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The SSHOC Multilingual Data Stewardship Terminology is a multilingual terminology that collects terms specific to the domain of Data Stewardship, as well as their definitions. A list of domain-specific terms was automatically extracted from a corpus pertaining to the domain of Data Stewardship and Curation, validated by domain experts, assigned a definition, and linked to other existing terminologies (Loterre Open Science Thesaurus, terms4FAIRskills, Linked Open Vocabularies, ISO terms and definitions). Each term-definition pair was then automatically translated into multiple languages (Dutch, French, German, Greek, Italian, Slovenian) by employing Deep-L. The Multilingual Data Stewardship Terminology thus consists of 210 concepts available in Dutch, French, German, Greek, Italian, Slovenian. This resource was created within the frame of the SSHOC (Social Sciences and Humanities Open Cloud) project (H2020-INFRAEOSC-2018-2-823782). It is the result of the work of Task 3.1.2 "extraction of terminology from technical documentation about standards and interoperability", as described in D3.9, carried out jointly by ILC-CNR and CLARIN ERIC.

Overview of the activities of the Aviation Laboratory under German rule between 1940 and 1945. Although officially driven by scientific motivation only, the laboratory worked closely with German aviation research and contributed to the German war effort.

Wikipedia is the largest encyclopedia ever assembled with the vision of enabling every human being to freely share in the sum of all knowledge. Wikipedia currently has a total of more than six million articles and over 17 billion words in its English edition. Unfortunately, millions of people cannot access this resource because it���s not available in their language. For instance, at the moment there are only 218 Tigrinya Wikipedia and 15,018 Amharic Wikipedia articles. In this project, we investigate the problem of translating Wikipedia articles from a high resource language into low resource languages using human-in-the-loop MT systems. In particular, we investigate different approaches to translate a sample of English Wikipedia articles into Tigrinya and Amharic. Currently, this repository contains 100k English Wikipeida abstracts translated using Lesan (https://lesan.ai) into Amharic and Tigrinya. Structure of data directory: data ��������� human ��������� mt ��������� google ��������� lesan ��� ��������� am.txt ��� ��������� en.txt ��� ��������� ti.txt ��������� microsoft

Penelitian ini menggunakan perspektif new historisme dengan tujuan untuk mengidentifikasi sejarah dan budaya Indonesia dalam novel Hati Sinden karya Dwi Rahyuningsih. Data dalam penelitian berupa teks, baik berupa kata, kalimat, atau wacana yang sesuai dengan rumusan masalah. Adapun sumber data dalam penelitian ini yaitu novel Hati Sinden karya Dwi Rahyuningsih yang diterbitkan oleh Diva Press pada tahun 2011. Instrumen dalam penelitian ini yaitu penulis yang berorientasi pada penelitian tentang sejarah dan budaya Indonesia dalam novel Hati Sinden karya Dwi Rahyuningsih. Teknik pengumpulan data dalam penelitian ini dilakukan melalui dua tahapan yaitu baca dan catat. Teknik analisis data dilakukan melalui tiga tahapan yaitu kategorisasi, deskripsi, dan penyajian data. Hasil penelitian menunjukkan bahwa pertama, dalam novel Hati Sinden karya Dwi Rahyuningsih terdapat representasi sejarah Indonesia yaitu tentang Partai Komunis Indonesia. Kedua, representasi unsur budaya dalam novel Hati Sinden karya Dwi Rahyuningsih yang terdiri dari unsur budaya pada sistem kesenian, sistem mata pencaharian hidup, dan sistem kepercayaan.

Variable Description Type Type of sample (calibration = calibration coin for Delta Innov-X Analyzer; standard = NIST geological standard; geological = geological sample from lithic raw material source; artifact = archaeological specimen) Replicate Replicate measurement (Yes or No) Source Geological source (delta = Delta Innov-x Analyzer calibration coin; nist = National Institute of Standards and Technology (NIST) geological standard; NS = Naibor Soit; NH = Naisuisui Hill; OL = Oldonye Okule; LD = Lemagarut drainage; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin) Outcrop Individual outcrop within geological source (NSM = Naibor Soit Main Hill; NSMH = Naibor Soit Manyata Hill; NSSO = Naibor Soit Southern Outlier; NH = Naisuisui Hill; LD1 = Lemagarut Drainage 1; LD2 = Lemagarut Drainage 2; BKE = BK East; SS = Shifting Sand; KG = Kelogi Hills; EN = Engelosin; NA = Not applicable) Specimen Individual find or sample number Material Raw material type (QTZ = "Quartz-rich"; GN = Gneiss; FGV = Fine-grained volcanic) Element concentration estimate Reported for each element (e.g., P, Cl, Ca; empty cells are "non-detect") Analytical error Reported for each element (e.g., P +/-, Cl +/-, Ca +/-; no error reported for "non-detect" elements) The published analysis focused only on granulite specimens (n = 186) and, more specifically, on six elements (Fe, Ti, Zr, K, Sr, and Y) that had detection rates >75% in the granulite specimens (that is, these elements were detected in more than 75% of the granulite specimens). These elements were used in the predictive models from the published analysis. Two of the 186 granulite specimens were missing values for five out of the six elements and were therefore not included in the statistical analyses. Of the remaining 184 specimens, 55 had missing data for one element, and two of those 55 had missing data for two elements. These missing values were treated as censored data (that is, the element is present but could not be measured precisely enough for the instrument to report a value). These missing values were interpolated in one of two ways. For those specimens subjected to replicate pXRF runs (n = 7), the missing value was replaced with the mean value of the replicates. The missing values for the remaining specimens (n = 48) were replaced with the mean of the four closest (as determined in two-dimensional space) specimens with measured (rather than interpolated) values. The main data file does not include these interpolated values. Should analysts choose to use them, interpolated values can be found in the additional .csv file. The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant. A total of 244 rock specimens (aka "geological specimens") were collected from eight primary (six granulite outcrops, one gneiss outcrop, one phonolite outcrop) and one secondary (a seasonal drainage containing basalt blocks) lithic raw material sources in the Olduvai Basin. Rock specimens were flaked directly from the sources with a rockhammer. Only granulite specimens with visually quartz-rich compositions were selected. Five quartz-rich metamorphic artifacts (aka "archaeological specimens") from BK East, a ca. 1.5 million-year-old site on the south wall of the side gorge in Olduvai Gorge, were also included. Portable XRF (pXRF) analyses were conducted with an Innov-X Delta Classic Environmental Analyzer equipped with a 4W Au anode X-ray tube and a Si-PIN diode detector. All analyses were performed while the instrument was docked into a stable, hands-free test stand. An unweathered, non-cortical surface free of sediment matrix was placed over, and completely covered, the detector window. Each specimen was measured for 360 seconds using all three of the instrument's beams (120 seconds/beam). After an initial energy scale calibration test with a factory issued metal coin of known composition, the following protocol was observed: (1) a powdered sample of Standard Reference Material (SRM) 2702 with elemental concentrations certified by NIST was measured; (2) four geological/archaeological specimens were then measured; (3) the fifth geological/archaeological specimen in a series was measured five times (that is, five consecutive 360-second cycles) without being moved or reoriented; (4) after the fifth geological/archaeological specimen was measured, the SRM 2702 sample was measured once again, which initiated the next series of measurements. Element concentrations were derived with the Compton Normalization correction model and the factory-set “Soil Environmental” calibration.

{"references": ["Barnes, R., S. Gupta, C. Traxler, T. Ortner, A. Bauer, G. Hesina, G. Paar, et al. 2018. \"Geological Analysis of Martian Rover-Derived Digital Outcrop Models Using the 3-D Visualization Tool, Planetary Robotics 3-D Viewer\u2014PRo3D.\" Earth and Space Science 5: 1\u201323. https://doi.org/10.1002/2018EA000374.", "Fraeman, A. A., B. L. Ehlmann, R. E. Arvidson, C. S. Edwards, J. P. Grotzinger, R. E. Milliken, D. P. Quinn, and M. S. Rice. 2016. \"The Stratigraphy and Evolution of Lower Mount Sharp from Spectral, Morphological, and Thermophysical Orbital Data Sets.\" Journal of Geophysical Research E: Planets 121 (9): 1713\u201336. https://doi.org/10.1002/2016JE005095.Received.", "Hughes, M. N. 2021. \"Landscape Evolution at Endeavour and Gale Craters on Mars, and How Terrain Characteristics Correlate with Mineralogy on Lower Mount Sharp, Gale Crater.\" https://doi.org/10.7936/c6se-5895.", "Milliken, R. E., J. P. Grotzinger, and B. J. Thomson. 2010. \"Paleoclimate of Mars as Captured by the Stratigraphic Record in Gale Crater.\" Geophysical Research Letters 37 (4): 1\u20136. https://doi.org/10.1029/2009GL041870.", "Persaud, D. M. (2021). Co-registered U. Arizona HiRISE DTM and ORI over Sakarya Vallis, Gale Crater, Mars [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5808371", "Persaud, D. M. (2021). Multi-Resolution Basemap of Northwest Aeolis Mons, Gale Crater, Mars [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5808381", "Thomson, B. J., N. T. Bridges, R. E. Milliken, A. M. Baldridge, S. J. Hook, J. K. Crowley, G. M. Marion, C. R. de Souza Filho, A. J. Brown, and C. M. Weitz. 2011. \"Constraints on the Origin and Evolution of the Layered Mound in Gale Crater, Mars Using Mars Reconnaissance Orbiter Data.\" Icarus 214 (2): 413\u201332. https://doi.org/10.1016/j.icarus.2011.05.002."]} This dataset comprises the unit map derived from 3D analysis of Sakarya Vallis in Gale crater, Mars. The units are named Package 1–7. These packages have been extrapolated from morpho-stratigraphic analysis of a HiRISE scene in PRo3D. They have been further extrapolated using underlying image data. Included here is a shapefile representing the marker bed (Milliken et al. 2010) in Gale crater. The "CDD" refers to the Central Debris Deposit, identified by Hughes (2021). The structural data represents dip measurements along the boundaries of these packages within the feature; the "sub-package" data represent layering within the packages. For more on how dip is calculated in PRo3D, see https://pro3d.space/. Finally, the profiles mark the locations where topographic profiles were extracted for constructing cross-sections, as discussed in the thesis Persaud (2022). These data are intended to be displayed with the HiRISE ORI (https://doi.org/10.5281/zenodo.5808371) and CTX ORI mosaic (https://doi.org/10.5281/zenodo.5808357) over Sakarya Vallis, and over the basemap over the northwest of Aeolis Mons (https://doi.org/10.5281/zenodo.5808381). Format: SHP, SHX, DBF, PRJ, QPJ Projection: Equidistant cylindrical Datum: Spheroid (r = 3396.190 km) N.B. the PROJ4 format of the project is "+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396190 +b=3396190 +units=m +no_defs" The first author is now at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California. Contact: divya.m.persaud@jpl.nasa.gov Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA. Government sponsorship acknowledged.

Description of the kittajafr-v2-2.0.1; This guide presents KittajaFr-v2. It is a set of free resources and tools frequently used in machine translation, to build a Transformer model to translate from Japanese into French. The corpus contains 2,5M aligned segments. Score BLEU is between 13,5 et 11,5.; Ce guide présente le kit kittajafr-v2baseline. Il s'agit d'un ensemble de ressources et d'outils libres couramment utilisés en traduction automatique, permettant d'entraîner un modèle Transformer de traduction neuronale du japonais vers le français. Le corpus contient 2,5M de segments alignés de qualité variable. La segmentation (sentencePiece) du vocabulaire est fixée à 16K mots pour chaque langue et les embeddings sont de 256. Les scores BLEU se situent vers 13,5 et 11,5 (on dispose aussi des scores chrF). Le kittjafr-v2 fait suite à la version v1, qui permettait de construire un très petit modèle (score BLEU 3 et 7).

As the price of copra increased in the global market, coconut cultivation in Buol received great attention in the early 20th century. During the New Order, the government paid attention again to people's plantations, especially coconuts. Coconut cultivation was promoted in several areas, including Buol. This paper scrutinizes the cultivation process and development of copra production in Buol. The commodities were closely related because the cultivation process greatly affected the production of copra. This study used the historical method to suggest that the people of Buol were cultivating coconut because of its great benefits, including how easy it was to be processed. In addition, other findings of this study indicate that the price of copra at the plantation level was determined by interconnected factors such as the quality of copra, the marketing network, and the absence of koperasi (economic enterprise) as a market counterweight. The study also found that although they were seen as economic symbols for the Buolese, these two commodities did not contribute significantly to the welfare of Buolese, particularly the small farmers who do not have vast cultivating lands.