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Abstract: Cinema Belgica (CB) is an online platform that integrates fourteen existing datasets covering Belgian film production, distribution, exhibition, censorship and reception. In order to facilitate (inter)national data exchange and comparative research into cinema history, the platform (a) integrates key datasets related to Belgian cinema history, (b) makes them accessible according to FAIR principles (Wilkinson et al., 2016), and (c) enriches them with heritage collections. CB is Belgium’s contribution to the growing network of historical cinema databases, such as Cinema Context and The German Early Cinema Database (Dibbets, 2010; Garncarz, 2014). CB's data model is a modified version of the Cinema Context data model (van Oort & Noordegraaf, 2020). It is built around eight main conceptual entities: programme, film, censorship, venue, person, company, archive and publication (cfr. tree view). We refer to our accompanying research paper for more information regarding the logical organisation of the CSV files. The data deposited here can also be accesses via the public website of Cinema Belgica.

A benchmark dataset is always required for any classification or recognition system. To the best of our knowledge, no benchmark dataset exists for handwritten character recognition of Manipuri Meetei-Mayek script in public domain so far. Manipuri, also referred to as Meeteilon or sometimes Meiteilon, is a Sino-Tibetan language and also one of the Eight Scheduled languages of Indian Constitution. It is the official language and lingua franca of the southeastern Himalayan state of Manipur, in northeastern India. This language is also used by a significant number of people as their communicating language over the north-east India, and some parts of Bangladesh and Myanmar. It is the most widely spoken language in Northeast India after Bengali and Assamese languages. In this work, we introduce a handwritten Manipuri Meetei-Mayek character dataset which consists of more than 5000 data samples which were collected from a diverse population group that belongs to different age groups (from 4 years to 60 years), genders, educational backgrounds, occupations, communities from three different districts of Manipur, India (Imphal East District, Thoubal District and Kangpokpi District) during March and April 2019. Each individual was asked to write down all the Manipuri characters on one A4-size paper. The recorded responses are scanned with the help of a scanner and then each character is manually segmented from the scanned images. The whole dataset consists of five categories: 1. Mapi Mayek 2. Lonsum Mayek 3. Cheitap Mayek 4. Cheising Mayek 5. Khutam Mayek. This dataset consists of segmented scanned images of handwritten Manipuri Meetei-Mayek characters of size 128X128 pixels in .JPG format as well as in .MAT format.

Problem Statement: the task facing biomedical scientists hoping to find publications that corroborate or debunk a hypothesis is akin to finding a needle in a haystack that keeps growing. Strategies that mine or summarize the scientific literature exist but have been largely focused on recovery of named entities (e.g. proteins, cells) or more sophisticated methods that make use of ontologies to recover also related terms and even, more recently, machine learning methods when there is sufficient training data. Our Approach: we describe a use case faced by a biomedical scientist who needs to compare tumor volume/weight results in papers describing mice experiments where mice were exposed to the same or similar compounds but housed in different temperatures. In our approach, we have extracted annotations of units and measures (U&M) in scientific literature, which we then used in combination with contextual information (e.g. section of the paper) and regular expressions to identify the specific entity being measured (e.g. Housing Temperature). Results and Discussion: from a corpus of ~1.1M open access publications we found 299 relevant papers using the U&M approach combined with its surrounding contextual information. This large drop in the number of papers can be explained by our restrictive search criteria which included looking for keywords, patterns and temperature annotations in specific sections of the paper. We found a clear prevalence of papers mentioning housing conditions in the range of 20-25°C, which is the approximate temperature range suggested by NIH guidelines. We also found a small increase in the number of paper describing mouse thermo-neutral housing conditions in the period after the observation that this variable has an impact in mice tumor growth (2014-2016). This dataset contains those results.

This data on doctor-patient interactions and malaria treatment was collected for the project "Incentives for Accurate Diagnosis: Improving Health Care Quality in Mali" funded by DfiD/ESRC Development Frontier Award ES/N00583X/1.

Datasets: DS0: Study-Level Files DS1: House (1st Congress) DS2: Senate (1st Congress) DS3: House (2nd Congress) DS4: Senate (2nd Congress) DS5: House (3rd Congress) DS6: Senate (3rd Congress) DS7: House (4th Congress) DS8: Senate (4th Congress) DS9: House (5th Congress) DS10: Senate (5th Congress) DS11: House (6th Congress) DS12: Senate (6th Congress) DS13: House (7th Congress) DS14: Senate (7th Congress) DS15: House (8th Congress) DS16: Senate (8th Congress) DS17: House (9th Congress) DS18: Senate (9th Congress) DS19: House (10th Congress) DS20: Senate (10th Congress) DS21: House (11th Congress) DS22: Senate (11th Congress) DS23: House (12th Congress) DS24: Senate (12th Congress) DS25: House (13th Congress) DS26: Senate (13th Congress) DS27: House (14th Congress) DS28: Senate (14th Congress) DS29: House (15th Congress) DS30: Senate (15th Congress) DS31: House (16th Congress) DS32: Senate (16th Congress) DS33: House (17th Congress) DS34: Senate (17th Congress) DS35: House (18th Congress) DS36: Senate (18th Congress) DS37: House (19th Congress) DS38: Senate (19th Congress) DS39: House (20th Congress) DS40: Senate (20th Congress) DS41: House (21st Congress) DS42: Senate (21st Congress) DS43: House (22nd Congress) DS44: Senate (22nd Congress) DS45: House (23rd Congress) DS46: Senate (23rd Congress) DS47: House (24th Congress) DS48: Senate (24th Congress) DS49: House (25th Congress) DS50: Senate (25th Congress) DS51: House (26th Congress) DS52: Senate (26th Congress) DS53: House (27th Congress) DS54: Senate (27th Congress) DS55: House (28th Congress) DS56: Senate (28th Congress) DS57: House (29th Congress) DS58: Senate (29th Congress) DS59: House (30th Congress) DS60: Senate (30th Congress) DS61: House (31st Congress) DS62: Senate (31st Congress) DS63: House (32nd Congress) DS64: Senate (32nd Congress) DS65: House (33rd Congress) DS66: Senate (33rd Congress) DS67: House (34th Congress) DS68: Senate (34th Congress) DS69: House (35th Congress) DS70: Senate (35th Congress) DS71: House (36th Congress) DS72: Senate (36th Congress) DS73: House (37th Congress) DS74: Senate (37th Congress) DS75: House (38th Congress) DS76: Senate (38th Congress) DS77: House (39th Congress) DS78: Senate (39th Congress) DS79: House (40th Congress) DS80: Senate (40th Congress) DS81: House (41st Congress) DS82: Senate (41st Congress) DS83: House (42nd Congress) DS84: Senate (42nd Congress) DS85: House (43rd Congress) DS86: Senate (43rd Congress) DS87: House (44th Congress) DS88: Senate (44th Congress) DS89: House (45th Congress) DS90: Senate (45th Congress) DS91: House (46th Congress) DS92: Senate (46th Congress) DS93: House (47th Congress) DS94: Senate (47th Congress) DS95: House (48th Congress) DS96: Senate (48th Congress) DS97: House (49th Congress) DS98: Senate (49th Congress) DS99: House (50th Congress) DS100: Senate (50th Congress) DS101: House (51st Congress) DS102: Senate (51st Congress) DS103: House (52nd Congress) DS104: Senate (52nd Congress) DS105: House (53rd Congress) DS106: Senate (53rd Congress) DS107: House (54th Congress) DS108: Senate (54th Congress) DS109: House (55th Congress) DS110: Senate (55th Congress) DS111: House (56th Congress) DS112: Senate (56th Congress) DS113: House (57th Congress) DS114: Senate (57th Congress) DS115: House (58th Congress) DS116: Senate (58th Congress) DS117: House (59th Congress) DS118: Senate (59th Congress) DS119: House (60th Congress) DS120: Senate (60th Congress) DS121: House (61st Congress) DS122: Senate (61st Congress) DS123: House (62nd Congress) DS124: Senate (62nd Congress) DS125: House (63rd Congress) DS126: Senate (63rd Congress) DS127: House (64th Congress) DS128: Senate (64th Congress) DS129: House (65th Congress) DS130: Senate (65th Congress) DS131: House (66th Congress) DS132: Senate (66th Congress) DS133: House (67th Congress) DS134: Senate (67th Congress) DS135: House (68th Congress) DS136: Senate (68th Congress) DS137: House (69th Congress) DS138: Senate (69th Congress) DS139: House (70th Congress) DS140: Senate (70th Congress) DS141: House (71st Congress) DS142: Senate (71st Congress) DS143: House (72nd Congress) DS144: Senate (72nd Congress) DS145: House (73rd Congress) DS146: Senate (73rd Congress) DS147: House (74th Congress) DS148: Senate (74th Congress) DS149: House (75th Congress) DS150: Senate (75th Congress) DS151: House (76th Congress) DS152: Senate (76th Congress) DS153: House (77th Congress) DS154: Senate (77th Congress) DS155: House (78th Congress) DS156: Senate (78th Congress) DS157: House (79th Congress) DS158: Senate (79th Congress) DS159: House (80th Congress) DS160: Senate (80th Congress) DS161: House (81st Congress) DS162: Senate (81st Congress) DS163: House (82nd Congress) DS164: Senate (82nd Congress) DS165: House (83rd Congress) DS166: Senate (83rd Congress) DS167: House (84th Congress) DS168: Senate (84th Congress) DS169: House (85th Congress) DS170: Senate (85th Congress) DS171: House (86th Congress) DS172: Senate (86th Congress) DS173: House (87th Congress) DS174: Senate (87th Congress) DS175: House (88th Congress) DS176: Senate (88th Congress) DS177: House (89th Congress) DS178: Senate (89th Congress) DS179: House (90th Congress) DS180: Senate (90th Congress) DS181: House (91st Congress) DS182: Senate (91st Congress) DS183: House (92nd Congress) DS184: Senate (92nd Congress) DS185: House (93rd Congress) DS186: Senate (93rd Congress) DS187: House (94th Congress) DS188: Senate (94th Congress) DS189: House (95th Congress) DS190: Senate (95th Congress) DS191: House (96th Congress) DS192: Senate (96th Congress) DS193: House (97th Congress) DS194: Senate (97th Congress) DS195: House (98th Congress) DS196: Senate (98th Congress) DS197: House (99th Congress) DS198: Senate (99th Congress) DS199: House (100th Congress) DS200: Senate (100th Congress) Roll call voting records for the United States House of Representatives and Senate through the 100th Congress are presented in this data collection. Each data file in the collection contains information for one chamber of a single Congress. The units of analysis are the individual members of the House of Representatives and Senate. Each record contains a member's voting action on every roll call vote taken during that Congress, along with variables that identify the member (e.g., name, party, state, and uniform ICPSR member number). In addition, the codebook provides descriptive information for each roll call, including the date of the vote, outcome in terms of yeas and nays, name of initiator, the relevant bill or resolution number, and a synopsis of the issue. This collection is derived from UNITED STATES CONGRESSIONAL ROLL CALL VOTING RECORDS, 1789-1990 (ICPSR 0004), and differs from that collection in several ways. Codebooks have been standardized in format across all Congresses, and a number of discrepancies in members' identifying information (member identification number, party, etc.) have been corrected. United States Congressional Roll Call Voting Records Series All roll call votes in the United States Congress.

This data collection contains tables from the 1980 Census of Population and Housing, which were tabulated for Standard Metropolitan Statistical Areas (SMSAs), tracted portions of states outside SMSAs, and the following SMSA components: counties, places with 10,000 or more inhabitants, and census tracts. The tables primarily contain sample data inflated to represent the total population, plus 100-percent counts and unweighted sample counts of persons and housing units. Tabulated population items include household relationship, sex, race, age, marital status, Spanish origin, education, nativity, citizenship, language spoken at home, ancestry, children, place of residence in 1975, veteran status, work disability status, labor force status, travel time to work, means of transportation to work, industry, occupation, class of worker, income, and poverty status. Tables of housing variables cover number of units at address, presence of complete plumbing facilities, number of rooms, tenure (whether owned or rented), vacancy status, housing unit value, contract rent, units in structure, stories in structure and presence of a passenger elevator, year structure was built, year householder moved into unit, acreage, source of water, sewage disposal, heating equipment, house heating fuel, water heating fuel, cooking fuel, kitchen facilities, number of bedrooms, number of bathrooms, telephone in housing unit, air conditioning, number of automobiles, vans, and light trucks, and selected monthly owner costs (real estate taxes, property insurance, utilities, and mortgage payments). Two series of population and housing tables, A and B, are shown for each geographic unit. The A tables are tabulated once for the total population, while the B tables are repeated for the total population and up to six different race and Spanish origin groups: (1) white, (2) Black, (3) American Indian, Eskimo and Aleut, (4) Asian and Pacific Islander, (5) other race, and (6) Spanish origin. The data for each state are contained in a separate file. Altogether, 48 states and the District of Columbia are represented in the collection. Datasets: DS0: Study-Level Files DS1: Alabama DS2: Alaska DS3: Arizona DS4: Arkansas DS5: California DS6: Colorado DS7: Connecticut DS8: Delaware DS9: District of Columbia DS10: Florida DS11: Georgia DS14: Illinois DS15: Indiana DS16: Iowa DS17: Kansas DS18: Kentucky DS19: Louisiana DS20: Maine DS21: Maryland DS22: Massachusetts DS23: Michigan DS24: Minnesota DS25: Mississippi DS26: Missouri DS27: Montana DS28: Nebraska DS29: Nevada DS30: New Hampshire DS31: New Jersey DS32: New Mexico DS33: New York DS34: North Carolina DS35: North Dakota DS36: Ohio DS37: Oklahoma DS38: Oregon DS39: Pennsylvania DS40: Rhode Island DS41: South Carolina DS42: South Dakota DS43: Tennessee DS44: Texas DS45: Utah DS46: Vermont DS47: Virginia DS48: Washington DS49: West Virginia DS50: Wisconsin DS51: Wyoming DS80: Codebook, Volume 1: User Notes to Table Outlines DS81: Codebook, Volume 2: Data Dictionary and Product Review DS82: Codebook, Volume 3: Appendices There are two components to the Census: a 100-percent enumeration and a sample of approximately 19 percent of housing units. Basic demographic information, such as age, sex, and race, was collected from all persons and housing units. More detailed information was collected from the sample of housing units. Census of Population and Housing, 1980 [United States] Series All persons and housing units in the United States.