Observations for Digital Florula - Las Cruces Biological Station

The "Protein Data Bank (PDB) archive" was established in 1971 as the first open-access digital data archive in biology. It is a collection of three-dimensional (3D) atomic-level structures of biological macromolecules (i.e., proteins, DNA, and RNA) and their complexes with one another and various small-molecule ligands (e.g., US FDA approved drugs, enzyme co-factors). For each PDB entry (unique identifier: 1abc or PDB_0000001abc) multiple data files contain information about the 3D atomic coordinates, sequences of biological macromolecules, information about any small molecules/ligands present in the entry, details about the structure-determination experiment, authors and publication information, experimental data, and the wwPDB validation report. Additional content stored in the archive includes documentation, summary reports, and software (among others). The PDB is a jointly-managed core archive of the Worldwide Protein Data Bank partnership [RCSB Protein Data Bank (RCSB PDB, rcsb.org); Protein Data Bank in Europe (PDBe, pdbe.org); Protein Data Bank Japan (PDBj, pdbj.org); Electron Microscopy Data Bank (EMDB, emdb-empiar.org); and Biological Magnetic Resonance Bank (BMRB, bmrb.io)]. RCSB PDB serves as the wwPDB-designated Archive Keeper for the Protein Data Bank. Additional wwPDB Core Archives are as follows: Electron Microscopy Data Bank (wwPDB-designated Archive Keeper: EMDB) Biological Magnetic Resonance Bank (wwPDB-designated Archive Keeper: BMRB)

Beginning in late 1979, the Alabama Coastal Area Board (CAB) funded a series of baseline surveys on the coastal resources of Alabama, from which they could develop a monitoring program to observe any significant changes in the resources over time. Eight stations within Mobile Bay, Alabama were sampled monthly from April 1980 to April 1981. Data collected included samples for benthic fauna, pelagic fauna, sediment particle size, total organic carbon, foraminifera, zooplankton, phytoplankton, chlorophyll, turbidity, river flow, and hydrographic parameters. The subset of data presented here are for the benthic fauna, which were sampled by 0.1 m^2 Peterson grab. Fauna were enumerated and identified to the lowest taxon possible, and mainly included crustaceans, molluscs, polychaetes, and echinoderms. Data in readily accessible digital form are available from April 1980 to February 1981.

Background: When overlapping sets of genes encode multiple traits, those traits may not be able to evolve independently, resulting in constraints on adaptation. We examined the evolution of genetically integrated traits in digital organisms—self-replicating computer programs that mutate, compete, adapt, and evolve in a virtual world. We assessed whether overlap in the encoding of two traits – here, the ability to perform different logic functions – constrained adaptation. We also examined whether strong opposing selection could separate otherwise entangled traits, allowing them to be independently optimized. Results: Correlated responses were often asymmetric. That is, selection to increase one function produced a correlated response in the other function, while selection to increase the second function caused a complete loss of the ability to perform the first function. Nevertheless, most pairs of genetically integrated traits could be successfully disentangled when opposing selection was applied to break them apart. In an interesting exception to this pattern, the logic function AND evolved counter to its optimum in some populations owing to selection on the EQU function. Moreover, the EQU function showed the strongest response to selection only after it was disentangled from AND, such that the ability to perform AND was lost. Subsequent analyses indicated that selection against AND had altered the local adaptive landscape such that populations could cross what would otherwise have been an adaptive valley and thereby reach a higher fitness peak. Conclusions: Correlated responses to selection can sometimes constrain adaptation. However, in our study, even strongly overlapping genes were usually insufficient to impose long-lasting constraints, given the input of new mutations that fueled selective responses. We also showed that detailed information about the adaptive landscape was useful for predicting the outcome of selection on correlated traits. Finally, our results illustrate the richness of evolutionary dynamics in digital systems and highlight their utility for studying processes thought to be important in biological systems, but which are difficult to investigate in those systems.

Currently, data-driven discovery in biological sciences resides in finding segmentation strategies in multivariate data that produce sensible descriptions of the data. Clustering is but one of several approaches and sometimes falls short because of difficulties in assessing reasonable cutoffs, the number of clusters that need to be formed or that an approach fails to preserve topological properties of the original system in its clustered form. In this work, we show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data. The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data. The resulting clustering approach only relies on metrics derived from the inherent properties of the clustering. The new method facilitates knowledge for optimised clustering, which is easy to implement.We discuss how the clustering optimisation strategy corresponds to the viable information content yielded by the final segmentation. We further elaborate on how the clustering results, in the optimal solution, corresponds to prior knowledge of three different data sets. This is the dataset and the codes required to conduct the above-mentioned analysis.

Occurrence details of New Zealand marine fauna and flora from around the coastline and offshore. Information is assimilated from a variety of sources including unpublished data sets and digitised from journal articles. Where the source is from a published paper the source paper citation is listed at the record level.Data was then assimilated from digital and non digital sources (such as journal publications, reports, work sheets) into a central dataset.Marine species occurrence data collated from research events along the coast and in New Zealand waters.Biological data was sampled in-situ using a variety of equipment such as trawls, pots, grabs, dredges, and beach surveys.The scientific names have been mapped to the World Register of Marine Species (WoRMS), using the online taxon match tool.All sampling locations have been plotted on a map to perform a visual check. The most important check would be to see if all sampling locations are (1) in the marine and/or brackish environment and (2) within the described sampling area.Citation: SWPRON (2014). Marine biological observation data from coastal and offshore surveys around New Zealand. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 5707 records. Online: https://nzobisipt.niwa.co.nz/resource?r=mbis_nz Released on April 17, 2015._Item Page Created: 2016-06-09 02:01 Item Page Last Modified: 2025-02-01 19:11Owner: NIWA_OpenDataMBIS_NZNo data edit dates availableFields: id,modified,language,bibliographicCitation,references_,institutionCode,collectionCode,ownerInstitutionCode,basisOfRecord,dynamicProperties,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,associatedTaxa,eventID,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,identifiedBy,typeStatus,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID

An online open-access open-data journal, publishing ''big-data'' studies from the entire spectrum of life and biomedical sciences whose publication format links standard manuscript publication with its affiliated database, GigaDB, that hosts all associated data, provides data analysis tools, cloud-computing resources, and a DOI assignment to every dataset. GigaScience covers not just ''omic'' type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale sharable data. Supporting the open-data movement, they require that all supporting data and source code be publicly available in a suitable public repository and/or under a public domain CC0 license in the BGI GigaScience database. Using the BGI cloud as a test environment, they also consider open-source software tools / methods for the analysis or handling of large-scale data. When submitting a manuscript, please contact them if you have datasets or cloud applications you would like them to host. To maximize data usability submitters are encouraged to follow best practice for metadata reporting and are given the opportunity to submit in ISA-Tab format.

This folder contains raw and processed data for all of the figures appearing in Ramsey and Pence, "evoText: A new tool for analyzing the biological sciences," Studies in History and Philosophy of Biological and Biomedical Sciences. All raw data was generated using evoText, as was the word cloud in Figure 1. Graphs in Figures 2 and 3 were generated using Microsoft Excel.

Dataset links to the Digital Collections of Colorado, DSpace Repository. From the homepage, you can search the 1240 datasets hosted there, or browse using a list of filters on the right. DSpace is a digital service that collects, preserves, and distributes digital material. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/ShortgrassSteppe_eaa_2015_March_19_1220

Marine visual imaging has become a major assessment tool in the science, policy and public understanding of our seas and oceans. The technology to acquire and process this imagery has significantly evolved in recent years through the development of new camera platforms, camera types, lighting systems and analytical software. These advances have led to new challenges in imaging, including storage and management of `big data, manipulation of digital photos, and the extraction of biological and ecological data. The need to address these challenges, within and beyond the scientific community, is set to substantially increase in the near future, as imaging is increasingly used in the designation and evaluation of marine conservation areas, and for the assessment of environmental baselines and impact monitoring for maritime industry. We review the state of the theory, techniques and technologies associated with each of the steps of marine imaging for observation and research, and to provide an outlook on the future from this active scientific and engineering community that develops and uses it.

This collection comprises two sets of transcripts of semi structured interviews, one with biological recorders (identified with ‘P’ codes) and the other with end users of biodiversity data collected by biological recorders (Identified with ‘DEU ‘codes). These interviews were recorded in 2021 as a part of the DECIDE project. The biological recorder interviews were collected to understand the motivations and practices of biological recorders with the intention of finding points of leverage where behaviours might be modified to fill gaps in existing data. The end user interviews were designed to understand attitudes to modelled biodiversity data and how this might be used in the work of the interviewees. This information was used to develop use cases for modelled data and to produce data products to fit those use cases. The interviewees were drawn from two areas of the UK, however this information is redacted in order to protect the anonymity of the participants. The DEU interview set contains 33 interviews. The biological recorder interview set contains 32 interviews. Interview topic guides are included alongside the interview transcripts, together with a document showing the participant types in the DEU interviews. Interviews lasted approximately 0.5-1.5 hours.

Biodiversity is under increasing pressure, with consequent impacts on the benefits people gain from nature. This means that it is vital to include biodiversity in our decision-making and for this we need high quality, fine-resolution, spatial biodiversity information. With this information we can better value nature, and this can be done formally through a process called 'natural capital' assessment, such as by government agencies or local economic partnerships. We also need this information to develop better plans for protecting nature, undertaking ecological restoration to develop resilient ecological networks, and make good decisions about infrastructure development (to achieve net biodiversity gain, as is the ambition in Defra's 25 Year Environment Plan). Much of our existing biodiversity information comes from volunteer-collected species records (a process often called 'citizen science'). However, in many cases, people record where and when they want - leading to large spatial unevenness in recording, both at a national scale and at a local scale. The people and organisations who need to use biodiversity information don't simply require more records: they require better information. This requires us to construct good biodiversity models generated from the available data, communicate these models well, and preferentially target effort to add records from times and places that optimally improve the model outputs. This project seeks to achieve all of this by addressing three important questions. Firstly, can we enhance existing biodiversity information through near real-time, fine resolution, species distribution models? Secondly, can we make biodiversity information more accessible and useful to end users through data flows and automated data communication? Thirdly, can we encourage adaptive sampling behaviour in recorders, by using intelligent digital engagements, so that they re-deploy a portion of their effort to optimally improve biodiversity models? Our team is expertly placed to address these questions because we are a multidisciplinary team (environmental, computer, social and data scientists), and we will use a service design approach that actively engages data users (from national to local levels) and biodiversity recorders alongside the research team. In this project we will produce fine-resolution distribution models for about 1000 insect species across the UK (in this study focusing on butterflies, moths and grasshoppers) using earth observation sensor data, and a data lab (an online analysis platform) to automatically update outputs as new data are available. It is important to communicate these results and their uncertainty so, in collaboration, with data end users we will develop interactive and automatically-generated visualisations and text to do this effectively. We will also develop ways of assessing when and where new data will be most valuable in improving the model outputs. This, when combined with constraints (such as land access or people's recording preferences) will be communicated to recorders as bespoke recommendations via a web app. This will be developed for recording butterflies and grasshoppers (a sunny day activity), and recording moths (supported by our provision of portable, low cost light traps). We will engage recorders through established recording projects across the UK, including with partners in London (many people, but relatively few biodiversity data) and North and East Yorkshire (fewer people, and a wide variety of land uses). Throughout this project our work flows will be implemented in an data lab, so they will be flexible for use with any species and indeed could be adapted for any environmental...

Specimenes for Digital Florula - Las Cruces Biological Station

A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large—but not intermediate-sized—populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A facility to help novices and experts find information about plants and animals in digital collections. The objectives of the Project are to facilitate access to online flora and fauna by both novices and experts through enhanced indexing, searching, and visualization techniques. Specific search facility and content will be added to help users with different levels of domain knowledge identify species based on the augmentation of professionally developed taxonomic treatments or species descriptions. This is a novel use of taxonomic descriptions.

A dynamic archive of information on digital morphology and high-resolution X-ray computed tomography of biological specimens serving imagery for more than 750 specimens contributed by almost 150 collaborating researchers from the world''s premiere natural history museums and universities. Browse through the site and see spectacular imagery and animations and details on the morphology of many representatives of the Earth''s biota. Digital Morphology, part of the National Science Foundation Digital Libraries Initiative, develops and serves unique 2D and 3D visualizations of the internal and external structure of living and extinct vertebrates, and a growing number of ''invertebrates.'' The Digital Morphology library contains nearly a terabyte of imagery of natural history specimens that are important to education and central to ongoing cutting-edge research efforts. Digital Morphology visualizations are now in use in classrooms and research labs around the world and can be seen in a growing number of museum exhibition halls. The Digital Morphology site currently presents: * QuickTime animations of complete stacks of serial CT sections * Animated 3D volumetric movies of complete specimens * Stereolithography (STL) files of 3D objects that can be viewed interactively and rapidly prototyped into scalable physical 3D objects that can be handled and studied as if they were the original specimens * Informative introductions to the scanned organisms, often written by world authorities * Pertinent bibliographic information on each specimen * Useful links * A course resource for our ''Digital Methods for Paleontology'' course, in which students learn how to generate all of the types of imagery displayed on the Digital Morphology site

Descriptive statistics for the three administered surveys Iceland 2019/2020, Iceland 2023 and Native range 2021

This data set, 1 of 5 archived at the NODC, contains digital orthophotography. The data set presents information that represented conditions for the specified Digital Orthophoto Quarter Quads (DOQQ) regions of interest as specified by PhotoScience Task No. 01012C0053 for coastal areas of South Carolina. The project area was selected specifically to cover those sections of the South Carolina coastal critical zone where oysters had historically been mapped by SC Department of Natural Resources, Marine Division. Additional data from this collection is archived at the NODC under accession numbers 0084749, 0084750, 0084751, 0084752.

The extent of the DOQQs for the project area ranges from the Hilton Head area in the southern part of South Carolina to the Myrtle Beach area in the northern part of the state. The digital orthophotos in this series have a theoretical ground resolution of 0.25 meter. The digital orthophotos are 4-band in nature (red, green, blue, near infrared) and are delivered as flown in four-band .img file format with associated .png, .jpg, and indexing files. The four-band imagery is delivered in mosaics equaling one eighth of a DOQ. All data were captured during specific imaging windows per contract. The total DOQQ area is approximately 1,527 square miles.

Abstract The Oswaldo Cruz Foundation is home to significant cultural heritage comprising biological collections, buildings, and historical documents and objects denoted as strategic assets for preserving institutional memory as well as for teaching and research in the field of science and health. However, a major portion of these collections was unavailable to the public until 2014. This article introduces the process involved in providing full access to these collections and describes the challenges, solutions, and results. Finally, the management of physical and digital documents is indicated as a central component in constituting sources of information for the present and historical sources for the future.

The dataset contains details of the biota caught during plankton surveys around New Zealand and the south western Pacific although some occurrence data is included from other oceans sourced from the digitisation of journal articles from New Zealand authors. A large source from this dataset is provided by the New Zealand Ministry of Primary Industries from its 'plankton' and 'rocklob' databases. This dataset records all biological specimens collected during plankton sampling including fish, larvae, and eggs. This dataset does not include and data from Constant Plankton Recorders.Data was then assimilated from digital and non digital sources (such as journal publications, reports, work sheets) into a central dataset.Species occurrence data collated from research events using plankton sampling equipment in and around New Zealand waters.Biological data was sampled in-situ using a variety of equipment such as plankton trawls, vertical nets, water samples.The scientific names have been mapped to the World Register of Marine Species (WoRMS), using the online taxon match tool.All sampling locations have been plotted on a map to perform a visual check. The most important check would be to see if all sampling locations are (1) in the marine and/or brackish environment and (2) within the described sampling area.Southwest Pacific OBIS (2013). Biological observation data from plankton surveys around New Zealand. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 36723 records, Online https://nzobisipt.niwa.co.nz/resource?r=mbis_plankton released on June 22, 2014._Item Page Created: 2016-06-09 02:03 Item Page Last Modified: 2025-02-15 17:33Owner: mackayk_NIWAMBIS_planktonNo data edit dates availableFields: id,modified,language,bibliographicCitation,institutionCode,collectionCode,basisOfRecord,dynamicProperties,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,associatedTaxa,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,identifiedBy,typeStatus,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID

The time-lapse dataset was acquired on a PerkinElmer Operetta CLS, using a Plan Apochromat 20x N.A. 0.8 objective, and an Andor Zyla 5.5 camera ( pixel dimensions: 0.298 microns ) The time-points were acquired every 15 minutes, for 60 hours. HeLa cells expressing H2B-mCherry Red channel and Digital Phase Contrast (DPC) images. Note1: The 5 hours dataset corresponds to timepoints 59 to 78 (provided as a lighter alternative to the full dataset) Note2: The dataset corresponds to a part of the original field of view.