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Text-fig. 4. Mastixia cf. oregonensis (R.A.SCOTT) TIFFNEY et HAGGARD from the London Clay, originally included within the concept of M. cantiensis. a–c: V. 22960(1). a: Transverse fracture, showing c-shaped locule, dorsal infold, and sculptured endocarp, reflected light. b: Transverse digital section from micro-CT scan data. c: Surface view of ribbed endocarp extracted from micro-CT data. d: Transverse fracture, reflected light, V. 22955 (originally illustrated in pl. 25, fig. 4 of Reid and Chandler 1933). e, f: Transverse physical section, V. 22963(2) showing U-shaped locule and longitudinal dorsal infold. g–i reflected light. g: Detail from left of (d). h, i: Detail from right of (f). Scale bars 5 mm in (a–f), 1 mm in (g–i). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 4. Mastixia cf. oregonensis (R.A.SCOTT) TIFFNEY et HAGGARD from the London Clay, originally included within the concept of M. cantiensis. a–c: V. 22960(1). a: Transverse fracture, showing c-shaped locule, dorsal infold, and sculptured endocarp, reflected light. b: Transverse digital section from micro-CT scan data. c: Surface view of ribbed endocarp extracted from micro-CT data. d: Transverse fracture, reflected light, V. 22955 (originally illustrated in pl. 25, fig. 4 of Reid and Chandler 1933). e, f: Transverse physical section, V. 22963(2) showing U-shaped locule and longitudinal dorsal infold. g–i reflected light. g: Detail from left of (d). h, i: Detail from right of (f). Scale bars 5 mm in (a–f), 1 mm in (g–i).
Universal Concepts List of 17 Indo-European Languages
<p>Universal Concepts List (100-words, Swadesh 1971=final) with supposed Proto-Indo-European cognate stems, and their primary, “unmarked” translations in 17 representative old and recent IE languages<br> (This is the summary list of work in progress at detailed lists, and not thoroughly formatted)<br> Important note: This summary is exclusively aimed at glottochronological purposes. This, above all requires finding the most common, popular, everyday, “unmarked” translation for the given test meanings/concepts, which often will neither be the term scientists would choose, nor must be similar with the assumed meaning of the root. Only then the root has to be found, only in order to decide, which translations in the above sense are cognate or not. In most cases this aim does not necessarily require detailed notions of the grammar. Note further that pure loan pronunciations are glottochronologically not counted as replacements.</p>
Public Understanding of Common Data Concepts
<p><span>This dataset reports on a survey on awareness amongst the European public of common data concepts, terms and principles. </span></p> <p><span>Elements of this survey were utilised in the following publication - </span></p> <p><span>O’Grady, M., Mangina, E. Citizen scientists—practices, observations, and experience. <em>Humanit Soc Sci Commun</em> <strong>11</strong>, 469 (2024). https://doi.org/10.1057/s41599-024-02966-x</span></p>
CONCEPT-COSTS. Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database)
<p><strong>Technical notes and documentation</strong></p> <p>The Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database) is a database of Spanish healthcare unit costs estimated from different national sources.</p> <p>Version 3.0 of the CONCEPT-COSTS Database contains costs estimates (expresed in EUR 2024) for a core set of service items commonly used in the chronic health problems evaluated in CONCEPT Project.</p> <p>It is a living document planned to be regularly updated and expanded in terms of the covered service over time.</p> <p><strong>Aims of CONCEPT-COSTS project:</strong></p> <p>CONCEPT-COSTS is part of the coordinated CONCEPT Project, which comprises four subprojects whose objective is to analyse the effectiveness and efficiency of care pathways (CP) in three chronic health problems of high prevalence and socioeconomic impact, which are diabetes mellitus type 2, breast cancer and ischemic stroke. As a common denominator, CONCEPT shares the innovative perspective of focusing its analysis on CP as a key determinant of healthcare adequacy, adherence to treatment, health outcomes and economic consequences. CONCEPT-COSTS' first objective is to complement the results produced by each CONCEPT clinical cohort, with a broad proposal of economic analyses based on real-world data (RWD), including incurred costs, avoidable costs and efficiency evaluation of identified CP. These results will be used to inform the clinical and management decisions about those CP to be promoted or avoided. As a second objective, CONCEPT-COSTS will identify the ethodological and logistical challenges faced by economic evaluations based on RWD, to develop a framework that will include recommendations for improvements related to feasibility, validity and transferability of results.</p> <p>Files included in this publication:</p> <ul> <li>CONCEPT_COSTS_Database_v3.csv</li> <li>CONCEPT_COSTS_Database_v3.html</li> <li>Readme_v3.doc</li> </ul> <p><strong>What's new</strong></p> <ul> <li>Costs updated to 2024</li> <li>Some sources updated</li> </ul>
Concept list of Vietnamese based on the Intercontinental Dictionary Series (IDS)
<div> <p>The list includes 1310 concepts for Vietnamese. The concepts are based on the items provided in the Intercontinental Dictionary Series (<a href="https://ids.clld.org/">IDS</a>, Key & Comrie 2023). </p> </div>
CONCEPT-DIABETES DATA MODEL TO ANALYSE HEALTHCARE PATHWAYS OF TYPE 2 DIABETES
<p><strong>Technical notes and documentation on the common data model of the project CONCEPT-DM2. </strong></p> <p>This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes, version v0.2.0.</p> <p><strong>Aims of the CONCEPT-DM2 project: </strong></p> <p>General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.</p> <p>Main specific aims:</p> <ul> <li>To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations</li> <li>To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines</li> <li>To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.</li> <li>To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.</li> </ul> <p><strong>Study Design: </strong>It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.</p> <p><strong>Cohort definition: </strong>All patients with code of Type 2 Diabetes in the clinical health records</p> <ul> <li>Inclusion criteria: patients that, at 2017-01-01 or during the follow-up from 2017-01-01 to 2022-12-31 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)</li> <li>Exclusion criteria: <ul> <li>patients with no contact with the health system from 2017-01-01 to 2022-12-31</li> <li>patients that had a T1D (DM1) code opened after the T2D code during the follow-up.</li> </ul> </li> <li>Study period. From 2017-01-01 to 2022-12-31</li> </ul> <p><strong>Files included in this publication: </strong></p> <ul> <li>Datamodel_CONCEPT_DM2_diagram_v0.2.0.jpg</li> <li>Common data model specification (Datamodel_CONCEPT_DM2_v.0.2.0.xlsx)</li> <li>Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data_v0.2.0) <ul> <li>sample_data1_dm_patient.csv</li> <li>sample_data2_dm_param.csv</li> <li>sample_data3_dm_patient.csv</li> <li>sample_data4_dm_param.csv</li> <li>sample_data5_dm_patient.csv</li> <li>sample_data6_dm_param.csv</li> <li>sample_data7_dm_param.csv</li> <li>sample_data8_dm_param.csv</li> </ul> </li> <li>Datamodel_CONCEPT_DM2_explanation_v0.2.0.pptx</li> </ul> <p><strong>CHANGE-LOG from version v0.1.0 to v0.2.0.</strong></p> <p>The main changes are the following:</p> <ul> <li>Missing data is now identified leaving the field empty</li> <li> <p>All ICD diagnosis given in the Datamodel refer to the root code, so that all codes and subcodes that start with the given codes need to be considered. For instance, if in the data model appears I21, then all codes I21.x should be included.</p> </li> <li> <p>All admissions registered in the CMBD will be included to facilitate the extraction procedure (annex 8 disappears).</p> </li> <li> <p>Three diagnosis codes and three procedure codes are now included in table dm_cmbd.</p> </li> </ul> <p><strong>CHANGE-LOG from version v0.2.0 to v0.3.0.</strong></p> <p>The main changes are the following:</p> <ul> <li>Variable 'copayment' (annex 2) change: cod 002.01 =>0; cod 002.02 =>1</li> <li>Variable 'visit_service' (annex 8) change: APR refers to Primary Care and APA refers to Pathological Anatomy</li> <li>Variable 'filglom' (annex 4) change: non numerical values compatible with '> 60' => 999</li> </ul>
FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept - Input Data
<p>This repository contains driving data used by training and evaluation of FLAME in the "FLAME: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept" paper. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over Brazil. </p> <div>Not all variables were used in the final analysis<br> <table> <tbody> <tr> <td><strong> NetCDF File</strong></td> <td> <p><strong> Variable</strong></p> </td> <td> <p><strong>Used/not Used</strong></p> </td> <td> <p><strong>Source/Reference</strong></p> </td> </tr> <tr> <td> <p>burned_area.nc</p> </td> <td> <p>Burned area</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018)</td> </tr> <tr> <td> <p>burned_area_nat_veg.nc</p> </td> <td> <p>Burned area in natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p>burned_area_non_nat_veg.nc</p> </td> <td> <p>Burned area in non natural vegetation</p> </td> <td> <p>As training data</p> </td> <td>MCD64A1/ Giglio et al. (2018) and Mapbiomas, 2022</td> </tr> <tr> <td> <p> tas_max.nc</p> </td> <td> <p> Maximum Temperature</p> </td> <td> <p>Used</p> </td> <td><br><br> <p>ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>precip.nc</p> </td> <td> <p>Precipitation</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>vpd.nc</p> </td> <td> <p>Vapor pressure deficit</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>rhumid.nc</p> </td> <td> <p> Relative Humidity </p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td><br>consec_dry_days.nc</td> <td><br> <p>Consecutive number of dry days </p> </td> <td>Not Used</td> </tr> <tr> <td> <p>soilM.nc</p> </td> <td> <p>Soil Moisture</p> </td> <td> <p>Not Used</p> </td> <td> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>lightn.nc </p> </td> <td> <p> Lightning</p> </td> <td> <p>Not Used</p> </td> <td><br> <p> ISIMIP3a</p> <p>FRIELER et al. (2023)</p> </td> </tr> <tr> <td> <p>popDen.nc</p> </td> <td> <p> Population density</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>road_density.nc</p> </td> <td> <p>Road density</p> </td> <td>Used</td> <td> <p> GRIP global</p> <p>(MEIJER et al., 2018)</p> </td> </tr> <tr> <td> <p>cveg.nc</p> </td> <td> <p>Vegetation carbon</p> </td> <td> <p>Not Used</p> </td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>csoil.nc</p> </td> <td> <p>Carbon in dead vegetation</p> </td> <td>Used</td> <td><br> <p>JULES-ES</p> </td> </tr> <tr> <td> <p>forest.nc</p> </td> <td> <p> Forest</p> </td> <td> <p>Used</p> </td> <td><br><br><br> <p> MAPBIOMAS, 2022</p> </td> </tr> <tr> <td> <p>grassland.nc</p> </td> <td> <p> Grassland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>savanna.nc</p> </td> <td> <p> Savanna</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>cropland.nc</p> </td> <td> <p> Cropland</p> </td> <td> <p>Not Used</p> </td> </tr> <tr> <td> <p>pasture.nc</p> </td> <td> <p> Pasture</p> </td> <td> <p>Used</p> </td> </tr> <tr> <td> <p>np.nc</p> </td> <td> <p>Number of patches </p> </td> <td> <p>Not Used</p> </td> <td><br><br> <p>Calculated from MAPBIOMAS,<br>2022</p> <br><br></td> </tr> <tr> <td> <p>ed.nc </p> </td> <td>Edge density</td> <td>Used</td> </tr> </tbody> </table> </div> <p> </p>
Figure 2 in A new species of Planipapillus (Onychophora: Peripatopsidae) that defies the original concept of its genus
Figure 2. Planipapillus absonus sp. nov.: male reproductive tract and associated glands, composite image drawn from holotype and paratype male. aag, anterior accessory gland; cg, crural gland; pa, posterior accessory gland; sv, seminal vesicle; t, testis; vd, vas deferens; ve, vas efferens.
Figure 1 in A new species of Planipapillus (Onychophora: Peripatopsidae) that defies the original concept of its genus
Figure 1. Planipapillus absonus sp. nov.: a, dorsolateral view in natura, ©David Paul; b, dorsal view; c, ventral view; d, anteriodorsal view; e, primary papilla; f, secondary papilla; g, crural papilla, leg 6; h, leg 5; i, nephridiopore, leg 5. b–d, stacked stereomicrographs, holotype ♂ 0.84 mm HWE (MV K7279), scale bars = 1 mm; e–i, scanning electron micrographs, paratypes MV K7280, scale bars = 30 µm; e, f, h, I, ♀ 1.04 mm HWE; g, ♂ 0.77 mm HWE.
Dataset: Venus Concept Inc. (VERO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Treace Medical Concepts, Inc. (TMCI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Neo-Concept International Group Holdings Limited (NCI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset of KO journal paper: Semantic analysis of archival concepts in CIDOC-CRM and in RiC-CM and RiC-O
<p>Semantic analysis of archival concepts (class, relations, atributtes, and relation attributes) presents in Records in Context family (conceptual model and ontology) and its possible equivalents in CIDOC-CRM.</p>
Fig. 5 in A New Species from Mountain Forest Soils in Japan: Porosia paracarinata sp. nov., and Taxonomic Concept of the Genus Porosia Jung, 1942
Fig. 5. Schematic demonstration of relationship of Porosia paracarinata sp. nov. and two closely related species, such as Nebela carinata and Porosia bigibbosa, based on morphological characters (average morphometric characteristics of species based on literature data and personal observations*).
Fig. 4 in A New Species from Mountain Forest Soils in Japan: Porosia paracarinata sp. nov., and Taxonomic Concept of the Genus Porosia Jung, 1942
Fig. 4. Keel variation in Porosia paracarinata sp. nov. A – SEM image of P. paracarinata with narrow keel; B – LM images of P. paracarinata with narrow; C – very narrow and D – almost disappearing keel. Scale bars: 50 μm.
Fig. 3 in A New Species from Mountain Forest Soils in Japan: Porosia paracarinata sp. nov., and Taxonomic Concept of the Genus Porosia Jung, 1942
Fig. 3. Porosia paracarinata sp. nov. A – SEM image of P. paracarinata with wider keel; B – LM image of P. paracarinata with wider keel; C – P. paracarinata in profile view; D – schematic draw of P. paracarinata. Scale bars: 50 μm.
Fig. 1 in A New Species from Mountain Forest Soils in Japan: Porosia paracarinata sp. nov., and Taxonomic Concept of the Genus Porosia Jung, 1942
Fig. 1. Map of Japan, with indication of Mount Tateyama (in the first figure) and with the detailed locality called Bijodaira (in the second figure) where Porosia paracarinata was found (figure modi- fied from http://klettertechnik-bauservice.de/images/pulau-honshu).
Fig. 2 in A New Species from Mountain Forest Soils in Japan: Porosia paracarinata sp. nov., and Taxonomic Concept of the Genus Porosia Jung, 1942
Fig. 2. Outline of Porosia paracarinata: ventral (left) and lateral (right) views. 1–7 – measured characters of the test (see Table 1). Scale bar: 50 µm.
Fig. 6 in Evidence Supporting the Concept of a Regionalized Distribution of Testate Amoebae in the Arctic
Fig. 6. Distribution map of Centropyxis pontigulasiformis and C. gasparella in the Arctic and subarctic Norway. Remark that C. pontigulasiformis has recently been observed in the Netherlands and in Austria.
Fig. 4 in Evidence Supporting the Concept of a Regionalized Distribution of Testate Amoebae in the Arctic
Fig. 4. Map of the Arctic (polar view) with climatic zonations according to Prik (1981) and the location of the different clustergroup members. The delination of the southern limit of the Arctic is given by the arrow with the number 1. The arrow 2 gives the southern limit of the High Arctic, arrow 3 the southern limit of the Polar Desert zone (according to Aleksandrova 1980). Sv-Gr stands for the suggested SvalbardGreenlandic protozoological region, Si stands for the Siberian.
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OpenNeuro
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