Databases - Science topic

Anuradha Goswami Thank you for the information.

Here are some websites where you can find free DICOM images:

Yes, Michal S. Karbownik you can reuse your published dataset, if you're able to generate some new insights from it. Also, others can use your dataset to drive research. However, other researcher's usage rights depends on the Creative Commons (CC) license that you've signed during the upload. You can go through all CC licenses, here: About CC Licenses - Creative Commons .

All in all, I would like to say that publishing your data is a good practice and valuable scientific contribution. Everyone must follow it. FAIR Principles - GO FAIR (go-fair.org) is a wonderful movement in this path.

Dear Dr. Hajikhani!

I would say also OECD is a decent resource of knowledge:

a) OECD Main Science and Technology Indicators (2022). Available at:

b) OECD (2023), Gross domestic spending on R&D (indicator). doi: 10.1787/d8b068b4-en (Accessed on 14 January 2023) Available at:

Yours, Bulcsu Szekely

Hi,

I'm trying to build one for fruits.

I've started with images of fruits; then, I've added more information (like content) for each fruit, vegetable, etc.

Here is the paper:

But it is not complete yet ... I'm still adding more information to it.

Best regards,

Mihai

I want to verify a list of genes, find them related to a disease I am researching on Blaise Manga Enuh

Have a look at our database www.raabindata.com which have more than 40,000 free white blood cell images. Also you can access to more than 10,000 free leukemia images.

Zotero maybe another option

Hi,

Maybe, this database: https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/database.html

Once, I saw this database, it is quite a good one, though, I don't know if it contains the material you are looking for.

David

Mamta Giri ,

Congratulations for your selection of a very important ResearchGate discussion thread question, which has in recent years been generating a great deal of controversy along with lateral and longitudinal expansion from the public into the private domain.

The following article appeared in 2018 and it gives a good overview of some of the relevant issues involved in big data sharing of genomic data:

"OPINION article

Front. Public Health, 28 November 2018 | https://doi.org/10.3389/fpubh.2018.00334

Big Data Sharing: A Crucial Democratic Issue for Genomic Medicine

📷Benjamin Derbez*

Introduction

Big data are often viewed as responsible for major upheavals in many aspects of contemporary life (1) and in the health sector in particular (2). For instance, in medicine, big data are perceived as one of the major drivers of genomic medicine (3). Indeed, rapid genomic data collection on a large scale, made possible by the use of high-throughput sequencing technologies, has made the production of new medical knowledge possible. This knowledge has helped to improve disease prevention, risk prediction, individualized care, and patient involvement (4, 5). One of the conditions of such progress, however, is the need to create databases large enough to enable successful comparative analyses (6). While some initiatives seeking to share different national databases have been launched at the international level (7), the sharing of data between public institutions and private organizations remains a critical question.

Drawing on the example of databases of variants in breast and ovarian cancer predisposition BRCA 1–2 genes, we will show that genomic data is a techno-scientific democracy issue worth discussing. In this case, the recent evolution of patenting legislation has led to a shift from gene sequencing to the clinical interpretation of its results as the key activity of oncogenetics (8). Database access, which is necessary to estimate the risks associated with sequenced genetic variants, has become a critical issue, especially for private firms wishing to break into the market. In this context, the partial privatization of public databases, such as that of the French consortium that will be discussed later, is proof that there is a growing movement of public-private hybridization of these infrastructures. This shift, accentuated by the developments of high-throughput sequencing and genomic medicine, needs to be accompanied by reflection about the public health system user information contributing to the constitution of these databases.

Patenting genes

The controversy that shook the world of genetic cancer for years is well known. Indeed, the American company Myriad Genetics filed a patent application claiming BRCA1, BRCA2, and genetic methods of diagnosing a predisposition for breast and ovarian cancer (9, 10). Thanks to the legal ownership of these genes which had been designed as biotechnologies, the start-up from Salt Lake City sought to have a global monopoly on the hereditary breast cancer market, which was expected to experience robust growth. In the face of this offensive, institutional resistance (bringing together hospitals, ministries, associations, etc.) arose in the early 2000s in Europe and then in the United States (11). This resistance has often been interpreted as paradigmatic of the opposition between an “open science,” regulated by peers respecting the law of priority, and a “proprietary science,” regulated by the market, and respecting intellectual property (12). There was thus concern that the production of public knowledge would decline because of the legal appropriation of genes by private organizations (13).

An analysis of the British case, however, helps to get a more balanced view of this dichotomy. Indeed, (14–16) has shown that patents are perceived as legal weapons by private organizations as well as by public scientific, medical, and social institutions. Moreover, actors from private and public groups cannot be radically distinguished insofar as each defines the other in a complex network of negotiated interrelationships. In line with the studies undertaken on the role of patents in management science between academic circles and the business world (17, 18), Parthasarathy calls attention to how the NHS and Myriad reached an agreement in the early 2000s, making it possible to connect the “moral order” of the former, based on the principle of equal access to healthcare for all citizens, to the freedom of consumers valued by the latter. Among the negotiated items, it appears clearly that the issue of the transfer of data from Myriad to the NHS was essential and intended to add onto the public BRCA mutation databases. Beyond the issue of monopoly over the gene sequence through the patenting of genes or methods, this example clearly shows that the ownership of data is of crucial importance to both groups. With high-throughput sequencing technology, it has become a major issue.

Next generation sequencing

Two major developments placed the issue of the sharing of BRCA databases at the center of the debate from the 2010s. The first, naturally, was the full or partial decline in the patents claimed by Myriad Genetics around the world (19, 20). Indeed, this decline opened up the sequencing market to new private actors (GeneDx, Invitae, Pathway Genomics, Counsyl, etc.) and allowed public laboratories to carry out their activities. The second development was the progressive introduction of high-throughput DNA sequencing technology which began in the mid-2000s. The use of these “next generation” devices reinforced laboratories' analytical capacities. It is now possible to analyse within a few hours, and at the same time, several genes (panels) of several individuals, or even the complete genome of an individual at a much lower cost-100 dollars is regularly mentioned, compared to the 3 billion dollars spent in the framework of the Human Genome Project 20 years ago (21). All these developments have led stakeholders to focus on the issue of the classification of the genetic variants in BRCA genes.

A genetic variant from a sequenced individual can only acquire the status of “mutation,” i.e., the status of “pathogenic” variant, if it is clearly linked to a history of illness, either directly (in the individual or in their family) or indirectly (in a family affected by cancer and found to have the same variant). According to the current classification in genetics, the clinical significance of these variants may vary: they can be pathogenic, probably pathogenic, of unknown significance, benign, or probably benign. As (22) have pointed out, distinguishing between these categories is a major “interpretive dilemma” for geneticists. The classification of a variant in a given category depends on available data concerning the frequency of the link associating it with a specific disease. In the absence of data, the clinical significance of the variant is deemed unknown—a Variant of Unknown Significance (VUS)—until it is identified in other individuals with similar phenotypic characteristics. The importance of new DNA sequencing technologies thus lies in their ability to increase genetic databases more quickly in order to reduce the at times dramatic clinical uncertainty associated with diagnosed genetic anomalies (23). The sharing of information among geneticists, thanks to databases fed on an international scale, is a central issue1. This sharing of information, however, is now problematic.

Genomic databases

For several years now, science and technology studies have been stressing that physical infrastructure plays a central role in the production of knowledge (24–27). In this area, the study of genetic databases serves as a model (28–31). Indeed, the first molecular biology databases were launched by different public institutions around the world in the early 1980s [(32): 75]. With the spread of the Internet and the Human Genome Project in the 1990s, they quickly developed as a form of support for new open “communication regimes” between scientists, likely to encourage the emergence of new knowledge (33). However, an analysis of the construction of this information infrastructure shows that the modes of data publishing remain a major source of tension between different actors.

This tension has been highlighted by Bruno Strasser, for instance, in his study on the development of the comprehensive GenBank sequence database (32). This historian of life sciences argues that tensions linked to the different conceptions of data ownership arose from the outset of the project. Participants engaged in a “moral economy of natural history,” i.e., in a “system of values that places emphasis on the exchange of scientific knowledge” inherited from the naturalists of the eighteenth century, considered that the sequences published in scientific journals should be freely accessible data. Other participants, advocates of a “moral economy of experimentation” which has garnered momentum among molecular biologists, view sequences as the products of scientific activity and as the property of their authors. According to Strasser, GenBank embodies a form of hybridization of these two value systems. It appears that those who conceived it succeeded in taking advantage of the “ambiguity” of the very notion of “data,” owing to the fact that what seems “literally given” is at the same time “the result of an organized action” (34): 248). In the context of the Human Genome Project, this ambiguity has manifested itself in the emergence of information control modes which involve a complex interplay of revelation and concealment (35). Nowadays, as seen previously, in addition to the tensions inherent in the moral economies of science, other tensions associated with the political economy of knowledge resulting from the growing role played by private firms in the production of knowledge emerged from the early 1990s (36, 37). Beyond the question of the patentability of living organisms, it is now the question of sharing that is in front of the debate, like the case of BRCA1 and BRCA2 genes clearly shows it.

Data sharing

In the present case, i.e., the focus on BRCA1 and BRCA2 genes, there is no unique and comprehensive database of BRCA variants accessible to all professionals around the world. On the contrary, different databases developed by consortia of multinational public institutions or private organizations exist, but their access is generally limited. This is the case of the database developed by Myriad Genetics throughout the period the patents were under discussion. Although this is the largest database in the world, Myriad Genetics has exclusive access to it. This has given the company a major competitive asset in the BRCA testing market insofar as the database offers a solid basis on which to interpret results. According to genetics professionals, the main issue is not the sequencing itself. Rather, what matters most is the interpretation of the results intended to give clinical significance. This has turned out to be the most costly activity, both in terms of the recruitment of highly qualified personnel and for the development, maintenance, and access to huge databases that list the known variants of specific genes. Certain professionals estimate that there is a 1 to 10 ratio with regards to the cost of complete genome sequencing and its interpretation. In this context, ownership and the opening up of genetic variants databases emerges as a crucial issue.

From this context, the example of the future of the UMD BRCA base—Universal Mutation Database-BRCA—speaks volumes. Developed in the 1990s by a public consortium of French geneticists, it was considered to be one of the most important global databases until 2015. Driven by two major players in genetic testing in the United States [Quest Diagnosis and Laboratory Corporation of America (LabCorp)], the database was partially privatized in 2015. These two companies purchased the right to obtain access to data in exchange for funding the database. While the French sought to finance over the short- and medium-term an activity that had become too costly for public finances to sustain, the Americans' objective was to quickly be able to compete with Myriad Genetics by improving the quality of their analyses. The question that arises, then, is: How will this be handled over the long term? Will the French geneticists at the origin of the database still be able to access it? Will French patients still benefit from the knowledge generated thanks to the data they provided? What justifies this privatization if we consider the donations made by patients who agreed to have their data kept in this database? Similar questions had already been raised by the NHS during its negotiations with Myriad in the early 2000s, when the issue of the privatization of access to BRCA testing for British citizens arose (16). Questions revolving around access (currently and in the future) to genetic databases thus remain relevant.

Conclusion

At a time when the opening up of public data has become common practice in the field of administration (38), the example of the genetics of breast cancer shows that data sharing is still a major issue in research (39). The question here is the extreme overlapping of public issues and private interests. In this case, there is a need to go beyond a simple comparison between the open regimes of data publication associated with academic institutions, and the closed regimes of the privatization of knowledge developed by business communities. Hybrid forms of database ownership such as those mentioned earlier, highlight the need to pay attention to the significance given to data sharing during the initial negotiations underpinning their establishment. Once these databases are filled by voluntary citizens who provide their DNA data, data sharing becomes a crucial issue in terms of technical democracy (40). Once again, however, citizens seem to be largely absent from the debate about the ownership and use of the genomic data stored in these databases. With increased power given to major programmes seeking to collect big data in genomics, it may be time to reflect on how citizens can be informed and involved in the decisions that will be made in this area.

At the very least, it seems necessary to provide people with information about the future of their genomic data: in which databases will the data be stored? For how long? Who will be able to use them? Can they be exploited for commercial purposes by private firms? As in the field of the Internet, database contributors should be able to oppose the reuse of their “data” for the benefit of private interests. The information challenge involves the very value of consent (41).

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Funding

This opinion paper is based on a research funded by the Fonds Avenir/Masfip pour la Recherche, 2016.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author would like to thanks Emmanuel Rial-Sebbag, S. de Montgolfier, Pr Dominique Stoppa-Lyonnet, Pr Eric Vilain, and Dr. Zaki El Haffaf for their help and collaboration. Thank you to Catherine Davies from UBO BTU for the translation.

Footnotes

1. ^For example: Human Gene Mutation Database (HGMD) or Online Mendelian Inheritance in Man (OMIM).

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Keywords: big data, genomics, BRCA, oncogenetics, database

Citation: Derbez B (2018) Big Data Sharing: A Crucial Democratic Issue for Genomic Medicine. Front. Public Health 6:334. doi: 10.3389/fpubh.2018.00334

Received: 03 July 2018; Accepted: 31 October 2018; Published: 28 November 2018.

Thomas Lefèvre, Université Paris 13, FranceEdited by:

Nicole C. Nelson, University of Wisconsin-Madison, United StatesReviewed by:

Copyright © 2018 Derbez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Benjamin Derbez, benjamin.derbez@univ-brest.fr

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher."

This published article is also available on ResearchGate:

Source link:

Respected Doctor

Big data has three characteristics as follows:

1-Volume

It is the volume of data extracted from a source, which determines the value and capabilities of the data to be classified as big data, and by the year 2020, cyberspace will contain approximately 40,000 megabytes of data ready for analysis and information extraction.

2-Variety

It means the diversity of extracted data, which helps users, whether they are researchers or analysts, to choose the appropriate data for their field of research and includes structured data in databases and unstructured data (such as: images, clips, audio recordings, videos, SMS, call logs, and data). Maps (GPS), and require time and effort to prepare them in a suitable form for processing and analysis.

3-Velocity

It means the speed of producing and extracting data and sending it to cover the demand for it. Speed is a crucial element in making a decision based on this data, and it is the time we take from the moment this data arrives to the moment the decision is made based on it.

There are many tools and techniques that are used to analyze big data, such as: Hadoop, Map Reduce, HPCC, but Hadoop is one of the most famous of these tools. Big data is on several devices and then distributes the processing process to these devices to speed up the processing result and is returned or called as a single package. Tools that deal with big data consist of three main parts:

1- Data mining tools

2- Data Analysis Tools

3- Tools for displaying results (Dashboard).

Its use also varies statistically according to the research objectives (improving education, effectiveness of decision-making, military benefit, economic development, health management ... etc.).

greetings

Senior lecturer

Nuha hamid taher

Dear Srdjan Atanasijevic,

Yes. You pointed to the important conditions for the development of big data analysis technology with the use of Big Data Analytics.

Thank you, Regards,

Dariusz Prokopowicz

Hi Driss,

You could check out the free datasets listed on www.imagingQA.com at the following link :

If you don't find what your looking for, its free to join so please open a new discussion topic (there are lots of data scientists and imaging specialists in the community) :

you can download the COS2021.hsrdb for XRD Database from the link below;

its 6.5Gbs.

Informative question

Currently any scientific topic relating closely or remotely to Covid19 is "in the buzz", extremely popular.

Vaccine and their properties and effects, propagation of the contamination, comorbidity statistics, this is on the radar of everyone in the broadest audience possible for science.

The popularity of the topics and anxiety around related issues additionallly creates a window for anti-science: fact-negationists, people opposing vaccines whereas vaccines have demonstrated huge benefits not obtainable otherwise (quasi eradication of polyomelitis, tuberculosis, etc), witchcraft recipe mongers (like a former US president who recommended injection of toilet desinfectant or something not far from it. Horrible effects to be expected).

It is likely that anything tagged "Covid19" becomes highly impopular once the pandemic will be over (thus belonging to "bad memories" not to be recalled with any pleasure).

In the longer term popular interest for Space science keeps growing, with impressive achievements from many missions, Europe, Russia, China, USA, Japan, India, etc.

This is a gem, where everyone sees common interest.

Climate science with its degree of probabilistic scenario calculation not understood by all is progressing its audience, especially with young people becoming more aware of huge risks for their future in the absence of immediate radical changes in the way we live on this planet earth.

I am on the same line as Xavier Rouard.

You can try Dr. Duke's Phytochemical and some other ethnobotanical Databases.

I would recommend Distributed Postgres using the Timescaledb extension. No DaaS provides this because the licensing prohibits it, however technology enablers such as Full Stack Engine. my firm, provide this as an implicit operator for Kubernetes. You wind up launching stacks like this:

You can do so in our cloud, which is free for life with few special conditions:

* Your research contributes to our open collective non-profit if plausible/possible

* You operate only stacks we approve of for the sake of security & performance on shared systems

Pretty lax in terms of who uses it, I'm not even tapped into the full power and it's got plenty of tenants. That could change, but for now it remains free. Just ping me and I can set you up for free with a Github Username.

Ping @large.systems on discord: https://lmg.systems/discord

Relevant:

You can find relevant information in this file:

Use M.S Excel to arrange data in csv file

It is very easy

please , could you provide us a file sample which could be used for STRUCTURE program when dominante marker (1/0) data is analysed.

Thank you so much

hi this may be useful

1) https://datascience.nih.gov/covid-19-open-access-resources

2) https://en.unesco.org/covid19/communicationinformationresponse/opensolutions

3) https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge

Publicly and privately available dataset links and related papers for statistics.

CACD Dataset: https://bcsiriuschen.github.io/CARC/

MORPH: https://ebill.uncw.edu/C20231_ustores/web/store_main.jsp?STOREID=4

CLF: https://ieeexplore.ieee.org/document/8987410

https://ieeexplore.ieee.org/document/9211009

ITWCC: https://www.osti.gov/servlets/purl/1559665

John Hopkin database for COVID 19 might have the resources you are looking for,

check this link:

I've recently published o public dataset. It contains 2690 images of lemons with annotations. For more information please visit: https://github.com/softwaremill/lemon-dataset

Dear Shafagat Mahmudova, Len Leonid Mizrah, Reema Ahmad, Shah Md. Safiul Hoque, Natesan Andiyappillai, Omar El Beggar, Tiroyamodimo Mmapadi Mogotlhwane, Thank you very much for participating in this discussion and providing inspiring and informative answers to the above question: What will Big Data be like in the future? Thank you very much for the interesting information and inspiration to continue deliberations on the above-mentioned issues. This discussion confirms the importance of the above-mentioned issues and the legitimacy of developing research on this subject. I also believe that the Big Data Analytics analytical and database technology is one of the most developing technologies included in Industry 4.0. What do you think about it?

Thank you very much and best regards,

Dariusz Prokopowicz

The alternatives are:

Thomson reuters Eikon: https://eikon.thomsonreuters.com/index.html

LexisNexis: https://www.lexisnexis.com/bis-user-information/dossier/

However, those databases require you to have an account. Are you requiring data for private companies?

Some countries allowing you to buy the audited company report that is listed in the stock market.

You can use Postgresxl for Relational or DGraph for Graphical Distributed Environment. Both these tools can be used in virtual or physical mode. You may use Row (Postgresql) and Column (MonetDB) stores for data partitioning labs.

I would like to recommend you to go through two of my articles:

1. B.K.Tripathy and Sooraj, T. R.: An Interval Valued Fuzzy Soft set Based Optimization Algorithm for High Yielding Seed Selection, International Journal of Fuzzy Sets and Applications, IGI publications, vol.7, issue 2, (2018), pp. 44 - 61.

2. B.K.Tripathy, Sooraj, T. R.: Optimization of seed selection for higher product using Interval valued Hesitant Fuzzy Soft Sets, Songklanakarin Journal of Science and Technology (SJST), 40 (5), Sep. -Oct. 2018, (2018), pp.1125-1135 .

Moreover, consider that PostgreSQL with the spatial PostGIS extension is entirely open-source and it is developed by a strong and active community. I therefore definitely recommend you to go with PostGIS and not to use proprietary stuff such as all the ESRI products.

In addition, PostgreSQL is - despite being a relational DBMS - offering some object-oriented features such as hstores or jsonb which allows you to extend your data model - if required - to unstructured data.

Dear Giorgio,

There are number of websites that offer freely/publicly available historical meteorological datasets. Links for few of them I have mention below:

You may find it useful.

Cheers

Ankur

If you're using Windows, the "copy" command should help (files should be in the same folder), using a Command prompt:

copy *.ris db.ris

If you're using Ubuntu Linux and can move all the ris files inside a folder, just open a terminal in the folder and use "cat":

cat *.ris > db.ris

There are more complex commands, but you can move files in a folder easily if you search for them by extension and cut/paste.

The files are basic text files, so it works for other text files of interest (i.e. CSV).

I used this to import some stuff to my JabRef database... seems like the new version of JabRef doesn't like Drag&Drop and can't select multiple files for import.

p.s. there's a lovely extension for browsers called: BibItNow

I wonder why you need to use the Delphi software. If you simply want to conduct a small-scale Delphi study (less than 50 experts), it is no need to make use of any specific Delphi program. Just follow a Delphi methodology starting from identifying experts, through questionnaire design, data collection (the number of rounds) and data analysis, to data interpretation. It is quite sad that researchers nowadays cannot carry out research without a research-assisted program. In fact, we should focus more on research philosophy and methodology with reporting guidelines than the IT or software.

OECD, UNCTAD, Groningan, Penn World data and Federal Reserve Economic Data FRED St. Louis Fed,

IMDB-WIKI: (523,051)

FG-NET: (1,002)

MORPH: (55,134)

CACD: (163,446)

LAP : ( 4,691)

Italy, web, data on symptom onset, ministry of the intern: http://www.salute.gov.it/portale/nuovocoronavirus/homeNuovoCoronavirus.jsp?lingua=english

Chandrasekar Subramani Narayana you have here an example of ground truth appearing, which is not the official data of a government, but the analysis from very professional independent journalists and the official institute of statistics for the UK, ONS:

-the government trailed at less than 30 000 deaths

-the ONS published data, which means 40 000 deaths

-The Guardian (and Reuters) picked it up

https://www.theguardian.com/world/2020/may/12/uk-coronavirus-death-toll-passes-40000-official-figures-say

-the government sticked to their figure.

Is it typical? The public gets more reliable information from sources other than government?

Another case was PPE, equipment for health staff. The government said "all good" , the official voice of the NHS (health care) said "missing masks and equipment almost everywhere"

Yet another controversy: tests

The government set a target of 100 000/day. They reported earlier confusing things to avoid saying they had failed to meet this.

Puzzling is it not?

For sure we now know who should not be trusted, but it should not be that way, not in a democracy.

Or, put it otherwise, is it not transparency and accuracy of information which characterises a democracy?

where can i find the data dictionary for the clinical parameters for this data? I could not find on physionet site..Thanks for any help

Karim Heidari

Hi, I downloaded from zinc library on windows and on Linux systems.

for windows, I select the desired LogP and molecular weight and by download, it gives me a text file with Linux then I copied them and used it with internet download manager I downloaded approximately million compounds by this step it was giving me rar files I just decompressed them.

for Linux as II remember I used WGET program Itext was using the same text file that I get from zinc with this program.

best regards.

If you are interested in a particular gene, UCSC genome browser HAIB Methyl RRBS Track (ENCODE at UCSC Downloads Subtracks⇓ Description⇓ Contact⇓ HAIB Methyl RRBS Track Settings) is a good place, too.

As I didn’t find any database of tetraplex structures, I decided to create my own. I then used it in the package G4-iM Grinder.

In the latest update (V1.5.9) G4-iM Grinder’s internal database (V.2.5.1) has been expanded to a total sum of 2851 already known-to-form or known-not-to-form quadruplex nucleotidic sequences.

- 2141 of these sequences form tetraplex and 710 don’t.

- 283 are i-Motifs and 2568 are G4s.

- 1858 are DNA and 993 are RNA.

This database is used by the algorithm to locate any known tetraplex within its results.

However, the database is available for anyone to use.

[GiG.DB within G4iMGrinder package]

The database also includes references for each entry and some biophysical results (which will be soon expanded).

Hope this helps.

It's around 42 per cent in average currently. More Dax than M-Dax. Quote me if you need a reference.

Hi Ahmad,

You can find some resources in the answers to this similar question: https://www.researchgate.net/post/Is_there_a_list_of_human_SNPs_associated_with_a_disease

Some examples:

PehGenl (Phenotype-Genotype): Users can search based on chromosomal location, gene, SNP, or phenotype and view and download results including annotated tables of SNPs, genes and association results, a dynamic genomic sequence viewer, and gene expression data. https://www.ncbi.nlm.nih.gov/gap/phegeni

NHGRI-EBI Catalog of published genome-wide association studies https://www.ebi.ac.uk/gwas/home (for example: https://www.ebi.ac.uk/gwas/search?query=rs7329174)

PharmGKB, a pharmacogenomics knowledge resource that encompasses clinical information including clinical guidelines and drug labels, potentially clinically actionable gene-drug associations and genotype-phenotype relationships. https://www.pharmgkb.org/

I hope this helps

ECOLOGICAL INFORMATICS

https://www.journals.elsevier.com/ecological-informatics

Akanksha Sharma Link Districts/ Blocks with census of India data.

There are many data in original cif file which makes it too heavy to ne uploaded in crystal explorer. In order to remove those data its better to open your file with mercury and save it in .cif format.

Best links for computer vision databases are:

1) https://www.robots.ox.ac.uk/~vgg/ (click on data and search the emotional data).

2) https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge

3) http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm

Good Luck.

You can search the Genome Browser mailing list archives and/or email them directly here:

Ferran Oró Arán hello!

factoextra is an R package for extract PCA data.

You can use PCA scores directly in glm model:

library(tidyverse)

set.seed(1234) #repro

df <- as_tibble(replicate(expr=rnorm(100),n=5)) #V1,V2,V3,V4,V5

pca <- FactoMineR::PCA(df[-1], graph = FALSE) #use V2:V5 in PCA

score <- as_tibble(factoextra::get_pca_ind(pca)$coord) #extract individual scores

mod <- cbind(df[1], score[1:2]) #use V1 as DV, use Dim.1:Dim.2 as IV

glm(V1~Dim.1+Dim.2, data = mod) #simple linear model

Hi I wonder whether you have found the updated siRNA database?

Hi Ali Motahharynia ,

Actually you can use those databases that you mentioned to conduct research and understand mechanisms of brain activity in health and disease in computational neuroscience!

We recently used KEGG and STRING to study gene networks in different psychiatric diseases.

However, if you are mainly interested in using fMRI and imaging databases in your research, please check the following as well:

1. Blue Brain project by EPFL has a great database in at circuit level that can be used:

2. Human Connectome Project (HCP) is a great database comprised of scans and analyses of more than 1100 subjects:

3. Allen Brain Atlas

4. BIRN: https://openfmri.org

and many other databases that you may find and use depending on your main focus in neuroscience.

Congratulations on your high response rate. I guess the follow up will be via email as well and online FU's or online recruitment is not somethin I am familiar with but I guess the main thing is to send out the FU reuqest early enough for the participant to get back to you as well. Also - we get so many emails from different companies that unless it is somethin we are expecting or someting that were familiar with we may just skip past the email without realising. So - you could send out an initial email - reminding the participants of the upcoming questionnaire and then send out another one with the link attached. In both communications, make sure you make it clear in the email subject / header that this email is regarding study / project that they agreed to participate in to jog their memory and to make it stand out from all their other emails. Hope this helps

Tarik Kabak ... here's a link for free download

http://www.mediafire.com/file/22n4509sdoage7a/PDF2.rar

Good Luck

Hey

SCINOTE

Hi

I recommend the Human Protein Altas (HPA) database which is visualized on its website.

If you search one specifc gene into HPA, the website could show the RNA expression level of the gene in various cancer cell line And different types of cancer. Moreover, the website (the Pathology category) also showed the Protein expression level (IHC) In different types of cancer (Human Cancer tissues). In addition, the website also show the cellular location of the gene you input.

With regards

Geoffrey M. Bonnin

Success! I called the customer service line and the nice woman knew exactly what I was talking about. For whatever reason the UI they are using does not have past 2014. But on the back end where you request the data it has all the most recent data. See it here: https://www.ncdc.noaa.gov/cdo-web/datatools/lcd

these indices have a range of values between -1 and +1 so if your values do not fall within these ranges, then there's a problem. The value also depends on the vegetation situation of your land-cover types. Healthy vegetation, for instance, have very high positive NDVI values and vice versa. A good understanding of the vegetation dynamics in your study area is also necessary to know the correctness of the your NDVI or NDMI values.

Dear Ali Akbar Jamali ,

Look the link, maybe useful.

Regards,

Shafagat

Based on chapter 2 and 9 of the book called "Applied linear statistical Model" (John Netter), the regression analysis with observed data couldn't give information about cause-and-effect problems. However, if a regression analysis is performed with experimental data (experimental study), then the regression can give information about cause-and-effect relations. In such cases, the effect of latent variables on the response variable reaches its minimum due to randomization in the experimental study and the causality effect can be examined.

The survival analysis is the special kind of regression analysis and if the study is experimental, the causality inference could be analyzed.

Thanks Shan Thomas i will check it out, looks like a more convenient solution than trolling through papers

Automated alge identification using neural networks is a great idea. I played with it few year ago as well :-).

Besides sources mentioned in previous answers https://atlasofcyanobacteria.com/index.php coud be another one.

In general, it is necessary to have reliable and double-verified identifications of micrographs used for machine learning.

I would be a bit cautious with mixing natural material images and photos of cultures, which may look quite different.

Good luck!

I suppose it all depends on your study species. For the most part, I think experts in most fields are able to identify the species they're most knowledgeable about with relatively high accuracy, given they have enough information in the photo and geographic location to do so.

It's usually when someone gets a bit overzealous and identifies something to the species level when given minimal information and just going off of an educated guess for species most likely to be in the area.

It also depends on what the question for your study is. If you're doing an SDM for a species, you could always thin the records to about 100 and then self-verify (if you're confident in your abilities to do so). You could see if the species occurrence data has any corresponding NCBI molecular data and use DNA to verify species.

If you're using a dataset of 1000 + (or some other number where it isn't feasible to self verify each account) from inaturalist, you could query the data with >3 verified ID agreements with no "maverick" or disputed IDs. The likelihood of obtaining false positives should decrease with user agreement on a species identification.

C K Gomathy & Stephanie Tonn Goulart Moura - thank you very much for coming back to me

Please find below a link a dataset of intracranial hemorrhage segmentation.

http://alpha.physionet.org/content/ct-ich/1.0.0/

I know my reply is too late, but just in case, other research are looking for similar datasets.

TB PCR it is Gene Xpert is the latest WHO recommended diagnostic.It is 90% sensitivity test for pulmonary tuberculosis.

Sidra Rafi

Sorry I did not read completely the question before. I read again, and it now seems to be more stupid then it appeared before.

Hello Niek van der Schaaf,

If you can use Excel, the transformation you want is a very straightforward application of Pivot Table. You basically select all your data, Ctrl+a on your spreadsheet, then Insert >> Pivot Table. You can then choose Date as rows, your company ID as columns and the variable of interests in values. (attached the illustration from your own file)

To some extent, I believe Open Office does it too.

This creates heavy datafile, you can copy-paste in value your Pivot Table to work with later (Paste Special >> Values).

Check any tutorial on Pivot Table, it's a basic and useful Excel function.

Then if you need more professional work, it is a better practice to code your analysis so that all steps of data manipulation are recorded: you can check online how to pivot data with R, Python, or other tools (I guess Stata and SPSS must do that quite directly too).

I hope this is helpful,

Good Luck with your work,

Mathieu

I am aware of research on the relationship of mathematics skills and musical ability. That may give you a start. see the link:

Best, David Booth

Anything you can to us Dennis Mazur ?

Yes I think there is no short cut.