional stress, as it was the only hormone to manifest a signal of nutritional stress, and that exposure to stress can be compared among different feather types when corticosterone concentrations are standardized by feather mass.</p>
Data from: Monitoring vultures in the 21st century: the need for standardized protocols
This article calls on scientists, managers and organizations focused on vulture conservation to promote and use standardized monitoring programs based on sampling of molted feathers.
Data from: Karyotype analysis of four jewel-beetle species (Coleoptera, Buprestidae) detected by standard staining, C-banding, AgNOR-banding and CMA3/DAPI staining
The male karyotypes of Acmaeodera pilosellae persica Mannerheim, 1837 with 2n=20 (18+neoXY), Sphenoptera scovitzii Faldermann, 1835 (2n=38–46), Dicerca aenea validiuscula Semenov, 1895 – 2n=20 (18+Xyp) and Sphaerobothris aghababiani Volkovitsh et Kalashian, 1998 – 2n=16 (14+Xyp) were studied using conventional staining and different chromosome banding techniques: C-banding, AgNOR-banding, as well as fluorochrome Chromomycin A3 (CMA3) and DAPI. It is shown that C-positive segments are weakly visible in all four species which indicates a small amount of constitutive heterochromatin (CH). There were no signals after DAPI staining and some positive signals were discovered using CMA3 staining demonstrating absence of AT-rich DNA and presence of GC-rich clusters of CH. Nucleolus organizing regions (NORs) were revealed using Ag-NOR technique; argentophilic material mostly coincides with positive signals obtained using CMA3 staining.
Data from: Making soil particle size analysis by laser diffraction compatible with standard soil texture determination methods
The standard sieving, pipette and hydrometer methods for soil particle size analysis (PSA) have three main drawbacks: procedures are tedious, time-consuming, and the results are protocol-dependent. Laser diffraction PSA delivers rapid results using standardized procedures, but so far it has been difficult to reconcile results with those from standard sedimentation methods. The objective of this study was to develop a protocol that would permit direct usage of laser diffraction PSA and render results compatible with current methods. The protocol was developed using standard soil samples from different textural classes. Regression of the laser diffraction PSA against the hydrometer/pipette method yielded coefficients of determination of 0.92/0.9, 0.92/0.94 and 0.99/0.99, and root mean square errors of 0.04/0.05, 0.07/0.06 and 0.05/0.03 for clay, silt and sand, respectively. These statistics are comparable to those obtained by regressing results of the hydrometer against the sieve and pipette methods. A key factor in securing accurate and precise results was limiting the particle size range of the samples by wet sieving the sand fraction. This created representative samples and stable soil dispersed suspensions, allowing accurate estimations of particle size distribution for clay and silt fractions without empirical transformations. Results obtained with the proposed protocol matched those of standard sedimentation analyses for a wide range of soils, encouraging further adoption of laser diffraction for soil PSA.
Data from: A curated and standardized adverse drug event resource to accelerate drug safety research
Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) data, which are the mainstay of traditional drug safety surveillance, are used for hypothesis generation and to validate the newer approaches. The publicly available US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data requires substantial curation before they can be used appropriately, and applying different strategies for data cleaning and normalization can have material impact on analysis results. We provide a curated and standardized version of FAERS removing duplicate case records, applying standardized vocabularies with drug names mapped to RxNorm concepts and outcomes mapped to SNOMED-CT concepts, and pre-computed summary statistics about drug-outcome relationships for general consumption. This publicly available resource, along with the source code, will accelerate drug safety research by reducing the amount of time spent performing data management on the source FAERS reports, improving the quality of the underlying data, and enabling standardized analyses using common vocabularies.
Data from: DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity
Documenting the diversity of marine life is challenging because many species are cryptic, small, and rare, and belong to poorly known groups. New sequencing technologies, especially when combined with standardized sampling, promise to make comprehensive biodiversity assessments and monitoring feasible on a large scale. We used this approach to characterize patterns of diversity on oyster reefs across a range of geographic scales comprising a temperate location [Virginia (VA)] and a subtropical location [Florida (FL)]. Eukaryotic organisms that colonized multilayered settlement surfaces (autonomous reef monitoring structures) over a 6-mo period were identified by cytochrome c oxidase subunit I barcoding (>2-mm mobile organisms) and metabarcoding (sessile and smaller mobile organisms). In a total area of ∼15.64 m2 and volume of ∼0.09 m3, 2,179 operational taxonomic units (OTUs) were recorded from 983,056 sequences. However, only 10.9% could be matched to reference barcodes in public databases, with only 8.2% matching barcodes with both genus and species names. Taxonomic coverage was broad, particularly for animals (22 phyla recorded), but 35.6% of OTUs detected via metabarcoding could not be confidently assigned to a taxonomic group. The smallest size fraction (500 to 106 μm) was the most diverse (more than two-thirds of OTUs). There was little taxonomic overlap between VA and FL, and samples separated by ∼2 m were significantly more similar than samples separated by ∼100 m. Ground-truthing with independent assessments of taxonomic composition indicated that both presence–absence information and relative abundance information are captured by metabarcoding data, suggesting considerable potential for ecological studies and environmental monitoring.
The DIsgust-RelaTed-Images (DIRTI) Database: Validation of a novel standardized set of disgust pictures
<p>Selecting appropriate stimuli is a major challenge of affective research. Although several standardized databases for affective pictures exist, none of them focus on discrete emotions such as disgust. Validated pictures inducing discrete emotions are still limited, and this presents a problem for researchers interested in studying different facets of disgust. In this paper, we introduce the DIsgust-RelaTed-Images (DIRTI) picture set. The set consists of 240 disgust-inducing pictures divided into six categories (<em>food</em>, <em>animals</em>, <em>body products</em>, <em>injuries/infections</em>, <em>death</em>, and <em>hygiene</em>). Additionally, we included 60 matched neutral pictures (10 per category). All pictures were rated by 200 participants on nine-point rating scales measuring <em>disgust</em>, <em>fear</em>, <em>valence,</em> and <em>arousal</em>. The present validation study covered a wide age range (18–75 years) with a balanced number of participants in each decade of life. For each picture, we provide separate ratings on the four scales for men and women. In addition to the original pictures, we also provide a luminance-matched version for experiments that require control of the physical properties of the pictures. The standardized DIRTI picture set allows researchers to chose from a wide set of disgust-inducing pictures and may enhance researchers’ ability to draw comparisons between studies on disgust. (Download DIRTI picture set: http://dx.doi.org/10.5281/zenodo.167037).</p> <p>Picture sets and supplementary material relative to the following publication:</p> <p>Haberkamp, A., Glombiewski, J. A., Schmidt, F., & Barke, A. (submitted). The DIsgust-RelaTed-Images (DIRTI) Database: Validation of a novel standardized set of disgust pictures. <em>Behaviour Research and Therapy</em>.</p> <p>The three files contain the following content.</p> <p><em>DIRTI Database.zip</em> → original picture set</p> <p><em>DIRTI luminance matched images.zip</em> → luminance-matched version of the picture set</p> <p><em>DIRTI Supplementary Material.zip</em> → excel file containing separate worksheets for all ratings (disgust, valence, arousal, fear), for the physical properties of the original and luminance-matched disgust pictures, and for a luminance-matched subset of the original disgust pictures</p>
Data for Standard Codon Substitution Models Overestimate Purifying Selection for Non-Stationary Data
<p>Codon-aligned, filtered alignments for Kaehler et al. (2016) (https://peerj.com/preprints/2218/). Please refer to preprint for preparation details.</p> <p>Data obtained from Ensembl (http://www.ensembl.org/) and antbase (http://antbase.org).</p> <p> </p> <p> </p>
Investigation of metadata standard use by geoscience data repositories
This dataset supports a paper being written about metadata standard use by geoscience data repositories. The study is being done to better understand which metadata standards and keyword vocabularies are prominent within the geoscience data repository landscape. The findings should be useful for NCAR's evaluation of metadata standards within our own systems, as well as by external data repository staff. The guiding questions of the study are as follows: 1. What metadata standards are geoscience data repositories using? 2. What keyword / subject term vocabularies are they using? 3. What interoperability challenges are present in the use of metadata and keyword vocabulary standards within the geoscience repository community?
Wind Effects on Non-Standard Shapes and Structures
<h1>Dataset Description</h1> <p>The data contained in this package aims to act as a open-access database with pressure maps, alongside proper description of the situations considered. In addition, the data within this package aims to contribute to the development and validation of an open-source numerical project with predictive capabilities. The study replicates various complex shapes of membrane structures for testing under Atmospheric Boundary Layer (ABL) flow, Tornadic-like flow, and Downburst-like flow at the Wind Engineering, Energy, and Environment (WindEEE) research facility. Prestressed membrane structures are lightweight structures with a multitude of application areas from small shading devices and façade elements to wide-span roofs covering sport stadia and other leisure facilities. Due to their architectural appeal and the ability to cover large areas with minimal material usage, they represent a highly resource-efficient category of engineering structures.</p> <p> </p> <div> <h2>S1. Wind Field Characterization</h2> </div> <div> <p>The purpose of this specimen was to develop and identify an ABL profile that corresponds to the 1:25 model scale. Tests were performed with various turbulence levels created using a combination of roughness elements, spires, and a trip. For each test, 3-dimensional point-based velocity measurements were taken with TFI Cobra Probes covering a range of heights.</p> <h3>E1. Atmospheric Boundary Layer (ABL) development</h3> <div> <p>Given the model scale, size, and region of installation, ABL flows were tested to develop an accurate simulation for testing the membrane structure models under. Tests were performed with both low-speed and high-speed configurations. Each configuration involved measurements with various turbulence production methods such as roughness elements, spires, trip, and fan speed variation.</p> <p> </p> <div> <h2>S2. M1 (Hyperbolic Paraboloid) Pressure Model</h2> </div> <div> <p>The purpose of this specimen was to test the M1, doubly, curved geometry at a scale of 1:25 under ABL, Downburst, and Tornado winds. The full-scale size of the geometry is 6 x 6 x 5m and at a scale of 1:25 the tested model is 24 x 24 x 20cm where the canopy itself is 8cm tall beginning 12cm from the ground. The canopy is 1cm thick overall (offset 0.5cm above and below the original surface geometry) with a 0.6cm cavity in between for pressure tubing.</p> </div> </div> </div> <h3>E1. ABL Loading</h3> <div> <p>This test involved the M1 model described above subjected to low-speed ABL, and high-speed ABL. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <h3>E2. Downburst Loading</h3> <div> <p>This test involved the M1 model tested under downburst-like flow loading conditions in three configurations where the model is offset from the center of the turntable to achieve 3 r/D values: 0.8, 1.0, and 1.2 (in the -x direction) (offset 0.65m, 1.29m and 1.95m respectively).</p> <h3>E3. Tornado Loading</h3> <div> <p>This test involved the M1 model tested under tornado-like flow loading conditions in two configurations where the model is offset from the center of the turntable by 0cm and 25cm (in the -x direction).</p> </div> </div> <p> </p> <div> <h2>S3. M2 (RidgeValley) Pressure Model</h2> </div> <div> <p>The purpose of this specimen was to test the M2 (RidgeValley) doubly curved geometry at a scale of 1:25 under ABL winds. The equilibrium shape of the M2 (RidgeValley) can be determined via form finding for a pre-stress ratio of 4 (e.g. kN/m) as an isotropic membrane pre-stress to 30 (e.g. kN) in the edge and ridge cables, with fixed support points. The full-scale size of the geometry is 6 x 6 x 5m and at a scale of 1:25 the tested model is 24 x 24 x 20cm where the canopy itself is 8cm tall beginning 12cm from the ground. The canopy is 1cm thick overall (offset 0.5cm above and below the original surface geometry) with a 0.6cm cavity in between for pressure tubing.</p> <h3>E1. ABL Loading</h3> <div> <p>This test involved the model described above subjected to low-speed ABL, and high-speed ABL. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <p> </p> <div> <h2>S4. M3 (Arch-Supported) Pressure Model</h2> </div> <div> <p>The purpose of this specimen was to test the M3 (Arch-Supported) doubly curved geometry at a scale of 1:25 under ABL winds. The equilibrium shape of the M3 can be determined via form finding for a pre-stress ratio of 4 (e.g. kN/m) as an isotropic membrane pre-stress to 30 (e.g. kN) in the edge cables, with fixed line supports at the arches. The arch geometry is defined as a NURBS curve (p=3, CPs at (0,0,0), (0,2,2.65), (0,4,2.65), (0,6,0), knot vector (0,0,0,1,1,1)). The full-scale size of the geometry is 6 x 6 x 5m and at a scale of 1:25 the tested model is 24 x 24 x 20cm where the canopy itself is 8cm tall beginning 12cm from the ground. The canopy is 1cm thick overall (offset 0.5cm above and below the original surface geometry) with a 0.6cm cavity in between for pressure tubing.</p> <h3>E1. ABL Loading</h3> <p>This test involved the model described above subjected to low-speed ABL, and high-speed ABL. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <p> </p> <h2>S5. M4 (Cone) Pressure Model</h2> <div> <p>The purpose of this specimen was to test the M4 (Cone) doubly curved geometry under ABL winds in an isolated, 1x3 row array, and a 3x3 square array configuration. The equilibrium shape of the M4 can be determined via form finding for an isotropic membrane pre-stress, with fixed supports at top and bottom circles. The support circle radii are 0.8m and 4.24m (full-scale). The form found shapes are then intersected at a 6m distance in order to generate straight edges for closed array geometries. The full-scale size of the isolated geometry is 6 x 6 x 5m and at a scale of 1:25 the tested model is 24 x 24 x 20cm where the canopy itself is 8cm tall beginning 12cm from the ground. The canopy is 1cm thick overall (offset 0.5cm above and below the original surface geometry) with a 0.6cm cavity in between for pressure tubing.</p> <h3>E1. ABL Loading Stand Alone</h3> <p>This test involved the model described above subjected to low-speed ABL, and high-speed ABL. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <h3>E2. ABL Loading Row (1 x 3) Array</h3> <div> <p>This test involved the M3 model plus 8 similarly shaped, adjacent dummy models arranged in 3 different configurations subjected to low-speed ABL, and high-speed ABL. The configurations include of a 1x3 line relative to the main pressure model. In the configurations, the pressure model is located: at the center of the line, and at the corner of the line. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <h3>E3. ABL Loading Square (3 x 3) Array</h3> <div> <p>This test involved the M3 model plus 2 similarly shaped, adjacent dummy models arranged in 2 different configurations subjected to low-speed ABL, and high-speed ABL. The configurations include different locations of the main pressure model within a 3x3 square array. In the configurations, the pressure model is located: at the center of the grid, at the corner of the grid, and at the center edge of the grid. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <p> </p> <h2>S6. M5 (Umbrella) Pressure Model</h2> <div> <p>The purpose of this specimen was to test the M5 (Umbrella) doubly curved geometry at a scale of 1:25 under ABL winds. The equilibrium shape of the M5 can be determined via formfinding for a pre-stress ratio of 4 (e.g. kN/m) as an isotropic membrane pre-stress to 30 (e.g. kN) in the edge cables, with fixed line supports at the bottom circle (radius of 0.8m in full scale) and point supports at the top corners. The full-scale size of the geometry is 6 x 6 x 5m and at a scale of 1:25 the tested model is 24 x 24 x 20cm where the canopy itself is 8cm tall beginning 12cm from the ground. The canopy is 1cm thick overall (offset 0.5cm above and below the original surface geometry) with a 0.6cm cavity in between for pressure tubing.</p> <h3>E1. ABL Loading</h3> <p>This test involved the model described above subjected to low-speed ABL, and high-speed ABL. The model was tested every 10° between 0 and 180° plus a 45° angle of attack.</p> <p> </p> </div> </div> </div> </div> </div> </div> </div> </div>
Gold Standard and Annotation Dataset for CO2 Emissions Annotation
<p>This repository contains the results of a research project which provides a benchmark dataset for extracting greenhouse gas emissions from corporate annual and sustainability reports. </p> <p>The zipped <code>datasets</code> file contains two datasets, <code>gold_standard</code> and <code>annotation_dataset</code>(inside the outer zip file there is a password-protected zip file containing the two datasets. To unpack, use the password is provided in the outer zip file).</p> <h3>Data collection</h3> <ol> <li>A Large Language Model (LLM) based pipeline was used to extract the greenhouse gas emissions from the reports (see columns prefixed with <code>llm_</code> in <code>annotation_dataset</code>). The extracted emissions follow the categories Scope 1, 2 (market-based) and 2 (location-based) and 3, as defined in the GHGP protocol (see variables <code>scope</code>).</li> <li>Annotation of the pipeline output was done in 3 phases: first by non-experts (see columns prefixed with <code>non_expert_</code> in <code>annotation_dataset</code>), then by expert groups (columns prefixed with <code>exp_group_</code> in <code>annotation_dataset</code>) in case of disagreement of non-experts and finally in a discussion of all experts (columns prefixed with <code>exp__disc</code> in <code>annotation_dataset</code>) in case of disagreement between expert groups. The annotation guidelines for the <a href="https://zenodo.org/api/records/15124118/draft/files/Non-Expert%20Annotation%20Guidelines.pdf/content">non-experts</a> and <a href="https://zenodo.org/api/records/15124118/draft/files/Expert%20Annotation%20Guidelines.pdf/content">experts</a> are also included in this repository.</li> <li>The annotation results from all three phases are combined to form the final benchmark dataset: <code>gold_standard</code>. Codebooks detailing each variable of each of the two datasets are also provided. More details about the annotation template or the data wrangling scripts can be found in the <a href="https://github.com/soda-lmu/gist-data-descriptor/" target="_blank" rel="noopener">GitHub repository</a>. </li> </ol> <h3>Merging of datasets</h3> <p>Users can match the two datasets (<code>gold_standard</code> and <code>annotation_dataset</code>) using the variable combination of <code>company_name</code>, <code>report_year</code> and <code>merge_id</code> (index column). The <code>merge_id</code> already includes the company name and report year implicitly, but to avoid column duplication in the join operation, it should be included as join variables. For example this is useful when comparing LLM extractions to gold standard data.</p>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - May 2020)
<h2><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></h2><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
Fig. 8 in Efficient New Above-Ground Bucket Traps Produce Comparable Data to that of Standard Transects for the Endangered American Burying Beetle,Nicrophorus AmericanusOlivier (Coleoptera: Silphidae)
Fig. 8. Suggested improvements to above-ground bucket trap design using a wooden rain cover and landing pad to reduce disturbances from scavengers, reduce maintenance, and increase bait-life.
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.