cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/inscriptifact-ouc15244297
title: Kirta Legend

description: [Text Description:] The story recounts the loss and restoration of Kirta/Keret's family, and the consequences of his unfulfilled vow to Athirat ❧ [Text or Publication Number Note:] KTU 1.16; CAT 1.16; KTU2 1.16; CTU 1.16; CTA 16; RS 3.325,3.342,3.408 ❧ [Excavation Description:] Excavations at Ras Shamra 1931. Found in Library of the High Priest.3.325 Room 7/8, pt.335, dep.1.30, 3.342 Room 7, pt.338, 343, 341, dep.0.30-0.40, 3.408, pt.368, dep.0.20 on Acropolis, excavated by Claude F.A. Schaeffer, Mission Archeologique Francaise. ❧ [Medium:] Clay ❧ [Script Note:] Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet 23 cm. by 17.5 cm., fragments ❧ [Physical Object Note:] Ocher clay, six columns, incomplete.
DOI: 10.25549/inscriptifact-ouc15257246
title: Thutmosis III and Sety I

description: [Text Description:] Cartouche: mn-xpr ra, Throne name for Thutmosis III. Outside cartouche: mn-mAat-ra, stp-n-ra - Throne name for Sety I w/epithet 'Chosen of Ra' ❧ [Excavation Description:] Gifted to the Los Angeles County Museum of Art by Ruth Greenberg ❧ [Medium:] Stone ❧ [Script Note:] Hieroglyphs ❧ [Physical Object Description:] Steatite Scarab w/dark green glaze - colorized in modern times; bored lengthwise; wear on prothorax; legs deeply incised ❧ [Physical Object Note:] L: 1.82cm; H: .79cm; W: 1.35cm
DOI: 10.13130/rd_unimi/qa4bro/6tzeh1
title: 250408_NONOPT1_TensileTest_017-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_017.tif
DOI: 10.13130/rd_unimi/qa4bro/7nzqmd
title: 250408_NONOPT1_TensileTest_018-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_018.tif
DOI: 10.21950/s2irke
title: NLP4RARE-NER

description: El dataset contiene el código de la implementación y experimentación de los enfoques propuestos en el artículo: 
Isabel Segura Bedmar, David Camino Perdones, Sara Guerrero Aspizua. (2022). Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. BMC Bioinformatics 23, 263 . ISSN: 1471-2105. DOI: https://doi.org/10.1186/s12859-022-04810-y
&lt;/br&gt;
Estos enfoques basados en técnicas de PLN y aprendizaje profundo están dirigido al reconocimiento automático de enfermedades raras y sus manifestaciones clínicas en textos biomédicos. 
&lt;/br&gt;
Gracias a este código (también disponible en el repositorio público) https://github.com/isegura/NLP4RARE-NER
description: Descripción del proyecto: El proyecto NLP4RARE-CM-UC3M, financiado por la Comunidad de Madrid y la UC3M, en la Convocatoria del Programa de Apoyo a la Realización de Proyectos Interdisciplinares de I+D para jóvenes investigadores de la UC3M 2019-2020. El proyecto tiene como objetivo aplicar técnicas de Procesamiento de Lenguaje Natural (PLN) para analizar la literatura biomédica con el fin de aumentar el conocimiento relativo a las enfermedades raras y desvelar relaciones complejas presentes en estas enfermedades. El tratamiento y diagnóstico de estas enfermedades es un reto para nuestra sociedad actual. Gracias a la investigación biomédica, se ha generado una gran cantidad de conocimiento sobre enfermedades raras en los últimos años. Sin embargo, debido al elevado coste de los análisis moleculares y la existencia de una bibliografía reducida, en ocasiones inaccesible o dispersa, dificulta el avance en el diagnóstico y tratamiento de estas patologías. Frecuentemente, los pacientes afectos, a pesar de que sus condiciones son mortales o crónicamente incapacitantes, carecen de tratamiento. Aunque las bases genéticas hayan sido dilucidadas, en muchos casos la pérdida de función de los genes causantes no explica la complejidad fenotípica observada. Estas enfermedades comparten manifestaciones clínicas y predisposición al cáncer, a pesar de tener diferentes bases genéticas. Gracias a la identificación automática de estas manifestaciones, en el proyecto, se tratará de identificar biomarcadores precisos, dianas terapéuticas de estas patologías, así como facilitar su diagnóstico a clínicos no especialistas. 

Metolodogía: Basada en el ajuste de modelos Transformers y de aprendizaje profundo. Más información en https://huggingface.co/ y https://ocw.uc3m.es/course/view.php?id=316
description: Los ficheros son Jupyter notebook con las implementaciones en Pytorch de modelos transformadores ajustados a la tareas de reconocimiento de enfermedades raras y síntomas.
DOI: 10.13130/rd_unimi/qa4bro/arfujj
title: 250408_NONOPT1_TensileTest_019-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_019.tif
DOI: 10.21950/edtwr7
title: Dataset for the automatic detection of traffic lights, street crossings and urban roundabouts based on GPS traces while driving

description: A specifically designed dataset generated using a driving path in Madrid which has been traveled 55 times using 3 different car models. The journey includes two urban areas and a connecting motorway. The overall length is 8.1 km and includes several road elements of interest (traffic lights, street crossings and roundabouts). The dataset has been generated from one driver driving the 3 different vehicles without being aware of the purpose of the experiment. He was asked to drive normally while an Android mobile device recorded the GPS traces.&lt;/br&gt;
Vehicles used for the data gathering.&lt;/br&gt;
Model and Times used:&lt;/br&gt;
Peugeot 206, 7&lt;/br&gt;
Citroen Xsara Picasso, 28&lt;/br&gt;
Opel Zafira, 20&lt;/br&gt;
description: The dataset could help reproducing the results in the companiog paper and to foster new research.

Description of the project: The dataset has been gathered to valitade the research presented in Munoz-Organero, M., Ruiz-Blaquez, R., Sánchez-Fernández, L. (2018). “Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving”. Computers, Environment and Urban Systems, 68, pp. 1–8, https://doi.org/10.1016/j.compenvurbsys.2017.09.005

This paper combines outlier detection with deep learning based pattern recognition and classification from GPS derived data while driving. Only GPS data is used in order to minimize the deployment requirements and therefore facilitate the use of the proposed algorithm in crowdsensing scenarios. Speed and acceleration data series are computed form the GPS data. Window-segments of a constant duration around locations classified as outliers while driving are used to train a DBN (Deep Believe Network) followed by a classifier in order to detect 3 particular road elements: traffic lights, street crossings (where two roads cross each other with signs only) and roundabouts. The algorithm is applied to 16.97 h (61,076 s.) of unlabeled driving data in order to assess the detection performance.

Methodology: The GPS sensor in a mobile device is used to obtain both the vehicle's location and the estimated driving speed. The speed can be derived from the distance traveled per time unit. The distance traveled between two points 1 and 2 can be calculated form the GPS coordinates
description: The implemented model is described in the companion paper
DOI: 10.25549/inscriptifact-ouc15244288
title: Kirta Legend

description: [Text Description:] The story recounts the loss and restoration of Kirta/Keret's family, and the consequences of his unfulfilled vow to Athirat ❧ [Text or Publication Number Note:] KTU 1.16; CAT 1.16; KTU2 1.16; CTU 1.16; CTA 16; RS 3.325,3.342,3.408 ❧ [Excavation Description:] Excavations at Ras Shamra 1931. Found in Library of the High Priest.3.325 Room 7/8, pt.335, dep.1.30, 3.342 Room 7, pt.338, 343, 341, dep.0.30-0.40, 3.408, pt.368, dep.0.20 on Acropolis, excavated by Claude F.A. Schaeffer, Mission Archeologique Francaise. ❧ [Medium:] Clay ❧ [Script Note:] Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet 23 cm. by 17.5 cm., fragments ❧ [Physical Object Note:] Ocher clay, six columns, incomplete.
DOI: 10.25549/inscriptifact-ouc15257245
title: Ramesses II

description: [Text Description:] Cartouche: wsr-mAat-ra, stp-n-ra - Throne name of Ramesses II w/epithet 'Chosen of Ra'. Outside inscription: mrj Thoth, nb tAwy - Beloved of Thoth, Lord of the two Lands ❧ [Excavation Description:] Gifted to the Los Angeles County Museum of Art by Ruth Greenberg ❧ [Medium:] Stone ❧ [Script Note:] Hieroglyphs ❧ [Physical Object Description:] Steatite Scarab w/dark green glaze ❧ colorized in modern times; bored lengthwise; legs deeply incised w/hair marks ❧ [Physical Object Note:] L: 2.16cm; H: .89cm; W: 1.50cm
DOI: 10.13130/rd_unimi/qa4bro/8gkir7
title: 250408_NONOPT1_TensileTest_020-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_020.tif
DOI: 10.13130/rd_unimi/qa4bro/dcx5hn
title: 250408_NONOPT1_TensileTest_021-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_021.tif
DOI: 10.13130/rd_unimi/qa4bro/br0evq
title: 250408_NONOPT1_TensileTest_022-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_022.tif
DOI: 10.21950/jiclyw
title: Estimation of life expectancies for the dependent population in Spain

description: Estimation of the total and dependency-free life expectancy for the dependent population in Spain. Using this information, it is intended to obtain the premium for a private insurance policy that would cover these contingencies. (Project: Statistical assessment of risk factors in the dependent population in Spain
description: Excel
DOI: 10.21950/zfhl8j
title: Machine and deep learning applications in Insurance and Finance

description: Use of various algorithms for building stacked or multi-transformer network models. These models are used to estimate the volatility of the S&P500 index and the reserve level of an insurer. (Project: Use of machine and deep learning techniques to solve actuarial and financial problems)
description: Excel
DOI: 10.25549/inscriptifact-ouc15244286
title: Phylactery

description: [Text Description:] Portions of Deuteronomy 5, 10-11, Exodus 13 ❧ [Text or Publication Number Note:] 1Q13 Phyl ❧ [Excavation Description:] Found in Cave ❧ of Qumran during excavations between February 15 and March 5 1949, carried out of Roland de Vaux, G. L. Harding, and the Palestine Archaeological Museum. ❧ [Medium:] Parchment ❧ [Script Note:] Jewish ❧ [Physical Object Description:] 58 parchment fragments with carbon-based ink on one side
DOI: 10.25549/inscriptifact-ouc15257243
title: Royal Scribe, Amenhotep

description: [Text Description:] Inscription: sS nsw, jmn-http - The Royal Scribe, Amenhotep ❧ [Excavation Description:] Gifted to the Los Angeles County Museum of Art by Ruth Greenberg ❧ [Medium:] Stone ❧ [Script Note:] Hieroglyphs ❧ [Physical Object Description:] Steatite Scarab w/green glaze - colorized in modern times; bored lengthwise; chips at top and bottom bore hole; possible wear marks on head ❧ [Physical Object Note:] L: 1.46cm; H: .65cm; W: 1.08cm
DOI: 10.13130/rd_unimi/qa4bro/t16cxa
title: 250408_NONOPT1_TensileTest_023-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_023.tif
DOI: 10.21950/yg5diq
title: Thermo-electro-mechanical aging and degradation of conductive 3D printed PLA/CB composite

description: The dataset comprises the frequency response analysis for samples of conductive Polylactic Acid (PLA)/ Carbon Black (CB) composite with different longitudes and printing directions.&lt;/br&gt;
All file names consist of three characters: nAx.dat &lt;/br&gt;
where n is a sequential number.&lt;/br&gt;
A can be A=L for Longitudinal, A=O for oblique and A=T for transversal printing&lt;/br&gt;
Finally, x=1 for length L1 in the related publication, x=2 for length L2 and x=3 for length L2.75&lt;/br&gt;
The data in the files are self-explanatory
description: Description of the project: 
Conductive polymeric composites consist of a polymeric matrix filled with conductive particles, providing electrical properties to a naturally insulating material. Their use in material extrusion printers provides great flexibility to geometrical design functional components. However, the degradation of their mechanical behavior during and after the application of electric currents is still absent in the literature. The flow of electric currents
induces material heating affecting the strength, and mechanical deformation alters the electric properties leading to bilateral dependences. These processes evolve during the application of electric fields and may degrade the material behavior permanently. In this work, we address these questions taking as baseline material a 3D printed conductive Polylactic Acid (PLA)/ Carbon Black (CB) composite. 

Methodology:

We performed multi-physical characterizations on samples with different printing orientations and lengths evaluating: i) electric behavior under direct current
(DC) regime and a wideband analysis by a Frequency Response Analysis; ii) thermo-electrical behavior studying the temperature field evolution under applied electric fields; iii) thermo-mechanical behavior under uniaxial tension for different testing temperatures; iv) thermo-electrical aging/degradation effects on the mechanical behavior. The study considers a wide range of electric fields (from 180 V/m to 600 V/m) and temperatures (from 25◦ to 130 ◦C). The results show enhanced electrical conductivity and mechanical stiffness when using a printing orientation parallel to the electro-mechanical loading. This coupled response is highly influenced by thermal effects due to Joule heating, especially when surpassing the glass transition and cold crystallization temperatures
of the composite. The occurrence of such phase transitions governs the material degradation by promoting the growth of mesostructural pores during heating cycles arising from the application of electric fields. The obtained results are essential to unravel changes in mechanical properties when standing for continuous electric conduction
and provide an experimental background for the lifetime expectance for the devices and possible thermoelectro-mechanical failure.
DOI: 10.13130/rd_unimi/qa4bro/3nloew
title: 250408_NONOPT1_TensileTest_024-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_024.tif
DOI: 10.21950/sgyqpo
title: Integral-Based Event Triggering Actuator Fault-Tolerant Control for an Active Suspension System Under a Networked Communication Scheme dataset

description: This data set comprises the vehicle responses under different controllers for the active suspension system:&lt;/br&gt;
- Passive suspension. (Passive)&lt;/br&gt;
- Active-suspension networked Non Fault-Tolerant control under a traditional event-triggering scheme. (TET NFT)&lt;/br&gt;
- Active-suspension networked Fault-Tolerant control under a traditional event-triggering scheme. (TET FT)&lt;/br&gt;
- Active-suspension networked Non Fault-Tolerant control under an integral event-triggering scheme. (IET NFT).&lt;/br&gt;
- Active-suspension networked Fault-Tolerant control under an integral event-triggering scheme. (IET FT). This is the proposed methodology.&lt;/br&gt;
Data is contained in MATLAB .mat files. Each row corresponds to:&lt;/br&gt;
1) Time (s)&lt;/br&gt;
2) Vertical acceleration (m/s^2)&lt;/br&gt;
3) Pitch acceleration (rad/s^2)&lt;/br&gt;
4) Roll acceleration (rad/s^2)&lt;/br&gt;
5) Front right Actuator force (N)&lt;/br&gt;
6) Front left Actuator force (N)&lt;/br&gt;
7) Rear right Actuator force (N)&lt;/br&gt;
8) Rear left Actuator force (N)&lt;/br&gt;
9) Front right suspension deflection (m)&lt;/br&gt;
10) Front left suspension deflection (m)&lt;/br&gt;
11) Rear right suspension deflection (m)&lt;/br&gt;
12) Rear left suspension deflection (m)&lt;/br&gt;
13) Front right tyre load (N)&lt;/br&gt;
14) Front left tyre load (N)&lt;/br&gt;
15) Rear right tyre load (N)&lt;/br&gt;
16) Rear left tyre load (N)&lt;/br&gt;
17) Front right controller triggering&lt;/br&gt;
18) Front left controller triggering&lt;/br&gt;
19) Rear right controller triggering&lt;/br&gt;
20) Rear left controller triggering&lt;/br&gt;
21) Vertical displacement of the center of gravity (m)&lt;/br&gt;
22) Front right tyre deflection (m)&lt;/br&gt;
23) Front left tyre deflection (m)&lt;/br&gt;
24) Rear right tyre deflection (m)&lt;/br&gt;
25) Rear left tyre deflection (m)&lt;/br&gt;
description: The datasets were obtained using CarSim with different controllers (Passive, TET NFT, TET FT, IET NFT, IET FT) for different road conditions (Road bump, Sine road profile, B-Grade random road profile).
description: Matlab
description: Simulink
description: CarSim
DOI: 10.13130/rd_unimi/qa4bro/l7d9nz
title: 250408_NONOPT1_TensileTest_025-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_025.tif
DOI: 10.13130/rd_unimi/qa4bro/rdznfr
title: 250408_NONOPT1_TensileTest_026-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_026.tif
DOI: 10.21950/zyw0be
title: Experimental data of brazilian tests with loading arcs on concrete discs: force of the universal testing machine and deformation of the strain gauges in the loading arc.

description: The seven TXT files cointain the information related to the concrete discs tested for the paper indicated in "RELATED PUBLICATIONS" of this file (https://doi.org/10.1016/j.ijrmms.2020.104380). The names of the files coincide with the disc numbers indicated in the table 4 of the mentionned article. The gauges are numbered from left to right as 1,2,3,4 and 5 respectively (see figures 2 and 3). The dimensions of the discs are described in the table 1 of the article and their composition is indicated in the part "2.1 Material"
description: Methodology:
The sampling frequency of all the variables measured is 10 Hz.
DOI: 10.25549/inscriptifact-ouc15244269
title: Thutmosis III

description: [Text Description:] The name of Thutmosis III is in the cartouche in center of scarab base flanked by two bound captives ❧ [Excavation Description:] Gifted to the Los Angeles County Museum of Art by Ruth Greenberg ❧ [Medium:] Stone ❧ [Script Note:] Hieroglyphs ❧ [Physical Object Description:] Steatite Scarab w/red glaze w/in the engraving; back worn from use ❧ [Physical Object Note:] L: 1.7cm, H: .75cm, W: 1.21cm
DOI: 10.25549/inscriptifact-ouc15257240
title: Glyptic scarab seal

description: [Text Description:] An ape/monkey with and stylized ankh sign (life). ❧ [Excavation Description:] Donated to the Los Angeles County Museum of Art by the Hearst Foundation ❧ [Medium:] Stone ❧ [Physical Object Description:] Steatite w/green glaze; bored lenghwise and chipped at head area around hole; are two holes bored widthwise through the bottom legs and the back upper legs ❧ [Physical Object Note:] L: 1.88cm; H: .85cm; W: 1.39cm
DOI: 10.13130/rd_unimi/qa4bro/ahrtr3
title: 250408_NONOPT1_TensileTest_027-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_027.tif
DOI: 10.21950/mmvbuj
title: Dataset for sample T1DM simulated patients used to validate the paper Muñoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896

description: The AIDA diabetes simulator has been used to generate 10 days of data for the different models implemented by the tool.
description: The dataset could help reproducing the results in the companiog paper and to foster new research.
Description of the project: The dataset has been gathered to valitade the research presented in Munoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896
A new mechanism inspired by metabolic models for glucose dynamics and trainable on a per-patient-basis is proposed. The differential equations for carbohydrate and insulin absorption are modeled using a Recurrent Neural Network (RNN) implemented using Long Short-Term Memory (LSTM) cells.
Methodology: The dataset has been generated using the AIDA diabetes simulator which is intended for simulating the effects on the blood glucose profile of changes in insulin and diet for a typical insulin-dependent (type 1) diabetic patient. The simulator includes 40 different patient models with different parameters controlling the metabolic model that is used in order to generate BG levels for different food intake and insulin injection patterns. The AIDA diabetes simulator can be downloaded as a freeware tool or it can be used online. The simulator uses a 15 min sample rate for simulated BG levels.
description: The implemented model is described in the companion paper. In particular, the implementation of the model in Phython is described in Appendix A. Model Implementation
DOI: 10.13130/rd_unimi/qa4bro/alluwj
title: 250408_NONOPT1_TensileTest_028-1.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_028.tif
DOI: 10.15468/dl.phex4m
title: Occurrence Download

description: A dataset containing 25037 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Bahamas, Mexico)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Vitis rotundifolia"
 ]
}

 The dataset includes 25037 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028503-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.knth7f
title: Occurrence Download

description: A dataset containing 9167325 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9167325 records from 2870 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077444-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16669102
title: SARS-CoV-2 Infektionen in Deutschland

description: Der vorliegende Datansatz enthält umfassende Informationen zu SARS-CoV-2-Infektionen in Deutschland, die gemäß dem Infektionsschutzgesetze (IfSG) von den Gesundheitsämtern an das Robert Koch-Institut (RKI) gemeldet wurden. Die Daten umfassen Informationen zur Anzahl der bestätigten Fälle, Todesfälle und Genesungen, aus denen sich weitere Kennzahlen im Zusammenhang mit der COVID-19-Pandemie ableiten lassen. Der Datensatz wird täglich aktualisiert und enthält detaillierte Informationen auf Landkreisebene, die nach verschiedenen Altersgruppen aufgeschlüsselt sind. Die Bereitstellung des Datensatzes soll dazu beitragen, das Verständnis der COVID-19-Pandemie in Deutschland zu verbessern und die Berichterstattung, Forschung und Analyse in diesem Bereich zu unterstützen.
DOI: 10.21233/e2c6-hq61
title: Clair Lake? water chemistry dataset

description: Raw data for the Clair Lake? water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/9w16-m787
title: Sanford Lake water chemistry dataset

description: Raw data for the Sanford Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.