This file is the softwarecode in R for a clusteranalysis to cluster and characterize autonomous last mile concepts in a standardized and holistic manner.

With the developed clusters it is possible to better categorize and compare individual autonomous last mile concepts. Furthermore, the developed taxonomy of autonomous last mile concepts and the cluster analysis allow an evaluation of the interrelations between characteristics of autonomous last mile concepts, so that the design of new concepts as well as the adaptation or selection of a concept for a specific use case is supported.

PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2018-2019 autonomous platform ocean data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data was collected collection for this product using Sounding Oceanographic Lagrangian Observer II (SOLO-II) instruments. Data collection is complete.The PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises, deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 and R/V Sally Ride in Sept 2019. The first 2018 cruise coincided collaborative activities with R/V Mirai. The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. For characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). There are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:https://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 https://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001https://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 (this doi)https://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001https://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001The CAMP2Ex 2019 data DOI is:https://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001The CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which benefits the analysis of both data sets for achieving the science objectives.

The global autonomous delivery robots market size is expected to reach USD 2,111.3 million by 2029 according to a new study by Polaris Market Research.

This file is the softwarecode in R for a clusteranalysis to cluster and characterize autonomous fulfillment concepts in a standardized and holistic manner.

With the developed clusters it is possible to better categorize and compare individual autonomous fulfillment concepts. Furthermore, the developed taxonomy of autonomous fulfillment concepts and the cluster analysis allow an evaluation of the interrelations between characteristics of autonomous fulfillment concepts, so that the design of new concepts as well as the adaptation or selection of a concept for a specific use case is supported.

Statistics on R&D Activities in the Business Sector: Total internal expenditure and personnel on R+D by autonomous communities and internal expenditure/personnel. Autonomous Communities.

Replication datasets and R script for the forthcoming publication, entitled "Measuring public opinion about autonomous vehicles using data from Reddit, Public Deliberation, and Surveys", in Public Opinion Quarterly, Special Issue on New Data in Social and Behavioral Research.

**************** NTU Dataset ReadMe file *******************Please consider the latest version.Attached files contain our data collected inside Nanyang Technological University Campus for pedestrian intention prediction. The dataset is particularly designed to capture spontaneous vehicle influences on pedestrian crossing/not-crossing intention. We utilize this dataset in our paper "Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields" submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence.The dataset consists of 35 crossing and 35 stopping* (not-crossing) scenarios. The image sequences are in 'Image_sequences' folder. 'stopping_instants.csv' and 'crossing_instants.csv' files provide the stopping and crossing instants respectively, utilized for labeling the data and providing ground-truth for evaluation. Camera1 and Camera2 images are synchronized. Two cameras were used to capture the whole scene of interest.We provide pedestrian and vehicle bounding boxes obtained from [1]. The occlusions and mis-detections are linearly interpolated. All necessary detections are stored in 'Object_detector_pedestrians_vehicles' folder. Each column within the csv files ('car_bndbox_..') corresponds to a unique tracked car within each image sequence. Each of the pedestrian csv files ('ped_bndbox_..') contains only one column, as we consider each pedestrian in the scene separately. Additional details:* [xmin xmax ymin ymax] = left right top down* Dataset frequency: 15 fps.* Camera parameters (in pixels): f = 1135, principal point = (960, 540).Additionally, we provide semantic segmentation output [2] and our depth parameters. As the data were collected in two phases, there are two files in each folder, highlighting the sequences in each phase.Crossing sequences 1-28 and stopping sequences 1-24 were collected in Phase 1, while crossing sequences 29-35 and stopping sequences 25-35 were collected in Phase 2.We obtained the optical flow from [3]. Our model (FLDCRF and LSTM) codes are available in 'Models' folder.If you use our dataset in your research, please cite our paper:"S. Neogi, M. Hoy, W. Chaoqun, J. Dauwels, 'Context Based Pedestrian Intention Prediction Using Factored Latent Dynamic Conditional Random Fields', IEEE SSCI-2017."Please email us if you have any questions:1. Satyajit Neogi, PhD Student, Nanyang Technological University @ satyajit001@e.ntu.edu.sg 2. Justin Dauwels, Associate Professor, Nanyang Technological University @ jdauwels@ntu.edu.sgOur other group members include:3. Dr. Michael Hoy, @ mch.hoy@gmail.com4. Dr. Kang Dang, @ kangdang@gmail.com5. Ms. Lakshmi Prasanna Kachireddy, 6. Mr. Mok Bo Chuan Lance,7. Dr. Hang Yu, @ fhlyhv@gmail.comReferences:1. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", NIPS 2015.2. A. Kendall, V. Badrinarayanan, R. Cipolla,Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding", BMVC 2017.3. C. Liu. ``Beyond Pixels: Exploring New Representations and Applications for Motion Analysis". Doctoral Thesis. Massachusetts Institute of Technology. May 2009.* Please note, we had to remove sequence Stopping-33 for privacy reasons.

Context

This data was collected to train an autonomous car prototype as our college project. Use https://www.kaggle.com/firstofhisname/autonomous-arena-guide for starters.

Content

The data consists of 14x32 grayscale pixel values along with proper labels as in which direction to maneuver the car. The labels are represented as follows: L- Left, R- Right, F- Front, S- Stop

Inspiration

Udacity Autonomous driving nano degree​ program, Waymo, Tesla self-driving car

Other sectors: Public Administration, Higher Education and PNPI. Total domestic expenditure and personnel in R & D in Biotechnologyautonomous comunities and type of indicator. National.

The foreign body response impedes the function and longevity of implantable drug delivery devices. As a dense fibrotic capsule forms, integration of the device with the host tissue becomes compromised, ultimately resulting in device seclusion and treatment failure. We present FibroSensing Dynamic Soft Reservoir (FSDSR), an implantable drug delivery device capable of monitoring fibrotic capsule formation and overcoming its consequences via soft robotic actuations. Occlusion of the FSDSR porous membrane was monitored over 7 days in a rodent model using electrochemical impedance spectroscopy. The electrical resistance of the fibrotic capsule correlated to its increase in thickness and volume. Our FibroSensing membrane showed great sensitivity in detecting changes at the abiotic/biotic interface, such as collagen deposition and myofibroblast proliferation. The potential of the FSDSR to overcome fibrous capsule formation and maintain constant drug dosing over time was demonstrated in silico and in vitro. Controlled closed-loop release of methylene blue into agarose gels (with a comparable fold change in permeability relating to 7 and 28 days in vivo), was achieved by adjusting the magnitude and frequency of pneumatic actuations following impedance measurements by the FibroSensing membrane. By sensing fibrous capsule formation in vivo, the FSDSR will be capable of probing and adapting to the foreign body response through dynamic actuation changes. Informed by real-time sensor signal, this device offers the potential for long-term efficacy and sustained drug dosing, even in the setting of fibrous capsule formation.

Global Autonomous Underwater Gliders Market is anticipated to experience remarkable expansion, with a projected Compound Annual Growth Rate (CAGR) of 10.19% from 2025 to 2033. According to the market analysis, the market size is forecasted to reach USD 5.90 Billion by the end of 2033, up from USD 2.46 Billion in 2024.

The Global Autonomous Underwater Gliders market size to cross USD 5.9 Billion i

# Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings

## Background

This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil.

In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.

The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked.

For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935

Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column.

## Description:
Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).

Deviations (integer): number of face deviations of the participant during the question answering identified by the system.

EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.

NumberWord (integer): number of words comprised in the sentence the participant gave.

SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.

Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.

True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions.

Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201

## Creators
Daniel Tozadore: dtozadore@gmail.com
Roseli Romero: rafrance@icmc.usp.br

## License:
[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)

Table of INEBase Main R+D indicators in high tech sectors by Autonomous Communities, type of indicator and total / %. National. High Technology Indicators

Autonomous Drilling Robots Market Outlook

The global market size for autonomous drilling robots was valued at approximately USD 1.5 billion in 2023 and is expected to grow to USD 4.8 billion by 2032, exhibiting a CAGR of 13.5% during the forecast period. This remarkable growth is fueled by increasing demand for automation in the drilling industry and the pressing need to enhance operational efficiency and safety.

One of the primary growth factors of the autonomous drilling robots market is the significant technological advancements in robotics and artificial intelligence. These advancements have enabled the development of more sophisticated and reliable autonomous drilling robots that can perform complex tasks with minimal human intervention. The integration of AI and machine learning algorithms into these robots has improved their precision, adaptability, and decision-making capabilities, making them indispensable in critical drilling operations.

Another crucial factor driving the growth of this market is the escalating demand for energy, which necessitates the exploration and extraction of oil and gas from increasingly challenging and remote locations. Autonomous drilling robots offer a viable solution to the difficulties associated with such environments, including harsh weather conditions, deep-water operations, and complex geological formations. By reducing the human workforce needed in these perilous conditions, autonomous drilling robots not only enhance safety but also improve the overall efficiency and cost-effectiveness of drilling operations.

The growing emphasis on minimizing environmental impact and adhering to stringent regulatory frameworks is also propelling the adoption of autonomous drilling robots. These robots are designed to optimize drilling processes, thereby reducing waste, preventing oil spills, and minimizing the environmental footprint of drilling activities. Furthermore, the ability to perform real-time monitoring and data analysis helps in ensuring compliance with environmental regulations, making autonomous drilling robots a preferred choice for environmentally conscious companies.

From a regional perspective, the autonomous drilling robots market is witnessing significant growth across various geographies. North America, with its robust oil and gas sector and early adoption of technological innovations, is expected to dominate the market. The Asia Pacific region, driven by increasing investments in the energy sector and the presence of emerging economies, is also projected to exhibit substantial growth. Europe, Latin America, and the Middle East & Africa are anticipated to show moderate growth, supported by ongoing exploration activities and technological advancements.

Component Analysis

The autonomous drilling robots market is segmented into hardware, software, and services. The hardware segment includes various components such as sensors, actuators, control systems, and mechanical parts essential for the functioning of drilling robots. The demand for advanced hardware is on the rise as it directly influences the performance, durability, and reliability of autonomous drilling robots. Companies are investing heavily in R&D to develop robust hardware solutions that can withstand harsh operational conditions and enhance the efficiency of drilling activities.

The software segment is equally crucial, encompassing control software, data analytics tools, and AI algorithms that drive the autonomous functions of drilling robots. The integration of sophisticated software systems allows for real-time data processing, decision-making, and predictive maintenance, thereby improving the overall operational efficiency and reducing downtime. The continuous evolution of software technologies and the growing adoption of cloud-based solutions are anticipated to further propel the growth of this segment.

Services form another critical component of the autonomous drilling robots market, including installation, maintenance, training, and support services. As the adoption of autonomous drilling robots increases, the demand for comprehensive service packages is also rising. Service providers are focusing on offering customized solutions to meet the specific needs of their clients, ensuring optimal performance and longevity of the equipment. Additionally, the trend towards outsourcing maintenance and support services is gaining traction, providing a lucrative opportunity for service providers in this market.

Overall, the component analysis of the autonomous drilling r

**************** NTU Pedestrian Dataset *******************Attached files contain our data collected inside Nanyang Technological University Campus for pedestrian intention prediction. The dataset is particularly designed to capture spontaneous vehicle influences on pedestrian crossing/not-crossing intention.We utilize this dataset in our journal paper "Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields" accepted by IEEE Transactions on Intelligent Transportation Systems.The dataset consists of 35 crossing and 35 stopping* (not-crossing) scenarios. The image sequences are in 'Image_sequences' folder.'stopping_instants.csv' and 'crossing_instants.csv' files provide the stopping and crossing instants respectively, utilized for labeling the data and providing ground-truth for evaluation. Camera1 and Camera2 images are synchronized. Two cameras were used to capture the whole scene of interest.We provide pedestrian and vehicle bounding boxes obtained from [1]. The occlusions and mis-detections are linearly interpolated. All necessary detections are stored in 'Object_detector_pedestrians_vehicles' folder. Each column within the csv files ('car_bndbox_..') corresponds to a unique tracked car within each image sequence. Each of the pedestrian csv files ('ped_bndbox_..') contains only one column, as we consider each pedestrian in the scene separately.Additional details:* [xmin xmax ymin ymax] = left right top down* Dataset frequency: 15 fps.* Camera parameters (in pixels): f = 1135, principal point = (960, 540).Additionally, we provide semantic segmentation output [2] and our depth parameters. As the data were collected in two phases, there are two files in each folder, highlighting the sequences in each phase.Crossing sequences 1-28 and stopping sequences 1-24 were collected in Phase 1, while crossing sequences 29-35 and stopping sequences 25-35 were collected in Phase 2.We obtained the optical flow from [3]. Our model (FLDCRF) codes are available here: https://github.com/satyajitneogiju/FLDCRF-for-sequence-labelingIf you use our dataset in your research, please cite our paper(s):1. S. Neogi, M. Hoy, K. Dang, H. Yu, J. Dauwels, "Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields". Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2019.2. "S. Neogi, M. Hoy, W. Chaoqun, J. Dauwels, 'Context Based Pedestrian Intention Prediction Using Factored Latent Dynamic Conditional Random Fields', IEEE SSCI-2017."Please email us if you have any questions:1. Satyajit Neogi, PhD Student, Nanyang Technological University @ satyajit001@e.ntu.edu.sg2. Justin Dauwels, Associate Professor, Nanyang Technological University @ jdauwels@ntu.edu.sgOur other group members include:3. Dr. Michael Hoy, @ mch.hoy@gmail.com4. Dr. Kang Dang, @ kangdang@gmail.com5. Ms. Lakshmi Prasanna Kachireddy,6. Mr. Mok Bo Chuan Lance, and7. Mr. Xu Yan References:1. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", NIPS 2015.2. A. Kendall, V. Badrinarayanan, R. Cipolla,Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding", BMVC 2017.3. C. Liu. ``Beyond Pixels: Exploring New Representations and Applications for Motion Analysis". Doctoral Thesis. Massachusetts Institute of Technology. May 2009.* Please note, we had to remove sequence Stopping-33 for privacy reasons.FUNDINGSTE-NTU NRF corporate lab@university scheme

Statistics on R&D Activities in the Business Sector: Personnel in R+D in Full Time Equivalent by Autonomous Communities, sectors and type of personnel. National.

Table of INEBase Main R+D indicators in high tech sectors by Autonomous Communities, type of indicator and Total / %. National. High Technology Indicators

The dataset is comprised of both real and synthetic images from a vehicle’s forward-facing camera. Each camera image is accompanied by a corresponding pixel-level semantic segmentation image (all files are .png files). In total, the dataset contains 5600 images in the training/validation set and 1400 images in the testing set. The training dataset contains mostly synthetic RGB images collected with a wide range of weather and lighting conditions using the CARLA simulator [1]. In addition, the training data also includes a small pre-selected subset of data from the Cityscapes training dataset – which is comprised of RGB-segmentation image pairs from driving scenarios in various European cities [2]. The testing data is split into three sets. The first set contains synthetic CARLA images with weather/lighting conditions that were not present in the training set. The second set is a subset of the Cityscapes testing dataset. Finally, the third set is an unknown testing set which will not be revealed to the participants until after the submission deadline. [1] Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017, October). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR. [2] Cordts, M., Omran, M., Ramos,more » S., Rehfeld, T., Enzweiler, M., Benenson, R., ... & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).« less

The global autonomous bus door system market is experiencing significant growth, driven by the increasing adoption of autonomous vehicles and the rising demand for enhanced passenger safety and convenience in public transportation. The market, valued at approximately $250 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This robust growth is fueled by several factors, including advancements in sensor technology, artificial intelligence, and automation, leading to safer and more efficient bus operations. The integration of smart door systems into autonomous buses improves passenger boarding and alighting processes, enhances accessibility for individuals with disabilities, and streamlines overall transit efficiency. Different door types, including sliding, folding, and gliding doors, cater to diverse bus sizes and applications, ranging from minibuses to intercity coaches. The market is segmented geographically, with North America and Europe currently holding significant market shares, though the Asia-Pacific region is expected to witness substantial growth due to rapid urbanization and increasing investments in intelligent transportation systems. The leading players in this market—including Bode Sud S.p.A., Circle Bus Door Systems Co., Ltd, Ferro Doors A/S, IVY MACHINERY (NANJING) CO., LTD., R+W Antriebselemente GmbH, Schaltbau Holding AG, Daimler AG, Volkswagen AG, and Robert Bosch GmbH—are actively investing in research and development to enhance door system functionality and safety features. Further market expansion will depend on overcoming challenges such as high initial investment costs, regulatory hurdles for autonomous vehicle deployment, and ensuring robust cybersecurity measures to prevent potential vulnerabilities in the system. The continuous evolution of autonomous technology and the expanding adoption of smart cities will further contribute to the market's sustained growth over the forecast period. As autonomous bus technology matures and becomes more cost-effective, wider adoption and a resulting expansion of the market are expected.

andaluci_a arago_n asturias-principado-de autonomous-communities balears-illes canarias cantabria castilla-la-mancha castilla-y-leo_n catalun_a comunidades-auto_nomas comunitat-valenciana estadi_sticas extremadura galicia gasto-en-i_d-interna_ gasto-en-i_d-interna_-total high-technology-indicators indicadores-de-alta-tecnologi_a indicadores-si_ntesis-y-recopilaciones-en-i_d internal-expenditure investigacio_n-cienti_fica-y-desarrollo-tecnolo_gico madrid-comunidad-de murcia-regio_n-de national-total navarra-comunidad-foral-de pai_s-vasco personal-en-i_d_ personal-en-i_d_-total personnel-in-r_d_-total rd-indicators-synthesis-and-compilations rioja-la scientific-research-and-technological-development statistics tipo-de-indicador total total- type-of-indicator