Citizen Science is a new kind of science based on participation, knowingly and voluntarily, of thousands of citizens who generate large amounts of data. Anyone can contribute their intelligence or technological resources to find socially useful results. The challenge for researchers is to develop the digital society as a new tool to be seized and to be researched.
In Ibercivis we support numerous research groups around the world that launch their experiments through us. We help these groups by adapting their implementation, designing the interfaces, integrating and deploying technology and explaining their work to volunteers.
Choose one of the live experiments to begin working with us, or study completed experiments. And the best ones may still be yet to come, tell us your idea, and we will work on it!
If you have ideas for research that require many people participating through the computer network, talent or in any way, we will help. We provide:
These pictures show the differences between both processes.
Image 1: Crystal structure of the zeolite ZSM-11 with "hollows" where gas molecules pass through. Image 2: Other view of the crystal structure of the zeolite ZSM-11 with cylindrical galleries inside the material. Image 3: Channels of the zeolite getting full of gas molecules represented with blue spheres. Image 4: 3D Channel network of a zeolite.
Image: 2D representation of a pillars (blue circles) configuration tessellated with Dealuny triangles. In red we can see regions where methane molecules can not go trough.
In the image: Transistors y semiconductors
In the image : Quantic interference between two sources.
In the image : Nanometric semiconductor device
Image: The broccoli is an example of a natural fractal with approximate self-similarity.
Image: The Sierpinski triangle is one of the well-known mathematical fractal by a clear example of exact self-similarity.
Image: Example of an internal state for the three-dimensional Anderson model in the metal-insulator transition. In the critical point, the states are fractals but, unlike natural fractals (with approximate self-similarity) or the mathematical fractals (with exact self-similarity), in critical disordered systems the internal states show statistical self-similarity. Note that the critical state is extended in the interior of the cube, which defines the system's volume, but it does not occupy all the permitted volume. For this reason, we say critical states have an effective dimension (fractal dimension) which is smaller than the real dimension of the system, which in this case is 3. Image from L. J. Vasquez, A. Rodriguez, and R. A. Roemer, Phys. Rev. B 78 195106 (2008).
Image: Three-dimensional arrangement of points connected to their first neighbours. Also known as three-dimensional Anderson model. In the example, the system linear size is L=3, however the total volume is L^3=27. Each point represents an atom or molecule within a finite crystal network. In this model, the disorder is represented by a random potential assigned to each of the points. Note that the left face of the cube is connected to 9 terminals or ports from where the electrons are beamed. The beamed electrons interact with the points in the network before being dispersed or expelled to the outside of the crystal lattice through its terminals.
In the image: Model of a patient and a virtual radiographic generation using monte-carlo simulation techniques.
In the image: Temporal evolution of the energy deposited by a narrow beam of photons in a dummy semi-infinite dimensions.
In the image: View of a prostate cancer treatment using 112 radioactive seeds of Iodine-125.
Image: Typical cell membrane schema. The extracellular fluid and the cytoplasm are typically hydrophilic whereas the interior of the membrane is highly hydrophobic.
Image: Example of a pharmaceutical compound (large spheres) dissolved in water (blue and white molecules).
GRIPENET: end of the first season
Figure (left to right and up down): Cut of the vacuum chamber, where the plasma shall be confined. It's approximate measures are 3.5 x 8 m / 3 superconducting coils crating the main magnetic field, some 100,000 times the terrestrial field / ITER, within the cryostat keeping the coils in the superconducting state and within the shielding isolating it from the exterior.
In the figures: Part of a trajectory and behavior of the plasma as an ensemble. A home computer can take between 15 and 30 minutes in calculating a complete trajectory. The calculation of many trajectories allows us to get an idea about the aspect and properties of plasma in the reactor. Sooner or later, the nuclei in ITER eventually escape. Ibercivis helps us to learn the escape regions (in this case, the upper region).
Fusion and the energy panorama
Figure: Tokamak is a Russian invention, its names standing for Toroidnalnaya Kamera v Magnetnikh Katushkakh (literal English translation: Toroidal Chamber in Magnetic Coil) .
Fusion Reactors: Tokamaks and Stellarators
Figure: Tokamak. The engineer from the Lawrence Livermore Laboratory of California (right) and a visiting Japanese engineer examine the superconducting material for tests.
Fusion and the Science of Materials
Fusion reactors: ITER
Image: Protein-ligand docking.
Image: Characterization of the active site for carbon atoms interactions. The box shows the region where docking studies will be preformed. The yellow colour indicates the areas more favorable to the binding.
Image: Example of ligand, exhibiting in vitro and in vivo activity, on the protein surface.
Docking: Searching new drugs against cancer using the computer
In the figure: ttr-estabilizadores
Image: Above: System with a 10% dilution in which there is no coexistence of phases. However, there are magnetized regions of all sizes; it is the equivalent of a hailstorm. Bottom: Pure models near the transition which shows a bubble and a magnetized band. The black region is the equivalent of an iceberg floating in the sea.
Image: Material in a macroscopic magnetisation.
Glass and disordered systems
Image: simulation of magnetic materials by computer
Proteins are fundamental for living organisms. Almost every biological process depends on the presence or activity of this kind of molecules, whose function in an organism is determined by its molecular structure.
In the image: CPK representation of the molecular structure of a protein in the cellular membrane of the central nervous system neurons. It is made of 374 amino acids and 6700 atoms. Please note the complexity of the structure. Right: protein representation. Please note the helices, they are the secondary α-hélices called.
The different substructures are coupled to each other and form the tertiary structure of the protein, ultimately responsible for its biological function. Sometimes, several tertiary structures are combined in a certain way and are then told that they form a quaternary structure.
In the image: Box for simulating molecular dynamics. The structure of the amino acid is represented in the form of spheres, as the so-called Van der Waals representation. The set of 'rods' represents the surrounding water molecules. The simulation is performed modelling this box in three dimensions in such a way that corresponds to a real dissolution of the amino acid.
In the image: Map showing the energy landscape of an amino acid. It represents the energy of the system depending on two geometric parameters, in particular, the torsion angles that form the main chain atoms. Different valleys can be seen where the energy is lower. This would correspond to the most stable conformations of the amino acid and therefore to the more likely structures.
In these graphs one can observe a series of valleys separated by hills. The depth of the valley is related to the stability of this structure in particular. The deeper a valley, the most stable conformation is concerned. However, there are numerous valleys, corresponding to more or less stable structures, separated by barriers of varying height, that indicate how easy or difficult it is to move from one stable to another.
These landscapes, created from simulations performed in Ibercivis volunteer computers will be available to the scientific community in a publicly accessible database.
Image: from left to right, Sara Sanmartín, Nuria Robledo, Dr. Juan Francisco Vega, Prof. Javier Martínez-Salazar, Dr. Javier Ramos, Jon Otegui and Dr. Victor Cruz.
What are prime numbers?
In the figure: The Sieve of Eratosthenes was created by Eratosthenes of Cyrene, a greek mathematician from the 3rd century B. C. It is a simple algorithm to find all prime numbers up to a specified integer.
Around the year 300 B. C., Euclides demonstrated that an infinitude of prime numbers exists. The prime numbers are the opposite of composite numbers which are those numbers with some natural divisor rather than itself or the unit. By definition, the number 1 is not a prime nor a composite number.
The distribution of the prime numbers is a recurrent subject of investigation in the Theory of Numbers: if considered individually the prime number seem to be randomly distributed, however its "global" distribution follows well-defined laws.
There is a great number of open conjectures about the prime numbers like for instance the Riemann Hypothesis and the Goldbach's Conjecture.
In the figure: Riemann zeta function ζ(s) in the complex plane. The color of a point s encodes the value of ζ(s): dark colors denote values close to zero and hue encodes the value's argument. The white spot at s = 1 is the pole of the zeta function; the black spots on the negative real axis and on the critical line Re(s) = 1/2 are its zeros.
We propose to take advantage of Ibercivis calculus capability to know a little more on these numbers, about their distribution, and to try to find counterexamples to the conjectures.
In this project the software code is publicly available to allow any volunteer to read the code and possibly to incorporate improvements on it. It is also available a forum to the exchange of ideas on the project.
History of the prime numbers
Image: Visualization of the various routes through a portion of the Internet. From 'The Opte Project'.
Image: Example of a network with community structure. Nodes represent email users from the Universitat Rovira i Virgili de Tarragona, and links means communication among them. Different colors stand for the departments of the university. Obviously, individuals inside a department communicate much more often. A. Arenas, L. Danon, A. Díaz-Guilera, P. Gleiser and R. Guimerà, European Physics Journal B, 38(2), 373-380 (2004).
Image: Gen regulation network for the Mycobacterium tuberulosis. Every node represent a gen, and the links stand for the regulation relationship between a transcription factor and the correspondent regulated gen. Different colors mean different character of the gens, as far as regulation dynamics is concern.
Image: from left to right, Pablo Piedrahita, Julia Poncela, Yamir Moreno, Carlos Gracia, Joaquín Sanz, Mario Floría
Surface screening results for PDB:1QCF. From up left to down right; a) beads represent protein spots and the color of each bead is related with the value of the scoring function, so colors from red to blue indicate lower values for the scoring function, b) histogram with the distribution of scoring function values, c) red and blue molecules represent crystallographic and predicted pose for the ligand, RMSD is lower than 1 Angstrom, and d) depiction of the hydrogen bonds established by the ligand with the closest residues.