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17,699 results for “India”
Bhiwkund, Maharashtra, India. Main cave, veranda roof
<p>Bhiwkund, Maharashtra, India (21.052010, 79.45922). Main cave, veranda roof made of an iron-age megalith, showing circular markings, as documented 2/2015.</p>
Amarāvatī, Andhra Pradesh, India. Drawing of a stūpa pillar with relief sculpture and inscriptions.
<p>Amarāvatī, Andhra Pradesh, India. Drawing of a stūpa pillar with relief sculpture of the Buddh's final days, labelled with Brāhmī inscriptions.</p>
Archaeological Survey of India Collections: Burma Circle, 1903-07. Photo 1004/1 : 1903-1907 (.jpg format)
<p>This is a digitized collections of photos held by the British Library (catalog record photo 1004/1). These photos were taken by the Archeological Survey of India (Burma Circle) between 1903-1907. The digitization was conducted by the photography lab of the British Library as part of the Pyu epigraphy sub-project (PI, Nathan W. Hill of SOAS University of London) of the ERC synergy grant "Beyond Boundaries: Religion, Region, Language and the State" (Identifier: ASIA 609823). The original photographs are under crown copyright, which means that sufficient time has past for them to be distributed to the public. Here is the record from the BL--</p> <p> </p> <ul> <li><strong>Title:</strong> <p>Archaeological Survey of India Collections: Burma Circle, 1903-07. Photographer(s): Archaeological Survey of India</p> </li> <li><strong>Collection Area: </strong> Visual Arts</li> <li><strong>Reference: </strong> Photo 1004/1</li> <li><strong>Creation Date: </strong> 1903-1907</li> <li><strong>Extent and Access: </strong><br> <strong>Extent: </strong>418 items<br> <strong>Conditions of Use: </strong>Appointment Required to view these records. Please consult Asian and African Studies Print Room staff.</li> <li><strong>Language: </strong> Not applicable</li> <li><strong>Contents and Scope: </strong><br> <strong>Contents: </strong> <p>Blue half-leather album, 385x322mm, containing prints mounted on pages interleaved with tissue. The photographs were taken by the Archaeological Survey, Burma, between 1903-07, and listed in the annual Report of the Superintendent... Copies of each year's report precede the photographs for that year, as follows:</p> <p>Year Photo no</p> <p>1903-04 1-123</p> <p>1904-05 124-304</p> <p>1905-06 305-407</p> <p>1906-07 408-509</p> <p>The photographs, including views of Burmese architecture, sculpture and relics, were taken under the direction of the Superintendent, Archaeological Survey, Burma (previously known as the Government Archaeologist), a post held at the time by Taw Sein Ko.</p> <p>Process = Collodio-chloride prints</p> <p>Photographers = Archaeological Survey of India., ;</p> </li> <li><strong>History: </strong><br> <strong>Immediate Source of Acquisition: </strong> <p>Official Deposit</p> </li> <li><strong>Related persons, etc: </strong> Archaeological Survey of India, Unspecified</li> <li><strong>Related places: </strong> Mandalay, Myanmar, Asia, Unspecified</li> <li><strong>Related subjects: </strong> Archaeological Survey Of India Collections<br> Archaeological Survey Of India: Burma Series</li> </ul>
Sārnāth, Uttar Pradesh, India. Lintel, circa 475 CE.
<p>Sārnāth, Uttar Pradesh, India. Lintel, circa 475 CE. Now in the National Museum of India, no. 59.527/5.</p>
A study of comparative (2019-2023) trends and current acceleration in Particulate Matter (PM2.5) concentration in India
<p><span> For PM<sub>2.5</sub><span> </span>monitoring model, the data was procured from the Central Pollution Control Board’s functional and selected air monitoring stations. The data is available online at the <span> Central Pollution Control Board but in form of daily trends with numerous air quality monitoring stations in an area; monthly and Annual average level especially PM2.5 trends processed from the original data. </span></span></p>
Extra data to accompany code in GitHub burntfields_punjab, both used in Walker et. al. 2022, Detecting crop burning in India using satellite data
<p>Supplementary data files to accompany GitHub code 'burntfields_punjab' supporting Walker et. al. (2022) Detecting crop burning in India using satellite data [<a href="https://arxiv.org/abs/2209.10148">available here</a>] and Jack et. al. (2024) Money (not) to burn: Payments for ecosystem services to reduce crop residue burning).</p> <p>Includes custom Sentinel-2 cloud masks and data from Sentinel-2 Spectral Mixture Analysis to highlight Char (burning) based on general concept and methods from Daldegan et. al (2019). Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment 232, 111340. </p> <p>Note: Bands in weekly BASMA layers are: 0 = green vegetation, 1 = Non-productive vegetation and bare soil, 2 = Char (burned).</p> <p>further details are provided at: <a href="https://github.com/klwalker-sb/burntfields_punjab">https://github.com/klwalker-sb/burntfields_punjab</a> (archived at: <a href="https://doi.org/10.5281/zenodo.11225292" target="_blank" rel="noopener">DOI: 10.5281/zenodo.11225292</a>)</p>
Birdwatching, eBird and citizen science in India: qualitative interviews with participants, practitioners and ecologists
<h1>Abstract</h1> <p>This study consists of qualitative interviews about birdwatching, citizen science, and the use of the birdwatching data platform <em>eBird </em>in India. Interview partners are birdwatchers, citizen science practitioners, and ecologists who have used eBird data. Some of the main topics covered include: the nature of the birdwatching community and styles of birdwatching in India; the history of the adoption of eBird in India; the value of birdwatching and citizen science; challenges involved in conducting or participating in citizen science; opportunities and limitations of using data from eBird and citizen science; processes of data collection and quality control in eBird; and ecological research, conservation priorities, and environmental activism in India. This study is part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS).</p> <h1>Methods</h1> <p>The data in this study was collected using semi-structured qualitative interviews.</p> <p>Interview partners were recruited by snowball sampling through their engagement with eBird India and related organisations. There were 17 interview partners, interviewed either once or several times. 19 interviews were conducted in total.</p> <p>Interview guides/questionnaires were designed for each interviewee depending on their status as birdwatchers, citizen science coordinators, and eBird data users.</p> <p>Interviews were conducted between April 2022 and June 2023. The interviews took place online using Zoom videoconferencing software. Interviews lasted 35-70 minutes. When participants provided their written consent, interviews were audio-recorded and transcribed smart verbatim using otter.ai and manual proofreading. Sensitive information was removed before publishing transcripts.</p> <p>Transcripts were analysed using semi-grounded coding. Codes were organised into parent codes using an inductive approach based on emergent categories.</p> <h1>Description of the data and file structure</h1> <p>Documentation files include interview guides, the information sheet and consent form, ethics approval, and the data narrative. Documentation files are named according to the structure: authorname_filename_DOCUMENTATION.</p> <p>Data files consist of a summary of participants, 17 of the interview transcripts, and a code list. Interview transcript files are named according to the structure: authorname_interviewnumber_date.</p> <p>A full list of files is provided in the README file.</p> <h1>Notes</h1> <p>This study was conducted as part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS). More information can be found at <a href="https://opensciencestudies.eu/">https://opensciencestudies.eu</a></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001145).</p>
Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data
<p>This dataset contains valuable information on earthquake events, including their location, magnitude, depth, and focal mechanism solutions. This README file provides detailed explanations of each header in the dataset, as well as information about the files included in the repository.<br><br><em>"Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data"</em> <strong>(Under Review)</strong><br> </p>
Kanheri (Bombay, Maharashtra, India). Plan of Kanheri caves complex
<p>Kanheri (Bombay, Maharashtra, India). Plan of Kanheri caves complex, dated 1881 and published in Campbell, James M. <em>Gazetteer of the Bombay Presidency</em>. Bombay: Government Central Press, 1896.</p>
Survey of India Topo Sheet 54L4 1992 2nd edition, detail
<p>Survey of India Topo Sheet 54L4 1992 2nd edition, detail</p>
Piprahwa (Siddharthnagar district, Uttar Pradesh). Photographic rendering of reliquary inscription by India Museum, Calcutta.
<p>Piprahwa (Siddharthnagar district, Uttar Pradesh). Photographic rendering of reliquary inscription by India Museum, Calcutta.</p>
CLDF dataset derived from Marrison's "Naga Languages of North-East India" from 1967
<p>Cite the source of the dataset as:</p> <blockquote> <p>Marrison, Geoffrey Edward (1967) : The classification of the Naga languages of North-East India. London: School of African and Oriental Sciences.</p> </blockquote>
National Checklists 2017: India Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from India collected using effechecka and geonames polygons
National Checklists 2019: India Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from India collected using effechecka and geonames polygons
Morphological traits of selected rock outcrop amphibians in the lateritic plateaus of the northern Western Ghats, India
<p>This project contains morphological trait data compiled for a study investigating the responses of rock outcrop amphibians to land-use change in the lateritic plateaus of the northern Western Ghats, at the community-level and at species-level.</p> <div> <div> <p>Species Coverage: <em>Euphlyctis jaladhara, Hoplobatrachus tigerinus, Minervarya cepfi, Minervarya gomantaki, Minervarya sahyadris, Sphaerotheca dobsonii, Microhyla nilphamariensis, Hydrophylax bahuvistara, Polypedates maculatus.</em></p> <p>Data was compiled by V. Jithin from literature, and Saunak Pal from Natural History Collections at the Bombay Natural History Society (2023).</p> </div> </div>
Monthly, Seasonal and Yearly Net Primary Productivity (NPP) data of India from 2003-2020 modelled using the CASA model
<p>We estimated monthly Net Primary Productivity (NPP) at 1km spatial resolution for India using the Carnegie-Ames-Stanford Approach (CASA) Model. The CASA is a light use efficiency (LUE) based model that simulates NPP driven by remote sensing and meteorological data inputs. NPP is calculated as a product of the light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR). The seasonal and annual data are prepared by aggregating the monthly data. The India Meteorological Department (IMD) recognizes four seasons in India based on climate conditions. The four seasons are defined as Winter (January-February), Pre-monsoon (March-May), Monsoon (June-September), and Post-monsoon (October-December). The seasonal data is prepared according to the above classification of the seasons. </p>
Mammal occurrence records (2024) in the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India
<p>This dataset contains Mammal occurrence records (November 2023 - October 2024) in the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. It includes a few occurrence records from other parts of southern India. Occurrence records were gathered in the field by researchers of the Nature Conservation Foundation, India, using a mobile data collection application (EpiCollect5). Suggested citation is:<br>Nature Conservation Foundation (2024). Mammal occurrence records (2024) in the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. Nature Conservation Foundation, India. Dataset, Zenodo. DOI: 10.5281/zenodo.13910696<br> <br><strong>CONTACT #1</strong><br>1. Name: T. R. Shankar Raman <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org <br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>CONTACT #2</strong><br>1. Name: Divya Mudappa <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org <br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p><strong>Keywords: </strong>tropical rainforest, plantations, Anamalai Hills, Western Ghats, animal distribution, mammals </p> <p><br><strong>Geographic Coverage:</strong><br>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br>1. Begins: 2023-11-01 (Year, Month, Day)<br>2. Ends: 2024-10-01 (Year, Month, Day)</p> <p>Besides the 00_readMe.txt file containing this information, the dataset includes 23 images (photographs) and two comma-delimited text (csv) files as explained below:<br><strong>1) 01_anamalai-mammals-2024.csv </strong>-- This file has the main mammal occurrence data with relevant and renamed columns derived from the original downloaded csv file from the EpiCollect5 application website.</p> <p><strong>2) 02_nameMatch.csv</strong> -- This file matches the vernacular name as originally recorded with the correct common name and scientific name</p> <p>+23 image files (with ".jpg" file extension)</p> <p><strong>FILES INCLUDED IN DATASET</strong></p> <p><strong>01_anamalai-mammals-2024.csv</strong><br>This file has the main mammal occurrence data with relevant and renamed columns derived from the original downloaded csv file from the EpiCollect5 application website.<br>ec5_uuid: Unique ID for each observation<br>created_at: Automatic time stamp of date and time when record was created on the mobile app<br>uploaded_at: Automatic time stamp of date and time when record was uploaded using the mobile app<br>recordedBy: Name of observer<br>title: Title assigned to each record (composite of date, species, and type of observation)<br>lat_gps: Latitude in decimal degrees N<br>long_gps: Longitude in decimal degrees E<br>accuracy_gps: Horizontal accuracy of GPS location in metres<br>UTM_Northing_gps: Latitude in UTM<br>UTM_Easting_gps: Longitude in UTM<br>UTM_Zone_gps: UTM Zone<br>eventDate: Date in ISO format (yyyy-mm-dd)<br>verbatimEventDate: Date in format originally recorded (dd/mm/yyyy)<br>eventTime: Time of observation<br>vernacularName: Species common name as initially recorded<br>individualCount: Number of individuals observed<br>occurrenceRemarks: type of observation<br>habitat: Habitat type<br>photo: Filename of photo if available (NA otherwise)<br>eventRemarks: Notes or remarks about the observation</p> <p><strong>02_nameMatch.csv</strong><br>This file matches the name as originally recorded with the correct common name and scientific name.<br>vernacularName: Common or English name as initially recorded <br>scientificName: Scientific name of the species</p> <p>+23 image files (.jpg extension)</p>
Multi-label Tweet Dataset for Textual Propaganda Detection related to anti-CAA protest in India (2019-2021)
<p>This is a collection of English Tweets about the Citizenship (Amendment) Bill protests that occurred in India in 2019-2021. The dataset contains tweet instances multiple labels for identified propaganda techniques. The data set consists of tweet ids, hashtags used, and corresponding propaganda techniques. Labels have been automatically generated using Weak Supervision. </p> <p>As of 2023, there are very limited textual propaganda detection dataset for Tweets. This dataset is released to facilitate future research as propaganda has become omnipresent in modern social media. </p> <p> </p> <p> </p>
Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India
<p>This dataset contains data from the following publication:</p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 94: 748-757.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a><br> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a></p> <p><em>Corrigendum:</em></p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 97: 612-613.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a></p> <p><strong>Description of dataset:</strong></p> <p>The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below.</p> <p>Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded.</p> <p><strong>AUTHOR #1</strong></p> <p>1. Name: Hari Sridhar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria<br> 4. Email address: harisridhar1982@gmail.com<br> 5. ORCID: https://orcid.org/0000-0003-3286-0120</p> <p><strong>AUTHOR #2</strong></p> <p>1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>AUTHOR #3</strong></p> <p>1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p><br> <strong>Keywords:</strong> tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills</p> <p><br> <strong>Geographic Coverage:</strong></p> <p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p> <p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><br> <strong>Temporal Coverage:</strong></p> <p>1. Begins: 2005-09-01 (Year, Month, Day)</p> <p>2. Ends: 2006-10-31 (Year, Month, Day)</p> <p> </p> <p><strong>Dataset files:</strong></p> <p>Besides the <strong>00_README.txt</strong> file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below:</p> <p><strong>01_Transect_locations.csv</strong> — contains transect location details and descriptions</p> <p><strong>02_Transects_and_opportunistic_surveys.csv</strong> — contains main dataset of observations on line transect and opportunistic surveys</p> <p><strong>03_Opportunistic_observations_locations.csv</strong> — contains location details of opportunistic surveys</p> <p><strong>04_Lion-tailed_macaque_counts.csv</strong> — contains counts of lion-tailed macaque (<em>Macaca silenus</em>) troops</p> <p><strong>05_allmammals_raw.xls</strong> — raw data NOT for use, for reference only in original Microsoft Excel format</p> <p> </p> <p><strong>Data variables and descriptions:</strong></p> <p><strong>01_Transect_locations.csv</strong><br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> TransectLength_m: Length of line transect in metres<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum<br> StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum<br> EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum<br> EndLon: transect ending longitude in decimal degrees (E), WGS84 datum<br> MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum<br> MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum<br> ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum<br> RouteDescription: description of transect route</p> <p><br> <strong>02_Transects_and_opportunistic_surveys.csv</strong><br> eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number<br> occurrenceID: unique ID assigned to each occurrence (detection) along line transect<br> Sno: serial number<br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> locality: name of transect<br> Transectno: unique number assigned to each transect survey or resurvey<br> Replicate: number indicating repeat survey of same transect<br> SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment<br> Date: date of transect survey or opportunistic observation<br> Weather: Weather at time of survey<br> Habitat: Habitat where observation was made<br> Time: time in 24 h HH:MM format<br> verbatimIdentification: Identification as originally entered<br> scientificName: Scientific name of species or taxon observed<br> vernacularName: Common English name of species or taxon observed<br> Perpdist: Perpendicular distance in metres<br> Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month)<br> individualCount: number of individuals counted, taken as minimum 1 if not noted in field<br> rawNumber: number as originally entered<br> DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track<br> verbatimDetection: raw entry corresponding to previous column<br> Height: height of observed animal above ground in metres<br> occurrenceRemarks: remarks on occurrence</p> <p><br> <strong>03_Opportunistic_observations_locations.csv</strong><br> locality: name of place or transect where opportunistic observation was made<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres)</p> <p><br> <strong>04_Lion-tailed_macaque_counts.csv</strong><br> Sno: Serial number of entry<br> Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted<br> Date: Date of observation<br> Time: Time of observation in 24h HH:MM format<br> Weather: Weather<br> Groupid: ID of Lion-tailed macaque troop, if known<br> Total: Total number of individuals recorded<br> AM: number of adult males<br> AF: number of adult females<br> A: number of adults (unsexed)<br> SA: number of sub-adults (unsexed)<br> SAM: number of sub-adult males<br> SAF: number of sub-adult females<br> JUV: number of juveniles<br> INF: number of infants<br> CARINF: number of infants carried by mother<br> UNID: number of unclassified<br> Remarks: other notes</p> <p> </p> <p><strong>05_allmammals_raw.xls</strong></p> <p>Raw data file in Microsoft Excel format -- for reference only (not advised for use)</p> <p><br> <strong>ADDITIONAL NOTES</strong><br> General notes taken about survey:<br> Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor.<br> 36TH hpb transect - very poor visibility on one side as it is very steep<br> Giant squirrels present within LTM troops might go undetected. Need to look carefully and check every movement<br> KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres<br> SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants<br> BAN - Ignore detections after 2.05 KM for first two replicates<br> KSPV 26/01/2006 - 1 GS not detected on transect detected while returning at < 40 m<br> Anaigundi - Transect in december strayed slightly from correct path<br> Var 30/01/2006 transect Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case<br> should I consider only january and afterwards for MGH numbers since vocal activity is much higher then?<br> TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting<br> Do NL individuals move solitarily; what average group size to use<br> visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA<br> rained on 1st & 2nd of March after a long dry spell<br> KSPV 31/03/06 - Could have missed some calls because of cicada noise<br> KSWT 01/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Great hornbills seem to be more vocal during april. To do with end of nesting??<br> malabar grey hornbills more vocal from february onwards</p>
CLDF dataset derived from Grierson's "Linguistic Survey of India" from 1928
<p>Cite the source of the dataset as:</p> <blockquote> <p>Grierson, George Abraham (1928): Linguistic Survey of India. Comparative Vocabulary. Calcutta: Government of India Central Publication Branch.</p> </blockquote>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.