A catalog of high-value public science and research data sets from across the Federal Government.

The Portal Project Teaching Database is a simplified version of the Portal Project Database designed for teaching. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. The database is currently available in csv, json, and sqlite. This database is not designed for research as it intentionally removes some of the real-world complexities. The original database is published at Ecological Archives(http://esapubs.org/archive/ecol/E090/118/) and this version of the database should be used for research purposes. The Python code used for converting the original database to this teach version is included as 'create_portal_teach_dataset.py'. Suggested changes or additions to this dataset can be requested or contributed in the project GitHub repository(https://github.com/weecology/portal-teachingdb).

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Database of bibliographic citations of multidisciplinary areas that covers various journals of medical, scientific, and social sciences including humanities.Publisher independent global citation database.

Research findings indicate that programmed cell death (PCD) plays a pivotal role in the pathophysiology of spinal cord injury (SCI), and a recently discovered form of cell death, disulfidptosis, has emerged as a novel phenomenon. However, the characterization of disulfidptosis-related genes in SCI remains insufficiently explored. We retrieved SCI-related data from the Gene Expression Omnibus (GEO) database and identified three key genes associated with disulfidptosis in human SCI (CAPZB, SLC3A2, and TLN1), whose mediated signaling pathways are closely intertwined with SCI. Subsequent functional enrichment analysis suggested that these genes may regulate multiple pathways and exert corresponding roles in SCI pathology. Moreover, we predicted potential targeted drugs for the key genes along with their transcription factors and constructed an intricate regulatory network. CIBERSORT analysis revealed that CAPZB, SLC3A2, and TLN1 might be implicated in modulating changes within the immune microenvironment of individuals with SCI. Our study provides compelling evidence confirming the significant involvement of disulfidptosis following SCI while offering valuable insights into its underlying pathological mechanisms.

Data sets include 1. Excel file with Hershberger assay protocols and data and summaries of in vivo antiandrogen studies 2. Figures of in vitro AR assay results from contract work and in house studies 3. Excel file with in house in vitro AR antagonism data. This dataset is associated with the following publication: Gray, L., J. Furr, C. Lambright, N. Evans, P. Hartig, M. Cardon, V. Wilson, A. Hotchkiss, and J. Conley. Quantification of uncertainties in extrapolating from in vitro androgen receptor (AR) antagonism to in vivo Hershberger Assay endpoints and adverse reproductive development in male rats. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 176(2): 297-311, (2020).

.SCIENCE Whois Database, discover comprehensive ownership details, registration dates, and more for .SCIENCE TLD with Whois Data Center.

Data obtained from computational DFT calculations on Tetragonal ScI is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

Motivation is of great importance in the teaching-learning process, because motivated students seek out opportunities and show interest and enthusiasm in carrying out their tasks. The objective of this review is to identify and present the information available in the literature on the status quo of motivation among nursing program entrants. This is a qualitative scoping review study, a type of literature review designed to map out and find evidence to address a specific research objective, following the Joanna Briggs Institute methodology. The objective was outlined using the PCC (Population, Concept, Context) acronym. The protocol was developed and registered on the Open Science Framework (OSF) platform under DOI 10.17605/OSF.IO/EJNGY. The search strategy and database selection were defined by a library and information science professional together with the authors. The search will be carried out in the following databases: Cumulative Index to Nursing and Allied Health Literature, Literatura Latino Americana e do Caribe em Ciências da Saúde, Lilacs Esp, National Library of Medicine (PubMed), ScienceDirect, Scopus, and the Web of Science platform. The researchers will meet to discuss discrepancies and make decisions using a consensus model, and a third researcher will be tasked with independently resolving any conflicts. Data extraction will involve two independent researchers reviewing each article. Documents such as original articles; theoretical studies; experience reports; clinical study articles; case studies; normative, integrative, and systematic reviews; meta-analyses; meta-syntheses; monographs; theses; and dissertations in English, Portuguese, and Spanish from 2017 to 2023 were included. The results will be presented in tabular and/or diagrammatic format, along with a narrative summary.

A database of articles with Open Science Badges.

The database is available at https://www.zotero.org/groups/2146879/open_science_badges Accessing this group library using the desktop Zotero application allows for easy sorting of the articles by their badge tags.

Versions of this database complete up to March, 2018 are available in the historical database backups component. Tracking of the current coverage (which is not complete up to the current date) is available at https://docs.google.com/spreadsheets/d/1KbCGviuR-XxHC55a195o0nyGO0ugAylu-CbOqVCPGYE/edit?usp=sharing

Please contact Felix Singleton Thorn at felixs@unimelb.edu.au or Steven Kambouris at steven.kambouris@unimelb.edu.au with inquiries.

ObjectiveThis study aims to explore the clinical efficacy of mesenchymal stem cell (MSC) transplantation in the treatment of patients with spinal cord injury (SCI) through a network meta-analysis and to discuss the optimal transplantation strategy for treatment.MethodsWe conducted a computer search of clinical randomized controlled studies on MSC treatment for SCI in databases including PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Journal Database (VIP), Wanfang Database, and Chinese Biomedical Literature Service System (SinoMed) up to March 2024. Two researchers independently completed literature screening and data extraction according to the inclusion and exclusion criteria and used RevMan 5.4 software to assess the quality of the included studies. Network meta-analysis was performed using Stata 16.0 software.ResultsA total of 18 studies were included in the analysis. The results showed that MSCs significantly improved motor, sensory, and activities of daily living activities after SCI. Network meta-analysis indicated that umbilical cord mesenchymal stem cells (UCMSCs) were the most effective cell source, and intrathecal injection (IT) was the optimal transplantation method.ConclusionThe study suggests that the current use of UCMSCs for IT transplantation may be the best transplantation strategy for improving functional impairment after SCI. Further high-quality studies are still needed to validate the results of this study and to ensure the reliability of the results.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier [CRD42023466102].

Quant. Sci. Stud

APXS, ChemCam, DAN, Hazcam, MAHLI, MARDI, Mastcam, Navcam, RAD, REMS, SPICE, SAM

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

Nowadays web portals play an essential role in searching and retrieving information in the several fields of knowledge: they are ever more technologically advanced and designed for supporting the storage of a huge amount of information in natural language originating from the queries launched by users worldwide.A good example is given by the WorldWideScience search engine:The database is available at . It is based on a similar gateway, Science.gov, which is the major path to U.S. government science information, as it pulls together Web-based resources from various agencies. The information in the database is intended to be of high quality and authority, as well as the most current available from the participating countries in the Alliance, so users will find that the results will be more refined than those from a general search of Google. It covers the fields of medicine, agriculture, the environment, and energy, as well as basic sciences. Most of the information may be obtained free of charge (the database itself may be used free of charge) and is considered ‘‘open domain.’’ As of this writing, there are about 60 countries participating in WorldWideScience.org, providing access to 50+databases and information portals. Not all content is in English. (Bronson, 2009)Given this scenario, we focused on building a corpus constituted by the query logs registered by the GreyGuide: Repository and Portal to Good Practices and Resources in Grey Literature and received by the WorldWideScience.org (The Global Science Gateway) portal: the aim is to retrieve information related to social media which as of today represent a considerable source of data more and more widely used for research ends.This project includes eight months of query logs registered between July 2017 and February 2018 for a total of 445,827 queries. The analysis mainly concentrates on the semantics of the queries received from the portal clients: it is a process of information retrieval from a rich digital catalogue whose language is dynamic, is evolving and follows – as well as reflects – the cultural changes of our modern society.

This Level 1 (L1) dataset contains the Version 2.1 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.0. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. The Version 2.1 release represents the second science-quality release. Here is a summary of improvements that reflect the quality of the Version 2.1 data release: 1) data is now available when the CYGNSS satellites are rolled away from nadir during orbital high beta-angle periods, resulting in a significant amount of additional data; 2) correction to coordinate frames result in more accurate estimates of receiver antenna gain at the specular point; 3) improved calibration for analog-to-digital conversion results in better consistency between CYGNSS satellites measurements at nearly the same location and time; 4) improved GPS EIRP and transmit antenna pattern calibration results in significantly reduced PRN-dependence in the observables; 5) improved estimation of the location of the specular point within the DDM; 6) an altitude-dependent scattering area is used to normalize the scattering cross section (v2.0 used a simpler scattering area model that varied with incidence and azimuth angles but not altitude); 7) corrections added for noise floor-dependent biases in scattering cross section and leading edge slope of delay waveform observed in the v2.0 data. Users should also note that the receiver antenna pattern calibration is not applied per-DDM-bin in this v2.1 release.

Gms-index-mediator is a standalone index for spatio-temporal data acting as a mediator between an application and a database. Even modern databases need several minutes to execute a spatio-temporal query to huge tables containing several million entries. Our index-mediator speeds the execution of such queries up by several magnitues, resulting in response times around 100ms. This version is tailored towards the GeoMultiSens database, but can be adapted to work with custom table layouts with reasonable effort.

This document describes data collected from the Main Collection of the Web of Science database. Records of published studies addressing the intersection of Open Science and data repository were searched up to January 15th, 2024, and the final dataset was comprised of 545 records for bibliometric analysis.

This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities. The dataset is intended for use in machine learning model testing and algorithm validation. The properties calculated include solvation energies using the COSMO-RS method [1] and ionization potentials and electron affinities using various high-accuracy computational methods as implemented in MOLPRO [2]. Computational details can be found in Ref. [3], with scripts used to generate the data mostly uploaded to our github repository [4].

Molecular Datasets Considered:

• QM9 Dataset: Contains small organic molecules broadly relevant for quantum chemistry [5]

• Electrolyte Genome Project (EGP): Focuses on materials relevant to electrolytes.[6]

• GDB17 and ZINC databases: Offer a broad chemical diversity with potential application in battery technologies. [7, 8]

Data structure

How to Load the Data:

All files can be loaded with

import json

with open("file.json", "r") as f:
data_dict = json.load(f)

and the filestructure can be explored with

data_dict.keys()

Solvation energies

The data is stored in two types of JSON archives: files for full molecules of GDB17 and ZINC and files for amons of GDB17 and ZINC. They are structured differently as amon entries are sorted by the number of heavy atoms in the amon (e.g., all amons with 3 heavy atoms are stored in ni3). Because of the large number of amons with 6 or 7 heavy atoms,they are further split into ni6_1, ni6_2, and so on. A sub dictionary of an amon dictionary or a full molecule dictionary contains the following keys:

ECFP - ECFP4 representation vector

SMILES - SMILES string

SYMBOLS - atomic symbols

COORDS - atomic positions in Angstrom

ATOMIZATION - atomization energy in [kcal/mol]

DIPOLE - dipole moment in Debye

ENERGY - energy in Hartree

SOLVATION - solvation energy in [kcal/mol] for different solvents at 300 K.

Files:

GDB17.json.zip (unpack with unzip first!) - subset of GDB17 random molecules

AMONS_ZINC.json - all amons of ZINC up to 7 heavy atoms

EGP.json - EGP molecules

AMONS_GDB17.json - all amons of GDB17 up to 7 heavy atoms

Atomic energies $E_{at}$ at BP and def2-TZVPD level in Hartree [Ha]

We follow the convention of negative atomization energies for stablity compared to the isolated atoms:

$E_{atomization} = E_{mol} - \sum_{i} E_{at,i}$

Free energy of solvation at 300 K in [kcal/mol]:

Ionization potentials and electron affinities

The upload contains two JSON files, QM9IPEA.json and QM9IPEA_atom_ens.json. QM9IPEA.json summarizes MOLPRO calculation data grouping it along the following dictionary keys:

COORDS - atom coordinates in Angstroms.

SYMBOLS - atom element symbols.

ENERGY - total energies for each charge (0, -1, 1) and method considered.

CPU_TIME - CPU times (in seconds) spent at each step of each part of the calculation.

DISK_USAGE - highest total disk usage in GB.

ATOMIZATION_ENERGY - atomization energy at charge 0.

QM9_ID - ID of the molecule in the QM9 dataset.

All energies are given in Hartrees with NaN indicating the calculation failed to converge. Ionization potentials and electron affinities can be recovered as energy differences between neutral and charged (+1 for ionization potentials, -1 for electron affinities) species.

"CPU_time" entries contain steps corresponding to individual method calculations, as well as steps corresponding to program operation: "INT" (calculating integrals over basis functions relevant for the calculation), "FILE" (dumping intermediate data to restart file), and "RESTART" (importing restart data). The latter two steps appeared since we reused relevant integrals calculated for neutral species in charged species' calculations; we also used restart functionality to use HF density matrix obtained for the neutral species as the initial density matrix guess for the SCF-HF calculation for charged species. NaN CPU time value means the step was not present or that the calculation is invalid. Note that the CPU times were measured while parallelizing on 12 cores and were not adjusted to single-core.

QM9IPEA_atom_ens.json contains atomic energies used to calculate atomization energies in QM9IPEA.json, the dictionary keys are:

SPINS - the spin assigned to elements during calculations of atomic energies.

ENERGY - energies of atoms using different methods.

(Note that H has only one electron and thus does not require a level of theory beyond Hartree-Fock.)

NOTE: Additional calculations were performed between publication of arXiv:2308.11196 and creation of this upload. For the version of the dataset used in the manuscript, please refer to DOI:10.5281/zenodo.8252498.

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY 2030+). O.A.v.L. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772834). O.A.v.L. has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. O.A.v.L. acknowledges that this research is part of the University of Toronto’s Acceleration Consortium, which receives funding from the Canada First Research Excellence Fund (CFREF). Obtaining the presented computational results has been facilitated using the queueing system implemented at https://leruli.com. The project has been supported by the Swedish Research Council (Vetenskapsrådet), and the Swedish National Strategic e-Science program eSSENCE as well as by computing resources from the Swedish National Infrastructure for Computing (SNIC/NAISS).

References

[1] Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172, 43–72

[2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schutz, M. Molpro: a general-purpose quantum chemistry program package. WIREs Comput. Mol. Sci. 2012, 2, 242–253

[3] arxiv link of draft

[4] https://github.com/chemspacelab/ViennaUppDa

[5] Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014, 1, 140022

[6] Qu, X.; Jain, A.; Rajput, N. N.; Cheng, L.; Zhang, Y.; Ong, S. P.; Brafman, M.; Mag- inn, E.; Curtiss, L. A.; Persson, K. A. The Electrolyte Genome Project: A big data approach in battery materials discovery. Comput. Mater. Sci. 2015, 103, 56–67

[7] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enu- meration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 2012, 52, 2864–2875

[8] Irwin, J. J.; Shoichet, B. K. ZINC A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 2005, 45, 177–182.

IntroductionThe rising prevalence of complex chronic conditions and growing intricacies of healthcare systems emphasizes the need for interdisciplinary partnerships to advance coordination and quality of rehabilitation care. Registry databases are increasingly used for clinical monitoring and quality improvement (QI) of health system change. Currently, it is unclear how interdisciplinary partnerships can best mobilize registry data to support QI across care settings for complex chronic conditions.PurposeWe employed spinal cord injury (SCI) as a case study of a highly disruptive and debilitating complex chronic condition, with existing registry data that is underutilized for QI. We aimed to compare and converge evidence from previous reports and multi-disciplinary experts in order to outline the major elements of a strategy to effectively mobilize registry data for QI of care for complex chronic conditions.MethodsThis study used a convergent parallel-database variant mixed design, whereby findings from a systematic review and a qualitative exploration were analyzed independently and then simultaneously. The scoping review used a three-stage process to review 282 records, which resulted in 28 articles reviewed for analysis. Concurrent interviews were conducted with multidisciplinary-stakeholders, including leadership from condition-specific national registries, members of national SCI communities, leadership from SCI community organizations, and a person with lived experience of SCI. Descriptive analysis was used for the scoping review and qualitative description for stakeholder interviews.ResultsThere were 28 articles included in the scoping review and 11 multidisciplinary-stakeholders in the semi-structured interviews. The integration of the results allowed the identification of three key learnings to enhance the successful design and use of registry data to inform the planning and development of a QI initiative: enhance utility and reliability of registry data; form a steering committee lead by clinical champions; and design effective, feasible, and sustainable QI initiatives.ConclusionThis study highlights the importance of interdisciplinary partnerships to support QI of care for persons with complex conditions. It provides practical strategies to determine mutual priorities that promote implementation and sustained use of registry data to inform QI. Learnings from this work could enhance interdisciplinary collaboration to support QI of care for rehabilitation for persons with complex chronic conditions.